If you have ever found yourself sinking in endless datasets, struggling with analysis bottlenecks, or just round-the-clock by the intricacy of data-driven decision-making, you are not alone. Data analytics is a vast field that requires precision, speed & efficiency. What if you could work smarter, not harder?.. 

So, here is a SMART technique, an established means of optimising work, improving productivity, and furnishing structure to data analysis tasks. Whether you are in a Data Analytics Course or a professional working in this field, these productivity hacks will lighten the work ahead of you, save you time, and even get you better results.

1. Why Productivity Matters in a Data Analytics Course

Productivity is not merely about working quicker- it’s about working more intelligently. As data gets more complex, analysts need to streamline their workflows to meet industry demands.

How Poor Productivity Impacts Data Analytics Work

Applying structured methods such as SMART goals and automation can enhance efficiency, lower stress, and boost accuracy in data-driven projects.

2. Learning the SMART Technique for Data Analysts

The SMART technique assists data professionals in establishing clear, actionable goals:

Specific: Define the exact outcome (e.g., “Optimize data reports by reducing processing time by 30%”).

• Measurable: Define key performance indicators (KPIs) for success.
• Achievable: Make the goal realistic with resources available.
• Relevant: Make the goal relevant to business goals and professional development.
• Time-bound: Establish deadlines for monitoring progress.

By applying SMART methods, analysts can plan their learning and work processes better.

3. Data Analytics Efficiency Techniques for Faster Results

In order to be more efficient, implement these established data analytics methods:

• Batch processing: Perform batch procedures rather than repetitious manual entries.
• Data visualization tools: Easily visualize insights utilizing Power BI, Tableau, or Excel.
• Data cleaning automation: Leverage Python or R scripts to purge duplicates and inconsistency.

4. SMART Goals for Data Professionals: How to Set & Achieve Them

Making your SMART goals may better enable your success in a Data Analytics Course:

5. Excel Hacks for Data Analysts to Save Time 

Excel is a core tool for data analysts. Give these hacks a try to enhance efficiency:

• Use Pivot Tables: Rapidly summarize large datasets.
• Master Keyboard Shortcuts: Minimize time spent working with Excel.
• Apply Conditional Formatting: Emphasize key trends in information.
• Automate Reports with Macros: Minimize manual efforts in repetitive work.

6. Automation of Data Processes for Increased Productivity 

• Automation saves time spent on repetitive work:
• Utilize Python scripts for automated data preprocessing.
• Utilize SQL stored procedures for reusable queries.
• Use APIs to retrieve real-time information.
• Establish Excel macros for regular calculations.

7. Maximizing the Data Analysis Process for Maximum Efficiency

To maximize data analysis, utilize these optimization techniques:

• Predefine queries to minimize execution time.
• Use cloud-based storage for seamless collaboration.
• Adopt machine learning models for predictive analysis.

8. Best Practices for Data Analytics Productivity

Follow these best practices to stay ahead in your Data Analytics Course:

• Break large projects into milestones for better tracking.
• Use version control tools (like Git) for dataset management.
• Limit multitasking to enhance focus and accuracy.

9. Task Management Strategies for Data Analysts 

Successful task management eliminates data overload. Test these techniques:

• Kanban boards: Map workflows with Trello or Jira.
• Time-blocking: Schedule rigid time slots for analysis tasks.
• Priority-based work: Apply the Eisenhower Matrix to separate urgent vs. important work.

10. Key Takeaways

• The SMART technique assists in organizing learning and work objectives.
• Automation and time-saving Excel short cuts boost efficiency.
• Task management eliminates burnout and boosts productivity.
• Best practices such as batch processing and data visualization speed up workflows

FAQ’s

Q1. How are SMART goals applicable in a Data Analytics Course?

SMART goals track progress for students and workers; they allow you to set realistic learning goals, enhance productivity through structured objectives, etc. 

Q2. Which automation tools are best suited for data analysts?

Commonly used for automating the so-called mundane tasks to accelerations in analyses: Python, stored procedures in SQL, R scripts, and Excel macros.

Q3. Some typical time-saving Excel tricks for data analysts?

Excel is an incredible tool for data analysts, yet handling huge datasets is a time-consuming activity. Some critical time-saving tricks are:

• Using Pivot Tables to analyze and summarize large datasets rapidly.
• Utilizing Excel shortcuts (such as Ctrl + Shift + L for filters) to enhance productivity.
• Utilizing Macros and VBA scripts to automate repeated activities.

Applying Conditional Formatting to highlight trends and anomalies in data without manual intervention.

Q4. In what way does automation contribute to productivity in data analytics?

