Data Science and Analytics - Finance, Tech & Analytics Career Resources

Excel Vs Python. What do you Choose?

Posted on July 24, 2025July 24, 2025 by Imarticus Learning

Starting data science, the first choice of tools that most will need to make is the use of Excel or Python. Excel is just a crass spreadsheet tool for quick-and-dirty analysis and Python needs to support scalability, automation, and big-shot analytics. Here in this article, we pit the two environments against each other, compare their pros and cons, and provide you with a feel for when to use which—and more importantly, perhaps, how a good data science course will teach you how to master both tools effectively.

Future data professionals should take Excel vs Python seriously. And why not?

Excel is everywhere—every organization uses it
Python powers automation and deeper insights
Both needed, for different reasons

Having the right data analysis tools at the right time makes one’s work more efficient and performance-oriented. Mastering the art of excel functions for data science is a must.

Excel for Data Analysis

Benefits of Using Excel

Excel data science tools provide:

Easy and friendly interface & fast feedback
Visual tools: filters, pivot tables, charts
Pre-programmed functions: VLOOKUP, SUMIFS, IFERROR
Beginner- and non-programmer-friendly

Best utilised for fast ad-hoc analysis, easy reporting, and finance.

Excel limitations
But Excel data analysis is not limitation-free:
Manually done, error-ridden task

Limitations of Excel

However, Excel for data analysis has limitations:

Error-prone manual operations
Restricted to data sets of ~1 million rows
Compute-bound on big scale
Weakly mechanized

Smarter forms of data wears out in Excel too soon.

Python for Data Science

Power of Python in Analytics

Python for data science excels with:

Python libraries for data analysis such as Pandas and NumPy for python data manipulation
Reproducible workflows and pipelines
More analytics with: Scikit-learn, TensorFlow
Smooth API, SQL, and big data engine support

That’s where scalable, production-grade analytics start.

Learning Curve

But in order to become a Python master, one needs:

Hand-coding reproducible flows
Learning scripts and data structures
Writing code and debugging
Getting over initial setup frustrations

One-off effort vs long-term gain.

Excel vs Python: Table Overview

Feature	Excel	Python
Ease of Use	★★☆☆☆	★★★☆☆ (for coders)
Speed (small data)	Fast	Fast
Speed (large data)	Slow and limited	High with Pandas/NumPy
Automation	Manual or VBA	Automated via scripts
Visualisation	Built-in charts	Matplotlib, Seaborn
Advanced Analytics	Limited	Extensive (ML, NLP, etc.)
Error Handling	Manual correction	Try/catch; reproducible code
Integration	Excel desktop only	APIs, databases, file systems
Scaling	Not suitable for large or repetitive jobs	Ideal for robust workflows

When to Use Excel

Ideal Scenarios for Excel

Excel is monarch when:

There needs to be fast analysis or discovery of data
Business users require spreadsheet output
Projects have short lives (<10K rows)
Tabulation and visualization are of utmost concern

Excel is ideal for finance, dashboarding, and accounting career paths.

When to Use Python

Python Shines When

Python is the language of choice when:

Large datasets (million of rows) need to process
Automating routine tasks (reporting)
Creating predictive or machine learning models
Combining data from different platforms (APIs, databases)
Constructing reproducible data analytics pipelines

Businesses that enable analytics at scale have Python as their go-to engine.

Can You Master Both?

A few experts leverage the following tool stack:

Quick Excel work
Python automation scripts
Python exporting data to Excel (openpyxl, xlwings libraries)
Single-application usage of Python back-end with Excel front-end
Deploying reusable workflows across teams

This hybrid method has maximum efficiency and effectiveness.

Learning Excel vs Learning Python for Data Analysis: Course Comparison

What a Data Science Course Should Include

Look for courses that offer:

Organized Excel modules:
- Advanced functions (INDEX-MATCH, pivot tables)
- VBA/macros
- Data cleaning and validation

Comprehensive Python coverage:
- Pandas, NumPy, Scikit-learn
- Data visualization (Matplotlib, Seaborn)
- Projects: automation, ETL, ML prototypes

Hybrid training with real-world examples
Career guidance and placement support

Imarticus Postgraduate Program Highlights

Imarticus Learning Postgraduate Programme in Data Science & Analytics covers:

100% Job Guarantee; 10 sure-shot interviews, over 2000 hiring partners
6 month course duration, weekday classroom + live online mode
25+ practice sessions in tools such as Python & SQL
22.5 LPA highest salary, 52% overall hike
Career counseling: resume development, mentoring, practice interview
National-level competition for hackathons

This end-to-end training guarantees expertise over Excel and Python.

Excel vs Python: Cost Considerations

Excel Training Costs

Self-study video: ₹1,000–₹15,000
Professional Excel certifications: ₹20,000–₹50,000

Python Training Costs

Beginner training: ₹2,000–₹25,000
Full-fledged data science courses: ₹50,000–₹3,00,000

Imarticus program is high return on investment mid-level due to guaranteed placement and tool proficiency.

Career Paths: Excel vs Python Expertise

Roles Emphasizing Excel

Finance Analyst
Audit Associate
Reporting Specialist
Operations Analyst

Excel skill is a must for financial reporting, operations management.

Roles Requiring Python

Data Scientist
Data Engineer
Machine Learning Engineer
Analytics Consultant

Python, data science skill are must in high-tech growth jobs.

Techniques: Excel vs Python Comparison Table

Task	Excel	Python
Data Cleaning	Manual, filters, VBA	Pandas (dropna, fillna, merge)
Aggregation & Summaries	Pivot tables, SUMIF	groupby, agg – fast & scripted
Visualisation	Charts	Matplotlib, Seaborn – programmable visuals
Repetitive Reporting	Manual refresh, copy/paste	Automated with scripts
Modeling (ML prototyping)	Limited (Add-ins)	Scikit-learn, TensorFlow for full ML lifecycle
Collaboration	Single-user file sharing	Jupyter Notebooks, Git, APIs, cloud

How to Start Learning Both

Beginners

Start with Excel: pivot tables, formulas, simple charts
Learn Python fundamentals: dicts, lists, loops
Then learn Pandas and data manipulation

Intermediate

Set up automated workflows: data import, cleaning, output to Excel
Mess around with simple ML work in Python

Advanced

End-to-end data analytics projects: pipeline from CSV → analysis → presentation
Skills are equally applicable for production projects

Imarticus course provides framework, guidance, and actual projects for implementing this development at the workplace.

FAQs

1. Which is better, Excel or Python?

Based on: Excel: best suited for small-sized quick analysis, business, or finance assignments

Python: best suited for big data, automation, and machine learning

Both need to be mastered in order to have professional development.

2. Can Excel perform large-scale data analysis?

Not necessarily—Excel is row-bound, time-consuming, and error-prone. Python is better suited for scalable, automated solutions.

3. Do employers expect proficiency in both?

Yes. Business context is provided by companies using Excel and Python for sophisticated data science work.

4. How long does it take to learn Python after mastering Excel?

You can learn 2–3 months worth of concepts and become pro and data science master in 6 months by virtue of trained practice and practice.

5. What are the costs to learn both?

Self-study: ₹10–50K

Full data science course (Excel & Python covered): ₹2–3.5L, with average placement assured

6. Why include Excel in a data science program?

Excel is needed for business analytics, finance, and junior positions—practically all jobs open up with Excel skills.

7. What tools are taught in the Data Science & Analytics program?

Imarticus trains one in Python, SQL, PowerBI, Tableau, and Excel, a complete analytics stack.

Conclusion

The battle between Excel and Python isn’t inferior or superior—it’s when and why to use one instead of the other. Excel is great for rapid, mechanical business analysis, and Python starts programmatic and scaleable data-driven conversion. They are both necessary to be prepared for one’s professional life in an industry.

If you want to become a data science expert, systematic training, live projects, and placement guarantee facilities—facility offered by Imarticus Learning—can facilitate you to achieve Excel with Python, accelerated growth, improved pay cheques, and repeat success.

The Best Weekend Data Science Course for Working Professionals in India

Posted on July 23, 2025July 23, 2025 by Imarticus Learning

In today’s data-fuelled economy, the most valuable skill isn’t just technical knowledge—it’s adaptability. And for working professionals, the real challenge is not learning data science, but finding the time for it.

A well-designed weekend data science course bridges this gap by offering a high-impact learning experience without disrupting your 9 to 5 job. If you’re based in India and looking to break into or advance in the world of data, this article is your complete guide.

Why Choose a Weekend Data Science Course?

The need for data-driven decision-making is no longer limited to tech firms. Companies across BFSI, healthcare, logistics, manufacturing, and retail are hiring data professionals in droves.

But most professionals can’t just take a career break.

That’s why weekend data science classes are becoming the go-to for engineers, analysts, marketers, consultants, and mid-career managers.

Here’s why:

They’re built for your schedule. No weekday conflicts or leave approvals.
You can apply skills instantly. Learn today, implement tomorrow.
Upskilling becomes sustainable. No burnout or work-life imbalance.

