Data Science - Finance, Tech & Analytics Career Resources | Imarticus Blog - Page 3

How Data Science Training Will Ensure Business Continuity In The Post-Covid World?

Posted on April 28, 2021March 26, 2024 by Imarticus Learning

Lockdown restrictions have pushed us into recession along with a health crisis we have never seen before. Businesses are struggling to make ends meet, and many have had to take tough decisions like layoffs, frozen hiring, salary cuts, and more. The year has also shown us how vital digitalization is and why data science is the driving force in the post-COVID era.

One of the most prominent examples is remote work and how people have adapted to such a form of work. Businesses are accepting digital tools to optimize their functionality, and this is where data science comes in.

Digital tools not only help businesses measure ROI but also determines every small or big aspect like regulating spends, analyzing the long-term impact, and more. The change is making more companies include AI, VR, AR, and cybersecurity to transform their businesses.

Why is data science essential for business continuity?

Here are some of the reasons why adopting a data science course is crucial for current and aspiring businesses leaders:

Analyzing and forecasting

The pandemic gave us clarity of how uncertain times can be. Businesses can go through drastic changes due to such conditions unexpectedly.

With the help of data analytics, we can predict our future better. It helps us analyze risks and develop strategies to mitigate them.

Companies can use historical data from the current situation to estimate t

he trends of the future. Data science training can also give us cues of future obstacles and how to handle them efficiently.

Moreover, historical data can help plan different outcomes of businesses during an unfortunate phase.

Assessing resources for maximum utilization

While analyzing risks and mitigating them fast can prevent disrupting business processes, knowing the way to put resources to use during such times is also crucial.

Every company needs to imply analytic practices to get through critical times and understand the state of their business.

One example of this would be setting up an analytics team for finance. The process will help a business find how changes in the economy can or are affecting their business. Data assists in allocating resources and promotes effective decision-making.

Identify Opportunities

When you adopt data science, you can identify new opportunities for business continuity. The process of analyzing available data resources helps catch loopholes early and find new possibilities to overcome them.

Currently, most companies are aggregating COVID data and combining the same with employee data. It helps one understand better ways to support employees in this crisis. For example, updating a dashboard to check geographical data helps offices take preventive measures or recommend closedown. HR’s are using such data to determine if offices are safe to open or not.

Necessity of cybersecurity

As soon as you step into the data science career, you will know how critical cybersecurity is. It is the most vulnerable risk that any form of data can pose.

Much of this has been at stake since people have been working remotely. There is also an increase in cloud services, which need constant technical tracking, maintenance, and recovery.

Even when companies work remotely, they have to imply IT security to safeguard data exposure and threat. Data science and AI give us solutions to cyber threats and monitor network traffics more effectively across VPNs. It quickly detects points of breaches and infringement in real-time.

Helps in quick changes of services

E-commerce and retail industries use data science and AI to attract customers; both online and offline. Companies now leverage AI-powered solutions to get insights into changing consumer demands. Such data helps optimize the supply chain and minimize disruption.

There is a high usage of AI chatbots to get quick solutions to external factors. These chatbots communicate with customers, answer their questions, and record their responses. It is assumed that the usage of chatbots will multiply by 50 times compared to what it was before COVID.

Final thoughts

Business continuity is critical and crucial. More and more companies need to incorporate data science to make businesses effective and reliable. While it has been vital for a long time already, it will become non-negotiable in the post-COVID era. The above-listed reasons give you a brief insight into the bigger picture and why data science is the future of your business goals.

Stay Competent with most In-Demand Data Science Skills!

Posted on April 11, 2021April 8, 2024 by Imarticus Learning

What is Data Science?

The Science of combining capital processes, algorithms, and many such best tools to collect, manage and analyze the most important data to make business decisions is Data Science.

Who is a Data Scientist?

A computing professional beholding the skill of data collection, data storage and management, and data analysis enabling the organization to make data-driven decisions quickly are Data Scientists.