Automation helps in ridding systematic, manual work processes for data analysts to spend time together for further data insight rather than for data preparation. Utilizing tools such as Python scripts, SQL stored procedures, and R automation, experts can:

• Minimize errors in data processing
• Save hours of manual effort
• Facilitate real-time data analysis and reporting
• Ensure consistency in workflows and reports

Q5. What are the optimal tools for streamlining data analysis processes?

The optimal tools for streamlining data analysis vary with the type of work. Some of the most commonly used tools are:

• Python & R for complex analytics and automation.
• Tableau & Power BI for interactive visualization.
• SQL for querying and managing structured databases.
• Excel for rapid calculations, pivot tables, and data structuring.

Q6. How can SMART goals enhance learning in a Data Analytics Course?

Having SMART goals in a Data Analytics Course keeps students organized, tracks progress, and attains career-focused results. For example:

Rather than “I would like to know Python”, a SMART goal is “I will take an online course in Python and implement it in real-world datasets in 3 months”. 

SMART goals provide a concise map, avoiding distractions and better retention.

Q7. What are professional best practices for data analytics productivity?

• To improve productivity in data analytics, professionals must:
• Employ automated scripts to manage data cleaning and preprocessing.
• Adopt a systematic approach to analysis, utilizing pre-defined templates.
• Update skills periodically to remain up-to-date with new tools and techniques.
• Use dashboards for real-time monitoring and decision-making.

Q8. How do data analysts effectively manage multiple projects?

Managing multiple projects is overwhelming, yet task management techniques will come to the rescue:

• Prioritizing tasks using urgency and importance (Eisenhower Matrix).
• Applying project management software such as Trello, Asana, or Jira.
• Time-blocking dedicated hours for data analysis activities.
• Working seamlessly with cloud-based platforms such as Google Sheets and Microsoft Teams.

Q9. What are the greatest productivity challenges facing data analysts, and how to overcome them?

Data analysts have several productivity challenges that are:

• Data overload → Overcome through data visualization tools and summarization methods.
• Repetitive manual work → Automate using Python scripts and SQL queries.
• Inefficient workflows → Optimize through standardized procedures and automation.
• Lack of structured learning → Set SMART goals to ensure ongoing skill development.

Q10. How can I enhance accuracy in data analysis while keeping productivity intact?

Accuracy is critical in data analytics, but it shouldn’t come at the expense of efficiency. To balance both:

• Use automated validation checks to identify errors before analysis.
• Document workflows to ensure consistency and reproducibility.
• Cross-validate findings with multiple tools (e.g., Excel, SQL, and Power BI).
• Minimize manual data entry by integrating APIs and automation scripts.

Conclusion 

A person must adopt a strategic approach to productivity in order to become an efficient data analyst. Transform your data analytics workflow with these techniques: the SMART approach, using automations, and time-saving tools. These productivity hacks are enough to set any individual apart in the industry, be it a data analytics course or swinging into the spotlight with real-world projects.

Raw input data are generally available in tabular formats, where rows highlight observations or instances and columns show attributes or features. Feature engineering is a tactical process which is used to transform raw data into valuable features that can be utilised for creating accurate predictive machine learning models. This uses Python programming and Power BI as key visualisation tools. 

Feature engineering helps to prepare models with reasonable prediction even when a few missing raw data are missing. This is possible when the work is done using the most relevant features that eliminate undesirable or non-influential ones.  

The Process of Feature Engineering

Feature engineering in machine learning broadly consists of four processes. They are as follows:

Feature creation

Feature creation is a process that uses the human brain’s creativity and is performed by addition, deletion or rationalisation of existing data variables. This activity is done by professionals who have chosen a career in data analytics. 

Transformation

The process of adjusting the selected variable so that it may contribute effectively towards the accuracy and performance of the predictive model is known as transformation. The process ensures that all the features follow the same scale. It also helps to make the model flexible to accept a variety of data inputs.

Feature extraction 

Feature extraction is an automated method of generating new meaningful variables out of the raw data provided. This makes the predictive model more reliable and accurate by reducing the input data volume. The process involves text analytics, cluster analysis, edge detection algorithms, and principal components analysis.

Feature selection

Feature selection is the process of selecting the most useful variables out of many for incorporating them into the predictive model. Irrelevant or noisy data are left out since they are useless to the model and negatively affect the model when infused into the system.

Tools of Feature Engineering

Many feature engineering tools help make good predictive models. A few of them are described below:

FeatureTools

FeatureTools helps to perform auto-feature engineering. It is particularly good at converting meaningful raw data to useful features in machine learning.  