In fact, according to LinkedIn’s Future of Work report, data science and AI roles are among the most in-demand jobs in India, and they show no signs of slowing down.

What Working Professionals Need in a Weekend Course

Criteria	Why It Matters
Flexible weekend-only schedule	Helps you study consistently alongside work
Hands-on, live instruction	Enables real-time feedback and collaboration
Industry-recognised certification	Adds credibility to your career transition
Career support with real hiring links	Bridges the gap between education and employment
Project-based learning	Builds practical proof of skills
Strong alumni and mentor network	Offers industry insights and long-term support

Common Myths About Part-Time Data Science Programs

1. “Weekend classes don’t cover enough.”
Truth: A good part-time data science programme is designed with working adults in mind, ensuring every session is packed with outcome-oriented learning.

2. “It’s hard to manage work and study.”
Truth: When structured properly, weekend programmes allow steady progress without burnout.

3. “You need a tech background to join.”
Truth: Many successful data science professionals come from business, marketing, and operations roles.

The Imarticus Learning Edge: India’s Best Weekend Data Science Course

The Imarticus Learning Executive Post Graduate Program in Data Science and AI by Imarticus Learning is crafted for professionals who want real transformation—not just a certificate.

Unlike generic MOOCs, it offers:

Weekend-only live sessions
Career mentorship
Industry-recognised dual certification from Imarticus + IBM
Access to 2000+ hiring partners
Global Capstone Project experience

🧠 Did you know? Learners report an average salary hike of 50% after completing the course.

🔗 Explore the Program Details

Why India Is Leading in AI—And Why It Matters

According to a BCG report, India’s AI adoption rate is 30%, higher than the global average of 26%.

This explosive growth is creating demand for:

Data scientists
Machine learning engineers
Data analysts
AI consultants

And it’s not limited to tech companies—finance, retail, manufacturing, and logistics firms are now hiring AI-literate professionals.

What You’ll Learn in the Course

The curriculum is one of the most practical in the industry:

Python and R for data wrangling
SQL for structured queries
Data handling using Pandas & NumPy
Machine Learning models from scratch
Scikit-learn, XGBoost and model tuning
Data visualisation using Tableau and Power BI
AI and NLP concepts
Cloud tools: AWS, GitHub

✅ Designed as a flexible data science course, it offers practical experience every weekend.

All About Data Science and Analytics Course by Imarticus Leaning | Data Science For Beginners

Real-World Projects You’ll Build

The course includes:

10+ mini-projects
1 global capstone project
3 case studies across BFSI, Healthcare, and Retail

🎯 Sample Projects:

Fraud detection in banking
Retail inventory forecasting
Marketing spend optimisation
Sentiment analysis of customer reviews

Career Support that Goes Beyond Advice

Imarticus Learning doesn’t just help you learn—it enables you to get hired.

You’ll receive:

1:1 career mentoring
Resume and LinkedIn profile building
Mock interviews with industry mentors
Access to 2000+ hiring partners, including Accenture, Genpact, TCS, and EY

This is what makes it one of the most career-focused data science training India offers today.

Weekend vs. Full-Time Data Science Programs

Feature	Weekend Course (Imarticus)	Full-Time Course
Schedule	Weekends only	5–6 days a week
Ideal for	Working professionals	Students / freshers
Live sessions	Yes	Yes
Career services	1:1 mentorship + job assurance	Often limited to placement drives
Flexibility	High	Low (fixed schedule)
Income during course	Maintained (work continues)	Often zero (if job paused)

Who Is This Program Ideal For?

👩‍💼 Working Professionals in Non-Tech Roles
Marketing managers, operations leads, HR analysts—learn data to future-proof your job.

🧑‍💻 Junior Tech Professionals
Developers, testers, support engineers—transition into high-paying data science roles.

🎓 Career Changers
Engineers and MBA grads who want to move from stagnant roles into the tech-driven future.

Frequently Asked Questions (FAQs)

1. Is this program suitable for absolute beginners?
Yes, it starts with foundational coding and statistics.

2. What is the course duration?
11 months, weekend-only sessions.

3. Is prior programming experience required?
Not at all. The course builds skills from the ground up.

4. Is it a part-time or full-time course?
It’s a part-time, weekend data science course.

5. What certification do I receive?
Dual certification from Imarticus and IBM.

6. What tools will I master?
Python, SQL, Tableau, Power BI, GitHub, AWS..

7. What if I miss a session?
You’ll get recordings and backup sessions.

8. Are there EMI or financial aid options?
Yes, easy EMI options are available.

9. Where can I apply?
👉 Apply here

Conclusion

In a world where industries run on data, professionals who can analyse, visualise, and predict with data are irreplaceable.

But upskilling shouldn’t force you to quit your job or pause your career. That’s where a weekend data science course makes all the difference.

Key Takeaways

Imarticus offers the best weekend data science course in India for professionals
It balances live learning, real projects, job assurance, and global credentials
With India leading in AI adoption, there’s no better time to start than now

Ready to Learn Without Quitting?

Click below to view the curriculum, download the brochure, or speak to an advisor:
👉 Join India’s Best Weekend Data Science Course

How to Ace Data Science Interviews

Posted on July 21, 2025July 23, 2025 by Imarticus Learning

Data science interviews are intimidating, particularly for an individual who is new to analytics. Case studies to algorithms and expressing your motivation for your thought process to the interviewer—these interviews try to assess not just technical skills, but business sense and communication skills too. In this blog, we will take you through cracking data science interviews with emphasis on popular data science interview questions, senior data scientist interview strategy, what to expect in technical rounds and behavioral rounds.

This blog is part of the career-readiness journey undertaken by Imarticus Learning’s Postgraduate Program in Data Science and Analytics, a 100% job-guarantee postgraduate program for early careerists.

Why Are Data Science Interviews So Intense?

Data science is an interdisciplinary field. Employers expect candidates to know statistics, programming, machine learning, and domain knowledge. Such sophistication often leads to multi-round interviews that encompass:

Technical acumen
Coding exams
Case study presentations
Business problem-solving
Soft skill evaluation

What Do Interviewers Look For?

When preparing for data science interviews, remember that recruiters evaluate three key areas:

Technical Competence – Your proficiency with Python, SQL, statistics, and machine learning.
Problem Solving Ability – How you solve problems logically with data.
Communication Ability – How deeply you clarify your thought process.

Each round is structured towards whether you are hireable as a data scientist or not.

Common Data Science Interview Questions

What are the most frequently asked data science interview questions?

Explain supervised and unsupervised learning differences.
How do you handle missing data? Describe
What is the bias-variance trade-off?
How is logistic regression different from linear regression?
What is overfitting and how can one avoid it?
How do you choose significant variables in a data set?
Describe the ROC curve and AUC.
What is bagging and boosting separately?
How does a random forest algorithm work?
What is PCA, and when would you apply it?

Knowing how to answer these best data science interview questions is the secret to success in early rounds.

Technical Questions for Data Science Interviews

How technical do data science interviews get?

Technical questions would be coding problems, algorithmic problems, and usage in the real world:

Write a Python function to perform linear regression.
Do you have a readily available dataset? Write SQL queries to retrieve top-grossing products by revenue.
Develop a classification model using scikit-learn and justify why you decided on that model.
How do you address multicollinearity in a regression model?
What are a few of the key metrics to monitor for a classification model?

Data science technical interview questions demand practical skills. Imarticus students practice on 10+ tools such as Python, SQL, Tableau, and Power BI in order to cross these rounds.

Behavioral Questions for Data Science Interviews

What are common behavioral questions in data science interviews?

So-called soft skills are just as crucial as technical skills. Those are some of the behavioral interview questions interviewers ask:

Tell me about a time when you have solved a tough problem with data.
Tell me about a project where you made an error and how you corrected it.
Did you ever work with cross-functional teams? And how was that?
How do you communicate complex outcomes to non-technical stakeholders?

These data science behavioral interview questions are meant to check your leadership, communication, and flexibility.

Data Science Interview Preparation Strategies

How should I prepare for data science interviews?

Data science interview preparation takes time and is methodical. Here’s what we know that works:

Master Fundamentals: Review probability, stats, and ML fundamentals.
Practice Coding: Practice Python/SQL problems on HackerRank or Leetcode. This.
Mock Interviews: Practice mock interview with friends or mentors for tech and behavioral rounds.
Build Projects: Highlight actual applications of your skills in a portfolio or GitHub.
Study Case Studies: The majority of interviews have business case studies that need to be solved.

Imarticus’ course provides 25+ projects, mentorship, and mock interviews to make sure that the students nailed their actual ones.

Real Interview Experience from Hiring Companies

What do top recruiters ask in data science interviews?

With more than 2000 hiring partners, Imarticus Learning has placed more than 15,000 students. Below is a sample interview timeline of a top recruiter:

Round 1: Technical Screening

Python basics and usage-use cases
SQL queries with joins, aggregations, and subqueries
Data cleaning techniques

Round 2: Case Study

Build a model to predict customer churn
Present results and model performance to a hiring manager

Round 3: Behavioral Interview

Stress work experience
Explaining data concepts to clients

Structured interview guide for data scientists like this one assist you in preparing yourself for the subsequent step.