In-Demand Data Science Skills

Some of the most In-Demand Data Science Skills are:

Understanding of Math & Statistics

Online Data Science course in India is all about extracting the required information from the data. A depth understanding of mathematical probabilities and statistical methodologies helps in data analysis.

The majority of the data science models are built using one or more, known or unknown variables. Thus, the in-depth understanding of multivariate calculus is the key requirement to develop Machine Learning models.

A detailed understanding of functions such as Logit, Cost, rectified Linear unit, Step, Sigmoid, etc. is very much required to deal with the large data. Apart from these functions, the detailed understanding of Matrix algebra.

vector Algebra and Differential and Integral calculus help the Data Scientists to develop and understand the systems at a faster pace.

Programming Skills for Data Science

In order to achieve the objective to transform the raw data into business insights, Programming skills plays a crucial role. Among all the programming languages, the go-to languages are Python and R, Python being the lingua franca in the data science field.

Skill to wrangle the Data

The process of removing imperfections from the raw data to get the data that can be easily analyzed is known as Data Wrangling. The entire process includes acquiring the data, combining the data with relevant fields, and cleansing the data. In short mapping the raw data from one form to the other to set up the data to get business insights.

Management Skills

Database management is a prerequisite of Data analysis. The basic requirements for a Database Management System is the family of programs to edit and manipulate the data and the operating system to provide the specific data.

Data Science Career

The special skills set will definitely make you stand out from the crowd when the field and hence the number of jobs in the market are increasing at a faster pace.

Data Science Career	Job Requirements	Average salary
Data Scientist	· Data collection and organization · Find the pattern in the data to help the strategic business decision	$139,840
Data Engineer	· Batch Processing of the database · Build and maintain data pipelines · Make the information available to the Data Scientists	$102,864
Machine Learning Scientist	Research for the new data approaches and deep learning techniques.	$114,121
Machine Learning Engineer	· Create data funnels · In-depth understanding of statistics and programming · Designing and developing machine learning systems	$114,826
Data Analyst	· Transform the large Database to meet the purpose. · Prepare the reports to facilitate the decision-making process by communicating trends and insights from the data.	$62,453
Business Intelligence Developer	· Design and develop the strategies to make the specific information accessible for business decisions in lesser time. · Facilitate the system understanding to the end-users to use the data effectively	$81,514
Statistician	· Facilitate the Data Collection process. · In-depth Data analysis · Data interpretation · Identify the relevant trends from the data · Design data collection processes · Advise the overall organizational strategy	$76,884
Applications Developer	· Keeping track of the applications used in the business and internal interaction · Design the overall process flow of applications with the inclusion of development of user interface components etc.	$113,757

Average Salary data is taken from https://www.glassdoor.co.in/Salaries/data-scientist-salary

Why Does Data Ops For Data Science Project Matter?

Posted on April 10, 2021March 26, 2024 by Imarticus Learning

What is Data Science?

Data plays a major role in every organization as it helps in making decisions based on facts, statistics, and trends. Data science helps to trace insights from the raw data generated, which in turn is used to make major business decisions. Implementing Data Science in business has several advantages.

It helps in reducing risks and identifying fraud models. Data scientists are trained to identify data that stands out in some way and they use methodologies to predict fraud models along with creating alerts every time unusual data is identified.
It helps organizations in identifying when and where the products best sell. This helps the organization to deliver the right products at the right time as per the customers’ needs.
It helps the sales and marketing teams to understand their audience well and helps with providing personalized customer experiences.

Why Data Science Needs DataOps?

Data scientists deal with searching for data, labeling, cleaning, and performing other tasks that consume a lot of time. Especially if the business has to maintain a backlog legacy, then the amount of data keeps multiplying every year. This is where the need for DataOps rises.

DataOps involves collaboration, automation, and continuous innovation to data within a data-driven environment. Just like software can not be expected to provide exact results outside its live environment, data projects may also tend to behave similarly and may have to be reworked completely to make it work in a production environment. It also has to be continuously monitored even after deployment. Which makes it even more necessary to implement DataOps in a Data Science project.