AutoFeat 

Linear predictive models with automated feature engineering and selection process is a key strength area of the AutoFeat tool. AutoFeat helps us to select the unit of useful variables.

TsFresh 

TsFresh is an open-source Python package tool that helps to correlate and automatically calculates a large number of time series data. It helps to extract details such as peak, average value, time reversal symmetry statistics etc. Knowing Python programming is of immense importance in today’s world.    

OneBM 

This tool works on the raw data, irrespective of whether they are relational or non-relational to the predictive model. It can generate both simple and complicated features.

ExploreKit

It is a structured framework to produce automated features. It can combine multiple data and may unearth common useful features thereby eliminating duplication. This makes the predictive model compact and error-free. 

Feature Engineering Techniques in Machine Learning

Some of the regular feature engineering techniques used in preparing data for machine learning models are as follows:

Imputation 

The most common problem is missing data, which arises out of the following typical cases of human errors, data flow interruptions, privacy issues etc. Numerical and categorical imputations are applied in these cases.

Handling outliers 

This is a process of suitably dealing with specific data which is exceptional in terms of value and category. When several outliers are very few, the process of removal is applied. However, if the number of outliers is quite a few, then removal will cause us to lose enormous data and hence be avoidable. In these cases, the process of replacing values, capping or discretisation is applied.

Log transform 

Logarithms are used to convert data of a skewed distribution into that of a normal distribution. This process is also used to handle confusing data. The efficiency of this tool may be best expressed visually with Power BI.    

Scaling 

It is the process of bringing all data under a common scale by scaling up or down, as required. The purpose is to make the features similar in terms of their range. The two standard procedures adapted here are normalisation and standardisation.

Binning

Excessive and irrelevant data and unwarranted numbers of parameters deter the performance of models. Binning is the process of segmenting several data and features and eliminating unwanted ones from the system.

Feature split 

This is a process of segregating features into two or more parts to closely monitor the same with the help of the data available. This characteristic produces meaningful features with better algorithms and is better numerically representative.

One hot coding 

It is a commonly used technique in machine learning. It is used to convert categorical data in a specific form which can be easily interpreted by machine learning algorithms and can be used in creating successful predictive models. 

Benefits of Feature Engineering in Machine Learning Models

Using feature engineering in machine learning applications has some notable advantages, which are as follows:

Flexibility 

Better features impart better model flexibility. Even if a wrong model is chosen by mistake, the flexibility of features will generate good predictions.

Simplicity 

Flexible featured models are simple and quick to operate.

Better Results 

With the same available data, the selection of better features gives way to better results in predictive models. 

Conclusion

A career in data analytics is a booming option for modern youth. A data science course with placement assistance makes this opportunity lucrative. Having a machine learning certification is very necessary for a prospective candidate. Several reputed institutes in India offer machine learning certification courses.

The Postgraduate Program in Data Science and Analytics at Imarticus will give the prospective candidate a perfect start to their career. This is a data science course with placement and the duration of the program is 6 months. The classes are held on weekdays where the mode of teaching is both online as well as classroom training. 

Visit the official website of Imarticus Learning for more course-related details.

Data science is a rapidly growing field that has seen a huge surge in demand over the past few years. As the demand for data scientists grows, many individuals are asking the question – can my data science course get me a job in one of the top companies?

In this article, we will explore the answer to this question and how a Data Science Course With job assurance can help you secure a placement in one of the top companies.

Data Science Overview

Data science is a field of study that combines computer science, mathematics, and statistics to analyse and interpret large datasets. Data scientists are responsible for collecting, analysing, and interpreting large datasets to answer business questions and develop predictive models. Data science is becoming increasingly important in many industries, including finance, healthcare, retail, and education.

Data Science Job Assurance

The demand for data scientists is growing rapidly in the corporate world as companies are looking to leverage data to gain a competitive edge. Data science professionals have the potential to make a significant impact on a company’s bottom line by uncovering new insights and driving innovation. Top companies such as Google, Apple, Amazon, and Microsoft are actively recruiting PG In Data Analytics for their teams.

Data Science Course with job assurance plays an important role in the placements of top companies, as it helps organisations identify the best candidates for their roles. Data scientists are skilled in using advanced analytics tools, including machine learning and AI, to analyse large datasets and uncover patterns and trends. They also help organisations build predictive models to make better decisions and improve their business operations. Data science is also used to optimise marketing and product strategies and identify new opportunities in the marketplace.

Benefits of Taking a Data Science Course

Here are some of the many benefits of taking a Data Science course:

Learn to Analyze Data

A data science course will provide students with the necessary skills to analyze data and draw meaningful conclusions. This is essential for any modern business, as data is constantly being generated and needs to be interpreted in order to make informed decisions.