Resume and Profile Optimization

Does my resume matter in data science interviews?

Yes! Your resume is your initial impression. Ensure that it highlights:

Tools and programming languages familiar to you
Projects with measurable outcomes
Certificates like the Imarticus Data Analytics Program
Attendance at data science competition or hackathon is of any importance

We help students craft data science interview questions and data science careers interview-winning data science resumes.

Learn Data Analytics Online – The Imarticus Advantage

Imarticus Learning’s Postgraduate Programme in Data Science and Analytics is crafted to:

Develop employment-ready data analytics skills
Live training + live projects
Create high-growth data analytics career paths

No matter Python, SQL, machine learning, or communication skills—this course renders you a master of every data scientist interview guidance and expectation.

Data Science Career Outlook in 2025

Data scientists’ demand will increase by 35% in five years.

Average Entry-Level Salary: ₹6–8 LPA
Mid-level Roles: ₹12–18 LPA
High-Level Roles: ₹22.5 LPA (as observed at Imarticus)

The journey is profitable, yet only the well-trained rivals nailed data science interviews.

Frequently Asked Questions

Q1. How do I prepare for a data science interview?

Technical skills, mock interviews, and communications skills are the things to practice. Imarticus provides all this in one package.

Q2. What kind of questions are asked in data science interviews?

Practice Python, SQL, ML concepts, business case studies, and behavioral questions.

Q3. How important is coding in data science interviews?

Very significant. Most companies test Python and SQL skills at initial rounds.

Q4. Are mock interviews useful?

Yes. Mock interviews eliminate fear and increase confidence via practice.

Q5. What if I’m from a non-tech background?

You can still enter the data analytics field via focused upskilling. The Imarticus program is experiential and newbie-friendly.

Q6. What’s the typical data science interview process?

Initial screening → Technical round → Case study → Behavioral round → HR discussion.

Q7. Do I need a master’s degree?

Not exactly. Proper training and profession-related education such as Imarticus’ may be as useful as a master’s.

Q8. How many projects should I include in my resume?

At least 3–5 quality projects on various subject matters of data analytics.

Q9. What’s the role of Tableau/Power BI?

These are simple visualization tools, which are tested for on a regular basis when interviewing for dashboard development.

Q10. What if I fail the first few interviews?

Accept constructive feedback, learn and improve skills, and try again. The market is full of more than ample demand.

Conclusion

Acing data science interviews does not involve memorization. It involves getting the concepts right, having a strong portfolio and portraying self-confidence in terms of delivery. With proper preparation, resources and guidance, it is quite easy to get your dream job in data science.

Programs like the Postgraduate Program in Data Science and Analytics from Imarticus Learning offer end-to-end guidance—from learning tools to acing interviews. If you’re serious about building a future-proof career in data science, it’s time to upskill and prepare to ace your interviews.

Excel for Data Analytics: What You Should Know

Posted on July 21, 2025July 23, 2025 by Imarticus Learning

In a world where data informs all decisions, Excel remains one of the most reliable, ubiquitous, and adaptable tools for data analytics.

Though newer tools and programming languages have arrived on the scene, Excel is still the point of departure—and most often, the preferred option—for professionals across sectors, particularly in finance and banking.

This blog demystifies how to best utilise Excel for data analytics, the absolute functions, tools, and techniques that any analyst must be proficient in, particularly if you’re eyeing a career in financial services, product sales, or banking analytics.

No matter if you’re just starting out or in need of refining your skill set, this blog will teach you everything you should know about data analytics using Excel.

Why Excel Remains Integral to Analytics

Excel for Data Analytics Beginners

Despite the automation and coding software boom, Excel remains a timeless tool because it’s easy to use, has an intuitive design, and is widely used. More than 750 million people across the globe use Excel—evidence of its longevity. (Source)

Why do analysts still prefer Excel?

It has no coding skills required.
It works on big sets of structured data
It seamlessly integrates with other tools and platforms. It.
It’s applied every day in 90% of finance careers.

Particularly for those starting with Excel in data analytics, the program offers a seamless learning curve with high-quality features that establish a solid foundation for more sophisticated tools.

Why Excel is a Robust Analytics Tool

Major Features Contributing to Excel’s Success

Excel is not only a spreadsheet tool—it’s a powerhouse for organising, analysing, and interpreting data. It facilitates a variety of operations, including:

Data cleaning with text and logic functions
Data summarisation with PivotTables
Trend analysis through charts and visualisation tools
Scenario planning with Solver and Goal Seek
Automation with Macros and VBA

They all add up to a very efficient career path in finance, particularly in sales, trading, or operations.

Top Excel Data Analysis Tools You Should Master

To be most productive and accurate, here are the tools that every analyst should learn to master in Excel:

Tool/Feature	Use Case
PivotTables	Quick data summarisation, segmentation, and grouping
Power Query	Automate and scrub data from various sources
Power Pivot	Build data models and DAX formulas for in-depth analysis
Charts and Graphs	Graph trends, spot outliers, and display data succinctly
Conditional Formatting	Highlight data points graphically based on conditions
Data Validation	Manage inputs, minimise user mistakes
What-If Analysis Tools	Apply Goal Seek and Scenario Manager to forecasting
Solver Add-In	Optimise tough optimisation problems

Learning the Excel data analysis tools will assist you in getting insights quicker and more effectively—something that is very important in high-paced positions such as product sales within finance.

Functions Powering Data Analytics in Excel

Essential Excel Functions for Analytics

You don’t have to learn every function in Excel. However, some are notable due to their frequent application in analytics activities. They are:

VLOOKUP / XLOOKUP: Match and return values between datasets
INDEX-MATCH: A more flexible replacement for VLOOKUP
IF, IFS, AND, OR: Make logical comparisons
SUMIFS, COUNTIFS: Sum and count data with conditions
TEXT, LEFT, RIGHT, LEN: Clean and work with strings
DATE, YEARFRAC, EOMONTH: Work with time-sensitive financial information
ROUND, CEILING, FLOOR: For financial rounding logic

These aren’t just necessary for day-to-day analysis—they’re essential to jobs that include Excel for data analysis in a structured, sales-driven finance space.

A Unique Edge: Power Query + Power Pivot Integration

Most individuals interface with the surface of Excel. But if you’re looking to work in financial services sales or as a product specialist, you must dive deeper.

Power Query assists with cleaning, transforming, and merging data.
Power Pivot assists with modelling that data and building relationships between tables through DAX (Data Analysis Expressions).

Together, they make Excel a low-code business intelligence machine. That’s a form of analysis even simple SQL tools can’t always keep up with.

The Increased Use of Excel in Finance and Banking

Statista reports that the Indian finance and banking industry expanded by 9.6% in FY 2021–22, up from 5.6% the prior year. That is a sign of increased digital adoption, automation, and analytics.

In day-to-day language, that means:

Increased demand for financial product analysts who can produce real-time reports.
Increased demand for Excel-based risk models and profitability models.
Excel usage in regulatory filings, audit trails, and forecasting reports.
Pros looking to join this profession with expertise in advanced Excel skills for analysts possess an unquestionable advantage.

Real-World Applications of Excel in Finance Analytics

If you are considering a career in finance sales positions, Excel will be part of your daily repertoire.

Typical Use Cases

Constructing client product bids from prior performance
Building revenue dashboards by product or geography
Comparing competitor analysis based on input from several market reports
Analysing investment portfolio returns with standard deviation and CAGR
Preparing regulatory reporting sheets for internal and external purposes
These activities require precision and speed, and that’s where Excel excels.

Tips and Best Practices to Master Excel for Analytics

Practical Excel Tips for Data Analysts

Use Excel Tables rather than ranges when working with dynamic models
Never join up cells—use “Centre Across Selection” instead
Name your ranges and formulas for easy understanding
Use keyboard shortcuts to minimize mouse reliance
Split logic, input, and output into separate sheets
Write down your formula logic in comments or footnotes
Avoid volatile formulas such as OFFSET unless there’s no alternative
Make backup versions before constructing nested calculations
By mastering these habits, you’ll be far more efficient in roles that rely heavily on Excel data analysis tools.

Watch our Excel Masterclass Playlist, and become a pro in Excel.

Excel Basics Made Easy: Master the Essentials!

FAQs: Excel for Data Analytics

1. Is Excel now outdated for data analytics?

No, particularly in finance, sales, and junior analytics positions.

2. Can I become a data analyst using only Excel skills?

Yes, Excel by itself can get you entry-level positions, particularly in small and mid-sized companies.

3. In which industries is Excel most heavily used?

Banking, finance, marketing, education, and operations are most dependent on Excel.

4. Is Power Query present in every version of Excel?

It’s in Excel 2016 and up; strongly suggested for analysts.

5. Can Excel support large data?

With Power Pivot and Power Query, Excel can manage millions of rows.