DataOps plays a major role in building best practices throughout a function. Through continuous production, DataOps helps organizations to deliver value to a range of stakeholders.

Another significance of using DataOps in Data Science is Automation. Data moves through a particular process within an organization. While Data is entered in one form, it does not exist in the same form. Data scientists have to build data pipelines, test, and change them before data is deployed.

Making use of DataOps best practices, you can get a constant stream of data flowing through the pipelines. Which in turn, helps to attain real-time insights from the data. This ensures to reduce the time taken in converting raw data into Valuable information.

Combining Machine Learning with DataOps helps in maintaining a continuous workflow through internal communication. With this, the data quality can be controlled through version control, constant development, and integration. Combining ML also improves the insights and has a great potential for extracting value from DataOps.

Introducing DataOps in the organization also means changes in the work process. It builds a new ecosystem with consistent communication between the departments. Employees of each department work together, in real-time, sharing a common goal.

Therefore, using DataOps in Data Science ensures to develop projects keeping in mind the business impact along with delivering it in a way that the management can understand.

Why Data Science Course?

The Data Science course covers a mix of topics like mathematics, Tools, Machine Learning techniques, Business Acumen, and several algorithms. The main principle behind Data Science is finding patterns from gigabytes of raw data collected.

In today’s competitive world, more and more organizations are opening up to big data, and the need for data scientists is also on the rise. They get exciting opportunities to work on and also get to come up with solutions for businesses.

10 Data Science Careers That Are Shaping the Future!

Posted on April 9, 2021March 26, 2024 by Imarticus Learning

Data is wealth in modern days and data scientists will be in huge demand in the coming years. Firms require skilled professionals to analyze the generated data. Data analysis is also predicted to surge with the rise of new-age technologies like machine learning, artificial intelligence, etc.

According to reports, there is a shortage of expert data scientists in the market. One can opt for a post-graduate program in machine learning to gain the skills needed in the data science industry.

Let us see about ten data science careers that are shaping the future.

Data Scientist

Data Scientists have to organize the raw data and then analyze it to create better business strategies. Data is analyzed for predicting trends, forecasting, etc.

Data scientists are technical personals who are fluent in data analysis software and use them to predict market patterns. Firms will require more skilled data scientists in the future due to the need to process & analyze big data.

Business Intelligence Analyst

Business Intelligence (BI) analysts & developers are required to create better business models. They also help in making better business decisions. Policy formation and strategy development are key responsibilities of a BI analyst. Firms have to face market disruptions and need good business models/strategies to tackle them. BI analyst/developer will be in demand in the coming days.

Machine learning Engineer

Machine Learning (ML) Engineers are required for creating better data analysis algorithms. They have research about new data approaches that can be used in adaptive systems. ML engineers often use other technologies like deep learning, artificial intelligence, etc. to create automation in data analysis.

Applications Architect

Firms require good applications and user interfaces to run business processes smoothly. Applications architects choose or create the right application for their firms. Due to the rise in the complexity of data, firms will require better applications to manage it.

Statistics Analyst

A Statistics analyst or statistician is required to interpret the data and present it in an understandable way to non-technicians. They have to highlight the key insights in big data to stakeholders/fellow employees. Data analysis results are also used to make predictions and identify potential opportunities. You need to be good with numerology if you are thinking to become a statistician.

Data Analyst

They have to convert large data sets into a suitable format for data analysis. They also help in finding the data outliers which can affect the business. There is a lot of data generated every day as humans analyze less than 0.5 percent of data produced! Data analysts are already in huge demand in the data science industry.

Infrastructure Architect

Infrastructure architect in a firm makes sure that the applications, software(s), databases used by the firm are efficient. Infrastructure architects also help in cost optimization. They make sure that their firm has the necessary tools for analyzing big data.

Data Architect

Data architects mainly focus on maintaining databases.

They attempt to make the database framework better. With the rise of automation in data science, data architects are in huge demand to provide better solutions.