Develop Problem-Solving Skills

A data science course will also help students develop problem-solving skills. Data science requires students to identify a problem, understand it, and find the best solution to it. This is a valuable skill to have in any field.

Understand Big Data

Big data is becoming increasingly important in the business world. A data science course will help students understand how to process, store, and analyze large datasets. This can be a great asset when looking for a job in the current market.

Develop Technical Skills

Finally, a data science course will also help students develop technical skills such as coding, database management, and machine learning. These skills are becoming increasingly important in the current job market, and a data science course can be the perfect way to gain these skills.

Better Placement Opportunity

One of the biggest benefits of a Data Science Course With Placement is the improved placement opportunity it can provide. Data science is a highly sought-after skill, and employers are always looking for candidates that possess the necessary skills and knowledge. By completing a Data Science Course With Placement, you will demonstrate to employers that you are up-to-date with the latest advancements in the field and have the ability to apply them to real-life scenarios.

Finding The Right Data Science Course

There are a few factors that you must have in your mind while finding the right data science course:

A. Researching Available

Courses When looking for the right data science course, the first step is to do some research. There are many online courses available, so it’s important to evaluate each one to determine the best fit for you. Look for courses that cover topics that are relevant to your career goals or that focus on specific programming languages or technologies. Be sure to read reviews and ask for feedback from other students who have taken the course.

B. Cost and Time Commitment

When looking for a data science course, be sure to consider the cost and the time commitment. Many online courses are offered at a discounted rate, but be sure to read the fine print to ensure that you understand what you’re signing up for.

Parting Words

Data science is an exciting and fast-growing field that has become increasingly popular over the past few years. With the right data science course and the right attitude, it is possible to secure a placement in one of the top companies. However, it is important to remember that success does not come overnight and that dedication and hard work are needed in order to achieve success. With the right attitude and determination, you can make the most of your Data Analyst Training and secure placement in one of the top companies.

Ready to take your data science skills to the next level and land the job you’ve always wanted? Sign up for our PG In Data Analytics program today!

Technology has revolutionized the field of data analytics. Technology has drastically changed how businesses and individuals use data to make decisions, from collecting data to analysing it. As a result, companies have recognized that having access to accurate and timely insights is vital for success in today’s competitive market. This is why pursuing a career in data analytics in 2023 could be so beneficial – you will possess highly sought-after skills by many organizations.

Data analytics allows you to gain insight into customer behaviours and preferences, helping you identify patterns and trends that can inform strategy or increase profitability. You will also be able to create reports from large volumes of structured and unstructured data, enabling decision-makers within an organization to plan for the future.

Data analytics also provides a unique opportunity to develop skills that apply to various industries, such as healthcare, finance, and retail. You can become an invaluable asset to any organisation with the right combination of technical knowledge and business acumen. Plus, the demand for data analysts is increasing – the Bureau of Labor Statistics projects that job growth in the field will increase by 28% between 2019 and 2029. This means there will be plenty of opportunities available even five years from now

Considering all of these benefits, it’s no wonder why pursuing a career in data analytics in 2023 could be so lucrative. You’ll have access to cutting-edge technology, gain valuable industry experience, and receive a competitive salary. So if you’re looking for a career path that can take you far, data analytics may be the perfect fit.

What skills does a Data Analyst need to outshine in the field?

Data Analysts need a wide range of technical and analytical skills to be successful in the field. Technical skills include knowledge of statistical software packages such as SAS, SPSS and R, database management systems like Microsoft SQL Server or Oracle, programming languages such as Python or Java, and data visualization tools like Tableau.

Analytical skills are also key for Data Analysts. These involve interpreting data effectively, identifying trends and anomalies, developing logical conclusions from quantitative information, and communicating findings easily. In addition to being technically proficient, Data Analysts should have strong problem-solving abilities, be detail oriented and possess excellent communication skills to collaborate with other team members effectively. Finally, Data Analysts should be able to work independently, prioritize tasks and manage multiple projects simultaneously.

What do you learn during a Data Analytics course and training?

A data analytics course with job interview guarantee and training will introduce various data-driven concepts, techniques, and tools. These include descriptive statistics, hypothesis testing, predictive modelling, machine learning algorithms, big data analytics and visualization.

Students in data analyst training will learn how to use industry-standard software tools like Microsoft Excel and Tableau for cleaning, preparing, analysing and visualizing data. In addition to practical skills in collecting and working with large amounts of raw data from multiple sources, students should expect to understand the underlying theories behind decision-making, problem-solving and risk assessment using statistical analysis.