6. What sets Excel apart from other software?

Its ease of access, self-explanatory UI, and departmental integration.

7. Is Excel superior to Google Sheets?

For advanced data models, Excel is stronger and more stable.

8. How can Excel assist with the sales of financial products?

You can quickly create pitch decks, compute IRR, and model product performance.

9. Do I need VBA to perform data analytics in Excel?

Not necessarily. Most applications are addressed by built-in tools and formulas.

10. How do I improve at Excel analytics?

Practice on real data. Attempt to redo the dashboards or model financial situations.

Conclusion: The Excel Advantage

Whether you’re creating dashboards for a manager, performing financial analysis, or researching market trends, Excel is probably the first application you’ll employ—and the last one you’ll abandon.

In a world of complicated platforms, Excel is unique in that it makes analytics easy to use and powerful simultaneously. With features such as PivotTables, Power Query, and complex formulas, it puts professionals at an advantage in careers ranging from financial services sales to trading analytics and beyond.

If you’re committed to starting or transforming your analytics career, don’t underestimate Excel—it’s not a spreadsheet; it’s a superpower.

Key Takeaways

Excel remains indispensable in analytics, particularly in finance, due to its depth and versatility.
Power Query and Power Pivot make big-data capabilities easily accessible in the familiar Excel environment.
Understanding the most important functions and tools provides a genuine competitive edge in finance sales roles and data-driven positions.

Start Your Analytics Career Today

Aspiring to create a successful financial analytics, sales, or banking operations career?

Learn the Postgraduate Program in Banking and Finance by Imarticus Learning.

How to Become Job-Ready with a Data Analytics Program

Posted on July 21, 2025July 23, 2025 by Imarticus Learning

In today’s data age, businesses are continuously searching for competent professionals who can analyze, interpret, and visualize data in an effort to make effective business decisions. Pursuing a comprehensive data analytics course is an intelligent decision for an individual who wishes to become a part of this vibrant field. If you are a college graduate, or a working professional who is interested in switching careers to a data career, the right course will provide you with the training and hands-on experience that you need to get hired. In this blog, we talk about the advantages, skill set one needs, and career prospects for pursuing a data analytics course and how it leads to a successful career in analytics.

Why Choose a Data Analytics Program?

A data analytics course program provides much more than classroom instruction. It provides experiential learning with hands-on training, live projects, mentoring, and career counseling. These programs aim at meeting the increasing need for data professionals in terms of job-readiness.

Data Analytics courses provide value in the form of learning the latest tools such as Python, SQL, Power BI, and Tableau.
Provides access to in-class or online real-time instruction.
Is implemented in real-world situations through over 25 projects.
100% job guarantee with interview guarantee.
Good resume development and interviewing skill help.

What Skills Do You Gain from a Data Analytics Program?

One of the major features of any data analytics course is its focus on work-based, functional skills. They are the skills that one needs to succeed in the current competitive employment market.

Technical Skills

Python & R: Learn programming in Python and R in order to clean, process, and visualize data.
SQL: For querying databases and processing structured data.
Excel: Starting point for basic data analysis and reporting.
Power BI & Tableau: Develop dashboards and visualizations to guide business decisions.

Analytical Thinking

Critical thinking and evidence-based problem-solving form the foundation of any data analytics profession. Such courses would normally encompass understanding the interpretation of big data sets, identifying patterns, and providing strategic recommendations.

Communication Skills

Transforming complex data results into simple-to-understand visualizations and actionable recommendations takes tremendous communication skills. Data storytelling well is the soul of a data analytics course.

Business Acumen

Having knowledge about the business environment is necessary. Most of the courses enable making sure that the students understand how data affects different parts of a business like marketing, operations, finance, and human resources.

The Structure of a Quality Data Analytics Program

An excellent data analytics course is not just course work and assignments. That is why it is comprehensive:

Live Learning Sessions

Live classes enable students to interact with instructors in real time. Imarticus’ Postgraduate Diploma in Data Science and Analytics also has online and offline facilities, which are ideal for every type of learning.

Real-World Projects

There are more than 25 industry-focused projects that replicate real-world scenarios that provide hands-on exposure to solve business issues of a data nature.

Tools Mastery

Master 10+ industry data tools. Hands-on experience using tools such as Python, SQL, Tableau, and Power BI makes you job-ready from day one.

Hackathons and Competitions

Coding hackathons enhance your problem-solving ability and give that extra oomph to your resume.

Dedicated Career Services

Resume building, mentorship, mock interviews, and job guarantee sessions train students to transition from the classroom to the boardroom.

Data Analytics Career Path in India

Data Analytics career is spreading at lightning speed as far as India is concerned because of the booming Digital data in business. Organizations from all industries like finance, health care, retailing, and technology require people to analyze amounts of data.

Some of the most in-demand skills for data analysts are:

Data visualization (ability)
Data cleaning and data transformation
Understanding statistical concepts
Machine learning background (senior positions)

Entry-Level Roles:

Basic Data Analyst
Basic Business Intelligence Analyst
Reporting Analyst

Mid-Level Roles:

Data Scientist
Machine Learning Engineer
Analytics Consultant

Senior Roles:

Data Architect
Director of Analytics
Chief Data Officer

Job-Ready Data Analytics Skills for 2025 and Beyond

As AI and Gen AI kick in, data analytics will more and more get entangled with newer automation. Therefore, the new course in data analytics must include:

Gen AI Integration: Learning about how AI tools assist in pattern detection and predictive analytics.
Cloud Platforms: Familiarity with AWS, Azure, or Google Cloud is the new norm.
APIs and Data Engineering Fundamentals: Ideal for processing data pipelines and integrations.

Data Analyst Training Program at Imarticus

Imarticus’ Postgraduate Program in Data Science and Analytics is India’s highest-rated job-guaranteed program:

Duration: 6 Months
Mode: Live Online + Classroom
Eligibility: Freshers and professionals in technical domains
Placement: 10 guaranteed interviews, 100% job guarantee
Projects: 25+ case studies
Tools Included: 10+ such as Python, SQL, Tableau, Power BI

Learn Data Analytics Online with Confidence

Online learning data analytics has ensured entry into this profession is now simpler than ever. Interactive websites and guide mentor experts make it possible for students to reach the same level as traditional classes with the comfort of convenience.

Benefits of Learning Online:

Learn at your own pace
Watch video lectures
Live interactions with experts
Interactions with classmates worldwide

CFA vs Data Analytics: Which is Better?

Data analytics and CFA are both in high demand, and most of the professionals get confused about whether to pursue CFA or data analytics. CFA is perfect for investment and finance careers, but data analytics gives exposure to a variety of industries such as retail, health, IT, etc.

Everything depends on:

Your career option: Investment or Analytics
Your background: Finance or Tech
Employment market demand in your field of interest

Top Recruiters Hiring Data Analytics Talent

Job-ready data analytics demand has attracted Indian and global top players. Imarticus has collaborated with 2000+ recruiters such as:

Deloitte
PwC
KPMG
Accenture
Capgemini
HDFC Bank
Cognizant
Infosys
EY

Average Salary and Career Growth in Data Analytics

Entry-Level: ₹6 LPA to ₹8 LPA
Mid-Level: ₹10 LPA to ₹15 LPA
Senior-Level: ₹20 LPA+
Highest Salary Achieved by Imarticus Graduate: ₹22.5 LPA
Average Hike: 52%

ROI on a well-planned data analytics program is high. The more digital disruption occurs in industries, the more skills-based data professionals are the need of the hour.

Is a Career in Data Analytics Right for You?

You would like to have a data analytics career if you:

Like playing with numbers and patterns
Possess a logical and analytical mind
Are ready to learn new tools and techniques on a daily basis
Like to be in a high-growth, high-demand profession

Conclusion

A course in data analytics is the door to a prosperous profession in the modern digital economy. It not only provides you with training for the most attractive jobs, but it also renders you hireable with experiential skill sets, live project tools, and industrial exposure. If you are looking to switch to a career in data analytics or wish to begin one, a well-designed training program like the Postgraduate Program in Data Science and Analytics by Imarticus Learning is the perfect starting point.

With the choice between 100% job guarantee, exposure to leading recruiters, one-to-one career counselling, and experiential training, this course has everything to get you going.

FAQs

1. What are the benefits of a data analytics program?

It gives you practical experience in data tools, project experience, mentorship, and job guarantee. All together, they make you industry-ready for high-growth careers.

2. What is the average salary after completing a data analytics course?

Salary in the first year is around ₹6-8 LPA, and experienced people earn over ₹20 LPA based on skills and designations.

3. Can I learn data analytics online?

Yes, online education provides adaptable, interactive environments with real-time classes, doubt clarification, and peer networking.

4. Is data analytics a good career in 2025?

Yes, demand for data professionals will increase manifold with growing digital transformation and AI.

5. What industries hire data analysts?

Finance, retail, health care, IT, manufacturing, and consultancy firms recruit skilled data analytics professionals on a regular basis.