Enterprise Architect

Enterprise architects are IT experts and provide firms with better IT architecture models. They suggest stakeholders & senior managers in choosing the right IT applications for data analysis. Top companies like Microsoft, Cisco, etc. hire enterprise architects for maintaining their IT framework.

Data Engineer

Data engineers are required to create a good data ecosystem for their firms where the data pipelines are maintained. Data Engineers are required to choose better data analysis applications to provide real-time processing. They also help in making the data available to data scientists.

Conclusion

Data science is a growing field and there are a lot of job opportunities. You can learn Data Science Courses in India from a reliable source like Imarticus learning. One can also target any particular job role in the data science industry and should learn the necessary skills. Start your post-graduate program in machine learning now!

Secure Your Finance & Analytics Careers With Imarticus!

Posted on October 21, 2020April 1, 2024 by Imarticus Learning

With the growing commercial and technological advancements in the global markets today, Finance and Analytics are quickly becoming one of the most lucrative careers in the business world. Finance & Analytics play a major role in data evaluation for businesses to streamline decision-making processes.

They mainly inform the investment decisions of the companies by researching the microeconomic and macroeconomic situations with the organization’s principles. The professionals in these fields also prepare financial models that project future economic conditions.

Many businesses also hire them to evaluate the efficiency of their marketing techniques in comparison to the cost using numerical data. They also work in franchise-based businesses to track the individual franchise or a group and evaluate their positive and negative points to speculate the growth of the business.

Importance of Finance & Analytics

In this competitive world, businesses today need on-time data to formulate decisions for business. Every company needs good financial and analytics planning to utilize its resources in the best way possible.

The changing needs of businesses and the emergence of new models are making it crucial for companies to have sound Finance and Analytics professionals that help them improve their decision-making

Shape the business goals and plans for optimal performance. The Finance courses and Data Science Course with Placement help businesses to run efficiently and attain their aims fast.

Scope of Finance & Analytics

The careers in these fields are highly coved in the business industry with a good pay scale. The professionals in these fields have ample job opportunities in sectors like banks, pension, insurance companies, mutual funds, and securities firms, etc. Since they assist people in their financial decisions and investments, they can also open their own offices and work as self-employed.

They can also do accounts, auditing, taxes, and prepare financial statements. They can also specialize in other segments like Cost accountancy. The work scope of Finance and Analytics professionals also includes tasks like account management, secretarial work, share valuation, etc.

Job Opportunities

The wide scope of Finance & Analytics allows professionals to find employment in a range of sectors. They can find job roles under the following titles:

Financial Analyst
Chartered Account Trainee
Analyst – Back Office
Accounts Trainee
Assistant Consultant – Business Analyst
Junior Financial Analyst
Management Trainee – Finance
Research Analyst – Option Strategist

The pay range in these roles is also good. The average pay of a Financial Analyst in India is around INR 40,000. With experience, this number multiplies manifold.

Educational Requirements

To build a substantial career in these fields, one needs to have in-depth knowledge along with a good practical skill-set.

For this, students can join vocational programs in Finance and Analytics from reputed institutions. Imarticus is one such renowned organization that is offering courses in these subjects to train students and make them job-ready for the business world. By learning these courses students get:

Guaranteed job interviews
Comprehensive, industry-approved curriculums
Hands-on, experiential learning with cutting-edge technology
Training by industry experts and veterans
Extensive career support and assistance
Certifications accredited by world-class organizations
… And so much more!

Learning with Imarticus

Imarticus Learning is organizing the ‘Great Indian Futuristic Talent Drive’ offering good discounts on its various courses for the festive season from 21st October – 27th October 2020. You can avail of the following discounts by registering in the drive:

Fee Waiver on PG Classroom Programs – Up to 25% off

Fee Waiver on Online ProDegrees – Flat 20%

Fee Waiver on Classroom CIBOP & ProDegrees – Flat 15%

With the online learning mode of Imarticus, you get the facility to learn at home.