6. What tools will I learn in a data analytics program?

Python, SQL, Tableau, Power BI, and Excel are typically full-course offerings.

7. What kind of projects are included in the course?

Real-world case studies of e-commerce, banking, health care, and logistics to allow you to apply what you learned.

8. Do I need a tech background to enrol?

Though preferred, the majority of courses also accept non-tech candidates who are willing to learn and put in the effort from scratch.

9. How long is the data analytics program by Imarticus?

The Postgraduate Program in Data Science and Analytics is a 6-month program that is fast-track and highly effective in terms of job readiness.

10. Will I get placement support after completing the program?

Yes, Imarticus offers 100% job guarantee with 10 guaranteed interviews, resume preparation, and mock interviews.

Data Scientist Qualifications: A Guide for Career Changers

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Table of Contents

Introduction: Is Data Science for You?
Qualifications Needed for Data Science
How to Become a Data Scientist
Data Science Career Transition: Tips for Professionals
Best Degrees for Data Science
Skills Required for Data Scientists
Certifications for Data Science Careers
Breaking into Data Science Without Experience
Key Takeaways
FAQs
Conclusion

What Is Data Science: Is It for You?

Have you ever felt intrigued by numbers, patterns, or predictions? Do you often find yourself wondering how Netflix knows what to recommend next or how Google reads your thoughts? If you do-and if you are contemplating a career change-you could fit the bill for data science to a T. In this guide we explain the qualifications you will need as a data scientist to break into this fast-offering field, especially if you are doing so from a mid-career switch.

Qualifications Required for Data Science

Most people mistakenly believe that one needs a Ph.D. to qualify as a data scientist. Certainly, that can help; however, it is not a requirement. What is most important is a combination of education, analytical thinking, and tool-based skills. This section will explain to you the baseline qualifications needed for data science, especially for non-technical people.

Here’s a quick comparison of qualifications typically expected for entry-level vs. mid-level roles:

Role Level	Qualification Requirements	Preferred Background
Entry-Level	Bachelor’s in STEM, basic programming skills	Math, CS, Economics
Mid-Level	Master’s or certifications, industry experience	Engineering, Business, Stats
Senior-Level	Advanced degrees or 5+ yrs experience	Specialized domain expertise

Key Points:

A degree is important, but hands-on skills often matter more.
Employers value problem-solving abilities and project experience.

How to Become a Data Scientist

So, how do you start the journey? There isn’t just one road to data science. This section outlines how to tailor your path based on where you’re starting.

Here’s a basic roadmap for how to become a data scientist:

Learn Python or R – These are the industry favourites.
Master SQL – Knowing how to manipulate databases is crucial.
Understand Statistics – Core concepts like probability, regression, and distributions are musts.
Build Portfolio Projects – Use real datasets to solve problems.
Network and Join Communities – LinkedIn groups, Kaggle competitions, and meetups matter.

Whether you’re coming from finance, marketing, or engineering.. data science skills can give you an edge.

Harvard Business Review called data science- the “sexiest job of the 21st century”. Why not make it yours?

Data Science Career Transition: Tips for Professionals

Changing careers isn’t easy—but it’s entirely possible. With the right strategy, even professionals with no tech background can break into data science. This section is tailored for those making a data science career transition.

Mid-career professionals often hesitate to pivot due to fear of starting over. But data science is surprisingly welcoming to diverse backgrounds. Your business acumen, domain expertise, and communication skills are valuable in this field. This guide will show you how to transition effectively.

Top Tips for Career Changers:

Bridge the gap with certification programs.
Highlight analytical work in your past roles.
Start freelancing or take internships.
Use LinkedIn to showcase learning progress.
Get a mentor or career coach.

Best Degrees for Data Science

While many data scientists have traditional STEM backgrounds, there’s increasing diversity in academic paths. Here’s what you should know about the best degrees for data science.

Not sure if your degree qualifies? Don’t worry. Employers are open to various academic backgrounds as long as you can demonstrate competency in data-related skills. However, some degrees are more aligned with the field’s demands.

Degree	Relevance to Data Science	Special Notes
B.Sc in Computer Science	High	Offers solid programming foundation
B.Tech in IT	High	Includes software and systems focus
B.Sc in Math/Statistics	Medium to High	Strong in analytical foundations
MBA (with analytics)	Medium	Good for business-data intersection

Skills Required for Data Scientists

Data scientist qualifications go beyond degrees and certificates. Skills—both technical and soft—are what really matter in real-world jobs. Let’s explore the skills required for data scientists today.

Employers look for more than just theory. You need hands-on, job-ready skills. A good mix of coding, data wrangling, statistical modelling, and communication is crucial. This section breaks down the skill sets most valued in India’s data science job market.

Must-Have Skills:

Programming: Python, R, SQL
Data Handling: Pandas, NumPy, Excel
Visualization: Tableau, Power BI, Matplotlib
Machine Learning: Scikit-learn, TensorFlow, basic algorithms
Communication: Ability to present findings to non-tech stakeholders

Certifications for Data Science Careers

If you’re hoping to get into the field fast, certifications can help address the experience disadvantage. Here’s list of certifications for a career in data science that employers recognise.

Not everyone is able to do a full-time education. This is where certifications can help out because they are quicker, and more narrowly focused on the current industry needs. Picking the right one can improve your chances of getting the job you want.

Top Certifications:

Google Data Analytics Certificate
IBM Data Science Professional Certificate
Microsoft Certified: Azure Data Scientist Associate
Imarticus Learning – PG Program in Data Science and Analytics

LinkedIn Learning also offers certain courses aligned with industry skills.

Breaking into Data Science Without Experience

This is the most common concern for career changers: “Can I really do this without experience?” The answer is yes—with planning and persistence.

If you’re breaking into data science without experience, focus on what you can control—your learning path, your portfolio, and your online presence. Companies often hire based on demonstrable skills, not just resumes. Here’s how to start.

Actionable Tips:

Build 3–4 strong projects and publish on GitHub
Start a blog sharing your learning journey
Contribute to open-source or volunteer data projects
Take part in Kaggle competitions
Attend webinars and industry conferences

Key Takeaways

Data scientist qualifications include a mix of degrees, certifications, and skills.
You don’t need a traditional tech background to start.
Certifications and self-learning platforms can fast-track your career switch.
Building a portfolio is essential for showcasing your abilities.
Employers value practical skills and communication as much as formal education.

FAQs

1. Do I need a Ph.D. to become a data scientist?

No.. most entry-level roles only require a bachelor’s or master’s degree along with relevant skills in programming & analytics.

2. Can I become a data scientist at 35 or older?

Absolutely! Many professionals successfully transition into data science in their 30s or 40s with the right preparation and learning path.

3. Which certification is best for a career switch?

Imarticus Learning’s PG program is industry-focused and great for learners. Google’s Data Analytics Certificate or IBM’s Data Science Certificate are also some starting points.

4. Is programming a requirement?

Yes, a bit of programming experience is required (in particular, knowledge of Python or R), but you don’t have to be a programmer.

5. How long to goal-to-job-ready?

If you focus, you can be ready for the job market in about 6–12 months.

6. Generally – what do data scientists earn in India?

Entry-level jobs range from ₹6–10 LPA on average, while experienced data scientists can earn over ₹25 LPA.

7. Are there remote opportunities in data science?

Yes, many data science roles offer remote or hybrid working models.

8. What’s more important—degree or portfolio?

A strong portfolio often outweighs a traditional degree, especially for career changers.

9. Can I switch from a non-tech role like HR or marketing?

Yes! Your domain knowledge combined with new data skills can give you a unique edge.

10. Should I pursue a Master’s in Data Science?

If time and budget allow, yes. But short-term certifications are also highly effective.

Conclusion

Stepping into the data science field may seem challenging but it’s more probable than ever to be able to break in. With the right data scientist qualifications, specific learning plan, and compelling portfolio, non-technical professionals can also get into this high demand field. The future is data-driven so it is a good time to explore your options and get involved.

Top Paying Companies for Data Scientist Salary in India

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Data science is no longer a buzzword—it’s a business imperative. Now that the world is data-driven, companies rely on data scientists to interpret complicated data and make strategic decisions. This growing demand is directly converted into huge compensation levels.

If you’re considering the most rewarding data science roles, this guide will walk you through the top companies offering the best data scientist salary in India, why they’re attractive, and how you can get in.

Why Data Scientist Salary Is Rising in India

The average pay of a data scientist in India has risen by more than 35% in the past five years, fueled by digitization, the adoption of artificial intelligence, and growing volumes of consumer data. From global MNCs to Indian tech startups, every industry is recruiting data professionals in large numbers. (Source)

A recent report by Nasscom noted that India possesses 11% of the world’s data science talent and is expected to double its talent pool by the year 2026. This trend is evident in salary levels, especially for top employers.

Top Data Science Employers in India

The following are the top data science employers in India who are reputed to pay more than market-average salaries to data science professionals:

1. Google India

Average Salary: ₹28–40 LPA
Credited for hiring PhD-level talent, Google’s data scientists work on groundbreaking AI and deep learning initiatives.
Benefits: Flexible work, top-notch infrastructure, and stock options.

2. Amazon India

Average Salary: ₹20–35 LPA
Data science is deeply embedded in every suggestion engine, pricing, and supply chain model.
One of the strongest hiring managers of entry through senior-level data experts.

3. JP Morgan Chase

Average Salary: ₹18–30 LPA
Quantitative analytics, credit risk modelling, and fraud detection are the functions filled.
Offers specialised algorithmic trading and financial modelling roles.

4. Microsoft India

Average Salary: ₹22–32 LPA
Focused on enterprise AI solutions, cloud data architecture, and AI research labs.
High emphasis on upskilling and global work.

5. Fractal Analytics

Average Salary: ₹15–22 LPA
One of the best companies for data scientists to work on AI-based consumer insights.
Known for innovation-first culture and industry exposure.

Table: Top MNCs for Data Science Job & Salaries

Company	Average Salary (INR)	Best Known For
Google	₹28–40 LPA	Machine Learning & AI Research
Amazon	₹20–35 LPA	E-commerce & Logistics Algorithms
JP Morgan Chase	₹18–30 LPA	Risk & Financial Modelling
Microsoft	₹22–32 LPA	Enterprise AI, Cloud Platforms
Fractal Analytics	₹15–22 LPA	AI for Consumer Behaviour

Tech Firms Actively Recruiting Data Scientists

If you are open to jobs outside the top 5, here are additional tech firms actively recruiting data scientists in India:

Tata Consultancy Services (TCS)
Flipkart
Zoho
Accenture
IBM India

These companies may not pay top dollar salary initially but provide good exposure, especially for junior professionals.

Average Salary of Data Scientist in India based on Experience

Experience Level	Average Salary (INR)
0–2 years	₹6–10 LPA
3–5 years	₹12–18 LPA
6–10 years	₹20–30 LPA
10+ years	₹35 LPA and above

Salaries also vary based on locations, with Bengaluru, Hyderabad, and Pune being at the top as hubs for data science careers.

Top Data Science Companies: Why They Are Top Favorites?

Project Ownership: Top companies allow data scientists to own end-to-end products.
Global Exposure: MNCs generally allow collaboration with global teams.
Research Focus: Google and Microsoft, for example, offer employment aside from commercial applications—pushing AI research.
Career Acceleration: Immediate growth, worldwide tool access, and cross-functional learning opportunities.

Humanised Learning Path: Where To Start?

Good companies offer great salaries, but it requires the right skills to get into those companies. That’s where industry-ready programs save the day.

One of those highlights is Imarticus Learning’s Postgraduate Program in Data Science and Analytics. Designed by industry experts, the program explores:

Python, SQL, and Power BI
Machine Learning & Deep Learning
Tableau and Generative AI
Live projects and placement guidance

A Day In My Life As A Data Scientist in Gurgaon India | CARS24

While awareness is increasing, the talent pipeline is thin. Analytics India Magazine tells us that only 45% of graduates from data science courses are industry-ready.

This is an enormous opportunity. If you’re skilled and certified, companies are eager to pay you top packages—no matter if you’re non-technical.

FAQs About Data Scientist Salary in India

Q1. What is the highest data scientist salary in India?

A1. Google and Amazon senior professionals can receive ₹40–45 LPA.

Q2. What industry provides the best to data scientists?

A2. Finance, e-commerce, and AI research labs are the front runners.

Q3. Are MNCs better payers than startups?

A3. Startups can offer high stock options and early responsibility, while MNCs offer stability and growth.

Q4. How much can a fresher data scientist earn?

A4. ₹4–10 LPA anywhere, based on where you are and what you know.

Q5. Which Indian city is best for data scientists?

A5. Bengaluru is the hub, followed by Hyderabad, Pune, and Gurgaon.

Q6. Are online data science courses worth it?

A6. Yes, if they offer industry projects and placement support.

Q7. How can I transition to data science without coding skills?

A7. A few tools like Power BI and Tableau allow one to get into analytics with zero coding.

Q8. How much difference is there between analysts and data scientists’ salaries?

A8. Analysts receive ₹4–8 LPA, while data scientists can start at ₹10 LPA and scale very high.

Q9. Is certification a factor in salary?

A9. Yes. Those who have certifications from highly credible institutions earn a much better salary.

Q10. Which of the tools should you be familiar with, according to top employers?

A10. Python, SQL, Power BI, Tableau, TensorFlow, and cloud platforms like AWS or Azure.

Conclusion: What This Means for You

Whether you’re a fresher or an established working professional who wishes to shift, data science remains one of the most profitable career choices. Salaries do not just indicate demand, but also the relevance that firms are placing on making decisions based on data.

Key Takeaways

The India salary for data scientists ranges between ₹6 LPA and ₹40+ LPA depending upon skill and experience.
Google, Amazon, JP Morgan, and Microsoft are some of the best data science employers in India, with high salaries and growth prospects.
Upskilling through structured programs like Imarticus Learning’s Data Science & Analytics course can prepare you bold enough to take these roles.

Explore the Imarticus Learning Postgraduate Program in Data Science & Analytics to boost your journey to the best-paying data science roles.

How Much Is Data Scientists Salary per Month?

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Introduction

With big data, of course, comes the work of data scientists in the front, and companies are motivated to make sense of and leverage huge amounts of information. Where there is high demand, though, there are great rewards packages, and data science is one of the highest-paying career choices.

But how much, exactly, do data scientists earn per month or what are the average data science earnings? This blog does not waste time, providing salary information, global comparisons, and trending data to shed light on mean data science compensation.

Data Scientist Salary Summary

The mean monthly data scientist pay is extremely volatile based on location, experience, and aptitude. Data scientists worldwide receive between ₹100,000 in India and about $10,000 in the US per month. Glassdoor quotes an international mean monthly rate of between roughly $7,000 and $12,000 based on the value placed on data abilities.

Salary prospects of data scientists increase manifold with experience and domain specialisation. This is even greater in industries like finance, healthcare, technology, and e-commerce, where decision-making is data-driven and has a direct impact on profitability.

Average monthly salary in a nutshell:

Experience Level	Avg. Indian Salary (per month)
Freshers	₹50,000 – ₹1.2 lakh
Mid-level	₹1.5 lakh – ₹3 lakh
Senior	₹3.5 lakh – ₹6 lakh+

Factors Impacting Data Scientist Salaries

There are certain key factors which determine the salary of data scientists:

1. Experience

Entry-level positions largely involve the execution of simpler data work, while high-level positions involve high-level decision-making, thereby fetching much greater salaries.

2. Education

Individuals with a higher qualification (Master’s or Ph.D.) usually get far more money. Machine learning or big data analytics certification also adds earning capacity.

3. Industry

Finance and technical sectors that critically rely on data-driven accurate information usually pay higher than other sectors.

4. Geographical Location

Market demand and cost of living play a major role in salaries. High-cost geographies like the US, UK, and Australia offer higher compensation packages.

5. Skill Set

The ability to code in a couple of programming languages like Python, SQL, R, or knowledge of machine learning tools works magic with the dollars earned.

Entry-Level vs Senior Data Scientist Salary

Diverging quite extensively for junior and senior data scientists could be the pay differences. Junior staff members earn $5,000–$7,000 per month, performing entirely routine analytical and data processing work.

Senior data scientists are responsible for big data projects and earn $13,000–$16,000 or more per month. Managerial experience, greater analytical proficiency, and ample experience are the bases for senior roles, which is rewarded with increased compensation.

Global Salary Comparisons

Country	Average Monthly Salary
United States	$10,000 – $12,000
United Kingdom	£4,000 – £6,000
Canada	CAD 8,000 – CAD 10,000
Australia	AUD 9,000 – AUD 11,000
Germany	€6,000 – €8,000
India	₹100,000 – ₹150,000

They are mirrorings of differences in local cost of living, economic conditions, and local demand in each market.

Freelance vs Full-Time Data Science Salary

Full-time or freelance working data scientist is notable while considering stability of income and take-home pay:

Freelance: Freelancers are paid hourly rates ($50–$250/hr), and their earnings depend on the volume of work. Freelancers may have more possible earnings but have income uncertainty.

Full-Time: Full-time data scientists are paid fixed monthly salaries and other perks like medical cover, leaves, and retirement schemes, which are secure.

Highest-Paying Data Science Jobs

There are certain specialised data science jobs with very high pay due to special know-how and significant contribution:

Machine Learning Engineer: Creates and builds forecasting models ($11,000–$14,000/month).
Data Architect: Manages sophisticated data infrastructure ($12,000–$15,000/month).
AI Research Scientist: Contributes to the establishment of AI methods ($13,000–$17,000/month).
Big Data Engineer: Manages big data systems ($10,000–$13,000/month).

Current Trends in Data Science Salary

The growing need for data-driven solutions in industry and business is pushing data science salary trends in the positive direction. The advancements in AI, machine learning, and cloud computing have created a rising demand for talented data scientists, and salaries are rising everywhere too.