Thus, keeping safe during the pandemic and productively using your spare time to upskill yourself for an excellent career.

Register today and make your learning fun & affordable!

How Freshers Can Get Real-World Job Experience In Data Science

Posted on October 12, 2020March 26, 2024 by Imarticus Learning

Introduction

For most freshers, landing a Data Science job seems like a chicken-or-egg situation. You need to have hands-on work experience to get selected for such a job, but how do you get any work experience without first being hired?

By now, you must have heard, read or seen a lot about the scope for immense growth that a Data Science career can offer. However, for many aspiring Data Scientists, the reality appears to be hard-hitting.

The career potential of a Data Scientist is undoubtedly very rewarding once an individual gets the job, but getting the job without prior work experience is the main obstacle they face. Below, we examine some practical solutions to this dilemma:

Work on personal Data Science projects

This is an interesting and highly practical way to gain real-life Data Science experience. Once you finish a project, you can showcase your work on a platform like GitHub. Focus on small projects, and try to demonstrate important Data Science skills in your efforts.

The advantages of working on your own project are that you gain hands-on experience in generating ideas, collecting data, cleaning data, analysing data and building predictive models.

Therefore, you gain a comprehensive understanding of the entire process. As far as possible, try to script clean codes and develop clear visualizations that potential stakeholders can find easier to follow.

Do not attempt to display too many skills at once, as you might end up unnecessarily complicating matters for your audience. Simple and small projects will illuminate the core skills you wish to draw attention to. For example, consider obtaining a complicated datasheet and cleaning it up. This simple project will demonstrate your prowess in:

Scoping a data project and formulating a suitable plan
Gathering data using different collection methods
Contemplating different data cleaning methods and choosing the most suitable one
Handling different data formats such as XML, CSV and JSON

Contribute to open-source projects

The best way to enhance your coding skills and get hands-on Data Science experience is to join an open-source community. Providing solutions to projects that are already in progress will help you deal with real-world problems, while giving you a taste of what working in a Data Science team would be like.

As a member of an open-source community, you need to constantly communicate with the other stakeholders when making your contributions. Open-source projects are an excellent way to access Data Science libraries, such as NumPy, Pandas, Scikit-learn, and more. Above all else, being a part of these communities will help you build a professional network with relevant people in the Data industry, and also significantly add to your existing knowledge.

Make tutorial / educational content

If you have confidence in your Data Science skills and knowledge, you can try authoring a Data Science blog feed, or creating tutorial videos that explain the core concepts of Data Science. These are excellent ways to highlight your abilities to prospective employers.

In-person meetups

After you complete a Data Science course, in-person meetups can present great opportunities for face-to-face interactions with industry leaders and representatives. Meetups are essentially corporate events being held in your city, such as business conferences, presentations, seminars, expos or coding competitions.

These events are excellent venues for networking with like-minded professionals who work for a range of different organizations. A simple Google search with keywords like Data Science meetups, along with the name of your city, will generate information about ongoing or upcoming events near you.

Volunteer for a good cause

Many non-profit organizations need Data Science professionals to volunteer for them. This is a good way to give back to society, while at the same time, you could get to work alongside experienced Data Scientists who can guide you and offer valuable career advice.

The tasks you perform can be showcased in your resume, and will be considered as valid work experience. Poverty, Environmental Protection, Equal Education, Public Health and Human Rights are some of the non-profit areas that you can contribute to.

Conclusion

The career scope for a Data Scientist is tremendous, but it often proves difficult to get a Data Science job without a certain amount of relevant work experience. The key is to show recruiters that you possess the requisite expertise and skills to do justice to the job if you are given the opportunity, and the steps listed above will go a long way towards accomplishing that.

SQL For Data Science: One-Stop Solution For Beginners!

Posted on September 29, 2020March 26, 2024 by Imarticus Learning

Data science has earned the reputation of being the most promising job of the times, even during this pandemic crisis. With the current changes in the global business and economic background, data science has proven to be a more relevant career opportunity. If you are following the subject and have a keen interest in making a data science career choice, you must have heard about SQL as well.

SQL is used to access and manipulate data. It helps to store data, access whenever you need it, and retrieve if need be. SQL training will give you a much-required head start in the highly competitive job market.

Why is SQL Important in Data Science?

Today’s business decisions are data-driven. Data is generated all through the day, across the globe. The amount of data generated every day is simply astonishing – about 2.5 quintillion bytes. This underlines the enormity of the subject we are dealing with.

Now that data is available, what is the next thing? How are you going to make sense of this huge amount of data and use it to make a decision? Data science steps in here. You need to collect, organize, and process them to make sense of the data and to derive insights. To do this, you need tools. This is what SQL does. It is a querying language used to store, access, and retrieve data.

What is Structured Query Language (SQL)

SQL is a language that is primarily concerned with managing relational databases. SQL is the typical API for such data tables. While using SQL, data can be accessed and managed without changing the databases. You can perform a variety of actions including updating, querying, deleting, and inserting data records. Oracle, MySQL etc. are examples of such databases which use SQL.

SQL works based on some simple commands that are associated with different data tasks. These commands can be used to create database and tables, insert, delete, or update data, to alter table and database, drop table and index.

How to Create a Table Using SQL

Let’s see how to create a table using SQL commands. Remember to use UPPERCASE letters for SQL commands, and use semicolons to terminate commands.

You may follow the steps given below to create a database.

Step #1 Creating a Database using SQL

CREATE DATABASE: Use this command to create a database “Test”.

USE: This command activates the database.

CREATE test;

USE test;

Your database named test is ready and activated.

Step #2: Creating a Data Table

It is as easy as typing a command to create a table, just like the way you created the database. All you need to do is to decide on the variables you want to include in the table.

Suppose you want to create a table with the following features:

Serial Number (SL)
Purchase item
Cost
Number of pieces

You can use the command CREATE TABLE to create the table. The four features of the table are SL, purchase item, cost, and number of pieces.

Now, to create the table, use the command as given below:

CREATE TABLE cart (SL NOT NULL PRIMARY KEY AUTO_INCREMENT Purchase_item TEXT, Cost INTEGER, Number_of_pieces INTEGER);

You might have noticed that we have given the value we are going to provide for each feature. The Serial Number is a primary key, which means it represents a unique data. The purchase item will be entered as text, while cost and number of pieces will be entered as numbers.

The table is now ready with the field names and the value to be entered to each cell of the table. To see how the table is executed, type the command “DESCRIBE cart”. This will give you a display of a table with the given features.

Field	Type	Null	Key	Default	Extra
SL	Int(11)	NO	PRI	NULL	Auto_increment
Purchase Item	Text	YES		NULL
Cost	Int(11)	YES		NULL
Number of pieces	Int(11)	YES		NULL

Step #3: Data Input

Once you create the table, you need to enter data into the respective fields. To do this. Use the SQL command “INSERT INTO”.

To insert values, follow this pattern:

INSERT INTO cart VALUES (NULL, “Rice”, 75, 10)

The “null” value is assigned to SL, as it will follow the command and auto_increment from 1.

The entered value will look like:

SL	Purchase item	Cost	Number of pieces
1	Rice	75	10

Follow the same pattern to enter more values.

Data Science is trending these days. Getting trained in a skill that is much in demand improves your chances of getting hired manifold.

So, choose a good data science course and give your profile an extra edge while competing for career opportunities.

What Does It Take To Be A Good Data Scientist?

Posted on September 18, 2020April 2, 2024 by Imarticus Learning

What does a data scientist do?

The importance and applications of data science have grown exponentially over the past decade. Data science is still in its nascent stage and there’s a whole lot to be identified about this discipline. Businesses have started implanting strategic decision-making tools that leverage data science.

Data helps businesses by providing them with hidden insights and helps them predict the future outcome of their decision. This helps organizations to make a better business decision.

Let’s delve deeper into what these data scientists do and how it helps the organizations.