All About Data Science and Analytics Course by Imarticus Leaning | Data Science For Beginners

New careers in the form of AI experts, data ethics officers, and predictive analysts also hold out a pink financial future for data professionals. Companies are heavily investing in holding on to talent by offering high salaries, competitive benefits, and highly profitable career advancement opportunities.

FAQs

1. What is the average global data scientist salary?

Average ranges from ₹4 LPA to ₹8 LPA and are predominantly location, industry, and experience dependent.

2. Does experience overshadow data scientist salaries?

Yes, veterans or experienced people generally receive twice or thrice as much as freshers.

3. Which countries offer the highest paying data scientist salaries?

The United States, Australia, and the United Kingdom tend to offer the best paying salaries due to demand and cost of living.

4. What are the levels of education that drive salary rates up in data science?

Higher levels such as Master’s or Ph.D. in the same field drive earnings much higher.

5. Which industries provide the highest data scientist pay?

Technology, finance, health, and consulting firms are known to provide best pay.

6. What are the essential skills that drive data scientist salaries?

Competencies like Python, machine learning, deep learning, and statistical analysis are in demand and are well paid.

7. Is data science a new career in terms of pay?

Yes, the pay for data science has risen over time and will keep on rising even more with increasing demand.

8. How does certification affect the data science pay?

Professional credentials do count towards earning higher money by crediting certain competencies and aptitudes.

10. Can work in data science remotely achieve competitive rates?

Indeed, remote working does equate to competitive marketplace rates, particularly in multi-nation technology companies.

Conclusion and Key Takeaways

Data science is a high-cost, high-emphasis profession signaling the irreplaceable worth of data in today’s businesses. Three quick nuggets are:

Experience and Education: The greater the ranks, they have a direct relation to much greater pay.
Location and Industry: Industries of high demand and locations offer the highest pay.
Skill Specialisation: Higher, specialisation skills have a larger role in earning capability.

Planning on changing careers and salary prospects in data science? Take a look at our Postgraduate Program in Data Science & Analytics at Imarticus Learning and become a most-desired data specialist today!

What’s the Average Data Science Course Duration? Breaking It Down by Program

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Introduction

So, you are probably wanting to enter in the world of data science, but wonder: how much time will it take? For students determining their next moves and working practitioners choosing to upskill, knowing about the duration of a data science course will help them make an informed choice. There are different types of learning available, from a weekend boot camp to a two-year master’s program, that may confuse you. But, fear not; we are going to lay it all down for you.

Table of Contents

Overview of Data Science Course Duration
Data Science Course Length: Short-Term vs Long-Term Options
Duration of Data Science Programs: Comparing by Type
Data Science Certification Timeline: Quick Upskilling Paths
Full-time vs Part-time Data Science Courses: What Suits You?
How Long Does It Take to Learn Data Science?: Influencing Factors
Master’s in Data Science Duration: What You Need to Know
Bootcamp vs Degree in Data Science: A Timeline Comparison
Key Takeaways
FAQs
Conclusion

Overview of Data Science Course Duration

Before diving into specifics -let’s get an overall view. The data science course duration varies drastically based on your choice of program & learning pace. For instance, bootcamps may wrap up in a few months, while a full-fledged degree could stretch over two years.

Here’s a quick glance at typical durations:

Program Type	Average Duration
Bootcamp	3 to 6 months
Certification Program	6 to 12 months
Bachelor’s Degree	3 to 4 years
Master’s in Data Science	1.5 to 2 years
Part-Time Programs	6 to 24 months

Data Science Course Length: Short-Term vs Long-Term Options

Whether you’re in a hurry to switch careers or looking to build a strong academic foundation, data science course length matters. The shorter options are often intensive, while longer courses go deeper into concepts.

Short-Term Courses (3-6 months):

Bootcamps
Online Certifications
Corporate Upskilling Modules

Long-Term Courses (1-2+ years):

Master’s Degrees
Integrated Postgraduate Diplomas
Online Degree Programs

Choose based on your learning style, goals, and time availability.

Duration of Data Science Programs: Comparing by Type

Understanding the duration of data science programs by category can help clarify which path works for you. Here’s a breakdown by program format:

Program Category	Duration
Online MOOCs (Coursera, edX)	4 to 8 weeks (modular)
Certificate Courses	3 to 12 months
Postgraduate Diplomas	10 to 18 months
Executive Courses	6 to 10 months
University Degrees	1.5 to 4 years

Explore platforms like edX, Coursera, or Harvard Online for high-authority examples.

Data Science Certification Timeline: Quick Upskilling Paths

If you’re aiming for fast-track learning.. then focusing on the data science certification timeline is essential. These are ideal for professionals who want to add value to their resume quickly.

Common timelines include:

Introductory certifications: 2-4 months
Intermediate to advanced courses: 6-9 months
Specialized tracks (like NLP or Deep Learning): 3-6 months

Certifications are flexible, often online, and backed by industry relevance.

Full-time vs Part-time Data Science Courses: What Suits You?

When weighing your options, think about your current commitments. Full-time vs part-time data science courses vary not only in length but also in learning intensity.

Full-Time Courses:

Duration: 6 to 24 months
Immersive and faster-paced
Ideal for students or those on a career break

Part-Time Courses:

Duration: 12 to 36 months
Suited for working professionals
Often asynchronous and more flexible

Your availability determines the best mode for you.

How Long Does It Take to Learn Data Science?: Influencing Factors

The big question — how long does it take to learn data science? The answer depends on multiple factors:

Your existing knowledge of math/stats/programming
Time commitment per week
Learning path chosen (bootcamp, certificate, degree)
Career goals (entry-level analyst vs data scientist)

Estimated timeframes:

Beginner to job-ready: 6 to 12 months with focused study
Career switch to senior roles: 1.5 to 3 years

Your progress will also depend on the consistency of your practice, real-world project exposure, and mentorship availability. Platforms offering guided capstones and community support may reduce your learning curve significantly.

Consistency is more important than speed.

Master’s in Data Science Duration: What You Need to Know

For those seeking academic depth and research opportunities, the Master’s in Data Science duration typically ranges from 1.5 to 2 years. Offered globally and in top Indian institutes, these programs offer intensive coursework.

Course modules often include:

Machine Learning & AI
Big Data Processing
Data Ethics
Capstone Projects

Some universities offer accelerated 12-month options, while others may go up to 24 months with internship components. You can also find flexible or hybrid options that accommodate professionals looking to study part-time while continuing their jobs.

Bootcamp vs Degree in Data Science: A Timeline Comparison

Let’s compare bootcamp vs degree in data science to see how they stack up in terms of time:

Criteria	Bootcamp	Degree Program
Duration	3-6 months	2-4 years
Career Support	Strong	Moderate
Cost	Lower	Higher
Ideal For	Quick upskilling	Long-term academic

Bootcamps like those by Springboard or General Assembly are gaining momentum in India for fast-track transitions.

Key Takeaways

Data Science course duration varies based on the format and your goals.
Short-term bootcamps can last 3-6 months, while degrees stretch up to 2-4 years.
Certification paths offer flexibility and career acceleration.
Full-time vs part-time courses differ in intensity and duration.
Choose based on how much time you can commit and what role you aim to land.

FAQs

1. What is the mean duration for a data science course in India?

The mean time is 6 months for bootcamps or certifications, and up to 2 years for master’s degrees with respect to course type and intensity.

2. Is it possible to get job-ready by taking a three-month data science bootcamp?

Yes, but it requires a high level of personal commitment, and little prior knowledge of coding or statistics will help.

3. Is part-time learning effective for data science?

Absolutely. Part-time courses are ideal for working professionals and offer flexibility while covering all core topics thoroughly.

4. How long does a data science certification take?

Most range from 4 to 9 months, with faster options available for foundational learning or specializations.

5. Are degree programs better than bootcamps?

Degrees offer academic depth, but bootcamps are quicker and often come with industry-led projects and job support.

6. Is it possible to learn all of data science online?

Yes, there are many online platforms that provide comprehensive online programs as they happen in person and self-paced programs.

7. Master’s in Data Science duration abroad vs. India?

International programs usually last 18- 24 months, and Indian master’s can vary from 12 to 24 months depending on the university.

8. Do short-term courses have placement support?

Many reputable institutes do offer placement assistance even for short-term or certification programs.

9. How long before I can land a data science job?

With dedicated study, projects, and internships, most learners get job-ready in 6 to 12 months.

10. Are there weekend-only programs in data science?

Yes, several institutes offer weekend-only schedules for working professionals that span 6 to 18 months.

Conclusion

The data science course duration can be as short as 3 months or as long as 4 years. There’s no one-size-fits-all answer. The decisions hinge on personal background, commitment, and professional intentions. No path is immune to merit or demerits – from boot camps to master’s degrees. Key is to align one’s own scope and goals with any option chosen. And don’t forget – the journey is just as important as, if not more than, the destination.