Finding a solution to business problems

One of the most basic and key responsibilities of data scientists in an organization is to identify existing challenges and problems that a business is facing and finding solutions to remedy the situation. This might seem like a generic responsibility of every important professional but the main difference here is that data scientists use tons of relevant data to find the problem.

They try to come up with solutions after properly assessing the situation using various analytical tools that provide them with useful insights. They leverage statistical analysis, data visualization and mining techniques to provide effective solutions.

Find out relevant data using complex research

The 21^st Century businesses are complex than ever, there are various factors that determine the fate of an organization. With the number of complexities that exist, it’s very difficult to figure out what impacts your business and how it does that.

Data scientists simplify this for organizations by studying all variables affecting a business. They use complex research work to identify the variables that have a maximum impact over the business and which are highly relevant.

Identify patterns and trends

Another important work of a data scientist that helps businesses is to identify patterns and trends. Data scientists use sophisticated data analysis techniques to find trends and patterns from the data sets at hand. These data sets are generally historical records of the organization. It helps them to identify the existing patterns and trends which is used to make predictions regarding the future movement of the variables.

How to become a data scientist?

Data science is one of the most in-demand skills in the industry and given the wide range of applications that it has, the demand for a data science professional will continue to rise in the future. One of the most common questions in the minds of data science aspirants is how to become a data scientist? There is no specific answer to this particular question. It depends on what stage of your career you are at and the skillset that you have.

A data science course by reputed institutions such as Imarticus Learning guarantees placement with top-notch firms in the industry in addition to providing relevant knowledge and skills. It also helps you provide guidance from the industry experts who are highly experienced in this domain.

Let’s delve deeper into some of the most prominent skills for data scientists that you should hone if you are planning to opt for a career in this field.

Analytical skills

One of the key skills that are required in this profession and that forms the base of all your work is your analytical skills. One should have an analytical mindset and should be able to identify trends and patterns from a big chunk of data. You should be able to assess a situation from a different perspective to reach a successful conclusion. One should be trained to work with software like Python and R and should be equipped enough to handle large volumes of data.

Problem-solving skills

Another important skill that you need to work on is your problem-solving skills. You need to use data to figure out challenges that exist in the business. After you have figured out the problems you will have to provide a solution using data analytics tools that will help the business to achieve its goals and objectives.

What Are the Topics Covered in a Data Science Course

Posted on September 16, 2020October 19, 2021 by Imarticus Learning

Data Science consists of six major topics. These are:

Statistics
Linear Algebra
Machine learning
Programming
Data Visualisation
Data Mining

Through a data science course, one can have a better understanding of these topics. These topics are discussed further in detail through the course of this article.

Statistics:
Statistics is the mathematical branch of business which includes the processes of collecting, classifying, analysing and interpreting the numbers to draw an understanding of them and thus, draw a conclusion.
Statistics is implemented in various ways in the field of data science. These are:

Experimental Design: The answers to various questions are found through means of experimentation including samples size, control groups, and so on.
Frequent Statistics: The user is allowed to define the value of the importance of the result of data.
Modelling: Having statistical knowledge is important for the further success of a data scientist, even though it does not see daily use in their lives. Old statistical models are being slowly replaced with the new models.
Linear Algebra: Linear algebra is a section of mathematics which involves the process of linear mapping between vector spaces. It sees use in data science in the following ways:
1. Machine learning: When working with data that is dimensionally high and involves matrices, linear algebra comes in very handy. It’s component analysis, and regression techniques see the most use along with eigenvalues principals.
2. Modelling
3. Optimisation
Programming

Coding is a very important part of data science and being able to code well is extremely important for any data scientist. Having a background in computer science is thus a large advantage, however, if one does not have such a background then these skills can easily be picked up through a data science course.