External resource: How to become a Data Scientist

Can Non-Tech Students Apply? Understanding Eligibility for the Data Science Course

Posted on July 8, 2025July 8, 2025 by Imarticus Learning

Data Science is a lucrative career, intertwining analytics, technology, and innovative problem-solving to extract meaningful insights from massive databases. If you’re eager to learn about the eligibility for the Data Science Course and worry that a lack of technical expertise may hinder you, don’t worry.

Most non-technical professionals are now making a mark in data science, with areas of specialisation varying from data analysis to business decision-making. As the industry is growing, there must be an explanation of who can apply, what are the eligibility criteria for data science, and if non-coding data science jobs do exist or not.

In this step-by-step guide, we shall discuss Data Science Course Eligibility criteria, entry criteria for data science, and how non-technical students can pursue this career path.

We will also mention quality learning options, like Imarticus’ Postgraduate Programme in Data Science & Analytics, to guide you towards taking that initial step towards establishing a successful career.

Let’s explore how data science for beginners has become more accommodating than ever, and why companies are increasingly opening their doors to a diverse range of applicants.

What Are the Core Data Science Eligibility Criteria?

When it comes to data science, institutions often set certain foundational requirements. However, these vary significantly. Below are the most common data science eligibility criteria you’re likely to encounter:

Educational Qualifications

Most require a bachelor’s degree, but the subject matter can vary from economics to literature. STEM degrees are helpful, but they’re not often the only route to success.

Mathematical Aptitude

Basic statistics and probability knowledge is helpful. Not always required, but helpful to be acquainted with the application of the words mean, median, and standard deviation.

Programming Fundamentals

Certain courses want you to have prior coding experience, but others are for beginners. If you’re starting from scratch, search for courses that include beginner Python or R modules as part of the course.

English Proficiency

Since most of the course material and lectures are done in English, institutes typically want you to be English proficient. Some even require you to produce an English proficiency test if you’re an international student.

Willingness to Learn

Maybe the most undervalued necessity is a curious mind. Data science is tough but worth it. A can-do mentality is priceless, particularly for tech students who wish to bridge gaps.

Despite these requirements seeming daunting, do not eliminate data science for beginners from your list. Data science for beginners has never been so cool.

Many schools explicitly structure their coursework to accommodate beginners, providing assistance that develops strengths incrementally.

Data Science Non-Coding Jobs: Fact or Fiction

People tend to visualise someone deeply coding when they hear data science. But data science non-coding jobs are in abundance. Data science needs people to interpret the findings of the analysis and present them in an appropriate way to stakeholders.

Data Visualisation Specialist: All about taking raw data and making it persuasive graphs and dashboards. Although little coding may be required, visualisation tools such as Tableau or Power BI are more important to learn.

Business Analyst: Spans technical teams and management. Examines data-driven insights for decision-making without necessarily drilling into code.

Project Manager: Oversees end-to-end data projects, ensures timely delivery, and coordinates with technical experts, data engineers, and clients.

If you’ve ever wondered, “Can non-technical students learn data science?” the existence of these roles should be a testament to the field’s flexibility.

Organisations increasingly recognise that data solutions must fit business objectives and user needs—skills that often come from those with non-traditional backgrounds.

Can Non-Technical Students Learn Data Science?

The quick answer is yes. For each highly technical job, there exists a corresponding one that works with other sets of skills. More to the point, a person who is a business or humanities individual can specialise in data interpretation, storytelling, or even leadership positions in analytics teams.

When you’re making the decision of whether data science is for you, ask yourself: Do you enjoy learning new technology? Do you like patterns and puzzle-solving? If you answered yes, you already possess a major piece of data science entry requirements—a true passion for getting meaning out of data.

Data Science for Beginners: Why the Sudden Rush?

Data science for beginners has been increasing to popularity for very good reasons:

Data Explosion: Businesses generate and store more data than before.
Accessible Tools Emergence: Cloud solutions and code-friendly libraries make it easier to manage data challenges.
Competitive Advantage: Businesses look for professionals who have the ability to extract data to unearth secrets and spur creativity.

Based on IBM Analytics, the demand for data-savvy individuals is increasing. Institutions are thus eagerly recruiting students from various backgrounds to form groups that can handle multi-dimensional data issues.

Who Can Apply for Data Science Courses?

One of the questions that most prospective students want to know is, “Who can enroll in data science courses?” The general answer is that most applicants are welcome as long as they fulfill the minimum data science entry criteria. This may involve a bachelor’s degree, maths fundamentals, and a desire to learn core software packages.

Finance, healthcare, law, or even sociology professionals can draw on their domain expertise in data-driven fields. With specialized knowledge complemented by analytical techniques, you can be a valuable contributor to linking technical results to practical uses.

Brief Overview of Key Data Science Entry Requirements

Requirement	Description	Importance
Formal Education	Bachelor’s degree or equivalent in any subject	High
Mathematics Competence	Conversant with basic stats and probability	Medium
Programming Skills	Useful but not necessary (most introductory-level courses)	Variable
English Competency	Comprehension of course material, lectures, and projects	Medium
Curiosity & Commitment	Ability to learn repeatedly and adjust	Very High

These columns identify the overall picture. Naturally, each program could have its variations. But the overall trend remains the same: subject matter knowledge and a high enthusiasm to learn.

Best Data Science Courses for Non-Tech Students

So, which are the best data science courses for non-technical students? The trick is to find courses that cut and slice challenging topics:

Introductory Programming: Choose courses that instruct in Python, R, or even SQL from the ground up.

Applied Projects: Learning from actual datasets sets a firmer base.

Flexible Schedules: Most part-time or online courses are made for full-time employees.

Recommended options are Imarticus’ Postgraduate Programme in Data Science & Analytics. Further, sites like Coursera and edX acquaint you with concepts at the foundational level in a structured way. Alternatively, KDnuggets is ideal for guidance, breaking news, and forum discussions on the go.

Adding a Fresh Perspective – Industry Stats Beyond Tech

According to a recent survey, more than 45% of companies that are not technically inclined embarked on data-driven projects over the past five years. Education, sports, real estate, and media sectors invest in data analysts capable of interpreting relevant information for better decision-making. This makes it possible for people with non-tech backgrounds to excel by combining industry knowledge with analytical skills developed recently.

Sources: LinkedIn , 365 Data Science

Path of Non Technical Person Towards Data Analytics Industry-Data Analyst And Data Scientist

FAQs: Frequently Asked Questions Before You Get Started

Do I have to code a lot for data science?

Not really. Most jobs need data visualisation, domain knowledge, or project management.

Is higher-level maths a hard requirement?

Basic algebra and statistics are normally enough to begin with. Some programs learn associated math concepts during the process.

Can students without technical backgrounds master data science in a short time?

Yes, if they work hard on the foundation courses and hands-on exercises. The learning curve is different for different individuals, but yes, it is possible with sustained effort.

Are there any entry requirements for data science in terms of previous work experience?

Most programs do not expect prior experience. Passion, fundamental knowledge, and hard work are valued more.

Which one should I learn first: Python or R?

Python is more common. But R is still used in academia and statistics. Either is fine for a beginner.

Who can enroll in data science courses if they are not from a STEM field?

Anyone who has a bachelor’s degree and is interested in learning may typically apply. Business and humanities majors typically prove to be good methods.

Is course accommodation an option?

Yes. Practically all institutions offer online, part-time, and full-time availability that suits different timetables.

What if I only want to specialise in data visualisation?

This is more than fine. Software tools like Tableau, Power BI, and particular data visualisation streams are available on the majority of courses.

Do best data science courses for non-tech students exist that include only applied learning?

There are certain courses that have more project-based emphasis than theory, ideal for students who learn well through practice.

Is beginner data science career placement guidance included?

There are certain institutes that have job placement facilities or career counseling available, especially hot programs that would like to possess a high rate of success.

Conclusion

Data science isn’t merely for programming hobbyists. The profession is expansive, dynamic, and more open than ever before to candidates without technical backgrounds.

If you have subject-matter expertise that you wish to use in conjunction with data-driven insights, or simply have a sense of enjoying pattern detection, then your non-technical background might prove beneficial.

More than ever before, today’s courses are designed to open doors to novices, progressively developing the technical and analytical proficiency required.

Key Takeaways

Inclusivity: Eligibility to a Data Science Course is generally kept open for diverse professional and academic backgrounds.
Multiple Pathways: There are plenty of data science non-coding jobs available—emphasise your ability, be it data storytelling, consulting, or visualisation.
Continuous Evolution: Space for innovative ideas exists within the industry. Continuous learning goes with the package of a successful data science career.

Remember, however, that the question of “Who can apply for data science courses?” has a straightforward answer: almost anyone who is willing to put in time and effort to learn. The future holds endless possibilities, and your background may just be the secret ingredient to be the standout in data science.

Chart Your Data Journey

If you are willing to go deeper, look at the Postgraduate Programme in Data Science & Analytics by Imarticus Learning.

It offers both foundational and advanced modules, so it is a strong contender for future students of any kind. Don’t let your non-technical background hold you back to discover the power of analytics and insights.