Automating tasks not only saves time and effort but also helps make the process of debugging, understanding and maintaining codes simpler. The practical skills involved in programming are as follows:

Being comfortable with data development. Usually, people with a software development background find it easier to work on commercial projects at a higher scale.
Having experience in the database area, such as knowledge of modern databases like NoSQL and cloud as well as on older databases like SQL, is important to any employer.
Teamwork and collaboration are important as most work as a data scientist would be tone in groups. Thus communication with teammates and holding strong relationships would help keep productivity at a maximum.

Important practices here involve:

Maintenance
Avoiding the use of hard values
Documentation and commenting continuously
Refactor the code

Machine Learning

Machine learning is important in data science and has shown use in a large number of groundbreaking technologies like self-driving cars, drones, image classification, speech recognition, so on and so forth. This field is expanding every minute and expanding very quickly. Thus the knowledge of machine learning and its implication would be necessary for any good data scientist.

Data Mining

The process involving the exploration of data and extraction of vital information is called Data Mining. A data science course makes the understanding of such a topic much clearer. The commonly used slang in data mining are listed below.

Data wrangling/Data munging
Data Cleaning
Data scraping

Data Visualization

Even though the term may seem self-explanatory, there is more to it than what we see. Data visualisation is the process of communication of data and its results through pictorial or graphical representation. The goal of it is to communicate the findings of the data in the simplest way for understanding.

Thus a data science course would further equip aspiring data scientists with all the tools in the toolkit necessary for optimal success in their career.

Why Data Scientists Should Follow Software Development Standards?

Posted on July 16, 2020October 12, 2022 by Imarticus Learning

Introduction

Technology has become the flagbearer of changes to which we are subjected to daily. Therefore, it impacts us in every possible way. How technology comes to us should mostly positively affect us. Therefore, it becomes important for the people driving this change to adhere to some pre-defined standards for improved quality of work and standardization of the same.

Data Science has come a long way. It has become one of the most popular subjects giving people the best in class in jobs and putting them in a position of the drivers of change. A Data Science course in Chennai would help you in becoming employment ready.

Data Science has enabled handling the bulk of data with ease. With Data Science you can drive different conclusions from the same set of data. You just need to change the algorithm.

Who is a data scientist?

Your Data Science career can bring a lot to the table. Initially, the word ‘Data Scientist’ was used for people who used to organize and analyze a huge amount of data. However, the role of a data scientist has drastically evolved in its due time course.

Today, data scientists develop algorithms that make sorting, compiling, and analyzing the sets of data a cakewalk. Effective data scientists have standardized the processes and have developed a standard procedure to work things out. These data scientists are technically well-equipped and can build complex algorithms which can be repeatedly used to make a task easy.

They have a strong quantitative background and are usually result oriented. Also, they have extensive knowledge of different programming languages like R, Python, Tableau, SQL, etc. As the demand for automatization is increasing, data scientists can access more and more jobs.

The need for data scientists to follow Software Development Standards

Standardization is important everywhere irrespective of the field. Therefore, these data scientists need to adhere to a specific set of software development standards that are already in place.

In the times where cybersecurity is a major issue, it is really important to have some software development standards in place. This would ensure that the new software is being designed keeping in mind these standards which will consider the safety and security of data and information of the end-users of that particular person.

Development standards have been also designed to keep uniformity across the organization. These standards ensure that the work output is generated at a certain level. Also, with software development standards, a set of consistent rules are laid down which makes the job of a data scientist quite easy.

With Software Development standards, you can use the same algorithm for different purposes with slight modifications. Also, it ensures that the program written by a data scientist is clear and understandable and adheres to the statistical principals. With standardization, codes will be written in a language that is understood by all.

Having simple rules is important. Software development standards follow a structured approach when it comes to writing a code or designing software. It bridges the gap between your research and the final product which you want to build.

These standards are up to date and are formulated keeping in mind different quality assurance standards. This would ensure that a quality product in the form of codes is delivered. With the implementation of these practices, it would be really easy for the data scientists to meet the requirements of their customers and deliver quality results.

Conclusion

Following a set of standard procedures can make the work of data scientists’ error-free to a great extent. Also, it enables easy quality checks ensuring good delivery of an end product.