Finally, PG in Data Analytics typically covers the principles of effective presentation design so that you can effectively communicate your data-driven insights to stakeholders and project teams. With the skills from a data analytics course, you will be able to identify patterns in data and make informed decisions that improve businesses and organizations.

Learn and grow with Imarticus Learning:

Advance your career in data science and analytics with Imarticus Learning, now offering a job interview guarantee program. Our comprehensive curriculum is packed with applicable content to prepare you for success – from fundamentals through complex concepts, we have you covered! And even if it’s been a while since school or programming isn’t your thing – don’t worry; we’ve got an introductory foundation module to get everyone up to speed quickly so that no one will be left behind. Don’t hesitate any longer—take the plunge into this emerging field today and employ tangible skills that are sure to make a positive impact on businesses around the globe!

Course USPs:

• Job Interview Guarantee
• Job-specific Curriculum
• Live Learning Module
• Real World Projects
• Dedicated Career Services
• KPMG India OE Organised Hackathons

For further details, contact the Live Chat Support system or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Data analytics is one of the most happening careers these days. According to multiple surveys, graduates and postgraduates seek additional qualifications and jobs in this sector. The main reason is the handsome salary and great career prospects. Therefore, having data analyst training in today’s time can give you a significant advantage over others.

However, interest in this field alone is not sufficient. As a data analyst aspirant, you need to study well and get certified by a premier institute. The best thing about getting an education from renowned institutes is that you get a solid foundation in data analysis. You know all the techniques required to extract information from it. Due to your strong grip on the fundamentals and experience of working on live projects and with industry experts, you stand out as a candidate.

In this regard, you can look at premier institutions like Imarticus Learning for PG in data analytics.

What do you get from the data analytics training from Imarticus Learning?

Apart from getting a strong foundation in the field of data analytics, you will also get a chance to build the core concepts in data processing. With this postgraduate program in data science and analytics, you will also get to understand the prevalent practices in the industry. Their teaching methodology has projects, which require a practical approach towards solving problems. That means you will work out problems in the ways professionals do in the real world. So you will be prepared beforehand with the skills that your employer expects from you after a year of being on the job. So, you can sail through the training time and get down to the game immediately after joining. That also means you can perform better and get a quicker promotion.

With that being said, Imarticus Learning also provides you with a data analytics course with placement in the top companies of the world. The hiring partners of Imarticus are industry leaders and offer a great experience for beginners. They get a chance to learn new things like handling a project in a team, taking responsibility as a professional, etc. This way, you can build up leadership skills and can also guide your colleagues in testing times. Resultantly, you build a great reputation as an employee, which further reflects on your career progress.

The top companies where you will get placed via Imarticus Learning

Capgemini

Capgemini helps other client companies process tons of data to help the enterprise grow its revenue. They also help to improve the value chains by transforming the operations by providing the necessary insights with the help of AI and ML, giving them the edge over the other competitors. So they hire many data analysts who need to deliver data-powered solutions for these client companies.

RBS

The data analysts at RBS perform data analysis that involves the visualisation of data and mining the data for procuring the solutions and giving a predictive analysis for the problem. The data analysts also need to provide data-based warehousing along with extraction, collection and cleaning of data. As a data analyst, you need to know Tableau, Power BI and SQL data modelling, along with great storytelling abilities, to deliver a presentation to the stakeholder and team leaders. You also need to know how to make ETL packages for regulatory reporting in the company. 

HSBC

HSBC being a banking organisation, requires data analysts for financial analysis roles. It requires a lot of data analysts who can help them improve the quantity of data delivered, maintaining the same standards they have set for financial reporting. As a data analyst, you are expected to know the programming languages that are used in data analysis, and these include SAS, SQL, and Python as compulsory languages. The data analyst also knows how to build dashboards so that they can give the stakeholders a clearer view of what insights can be generated for the data. The environment at HSBC is quite fast-paced, so the data science aspirant should be able to show dynamism on the job and also be able to deliver end-to-end individual projects.  

KPMG

The data analysts at KPMG are armed with all the high-quality data analysis techniques to acquire data from primary/secondary sources. Then they need to understand the warehousing of data to store and maintain the large client databases. They also need to identify, analyse and interpret new trends from the complex data sets improving the target operations.

To sum up, completing the data analyst training from Imarticus Learning offers you the foundation you need to begin your career in this domain. The course, with its updated curriculum, live projects, experienced faculties and placement assistance, ensures that you fulfil your dream of becoming a data analyst without any hiccups.

Data Analytics: The Secret to Growing Your Business Using Data

In today’s world, where every aspect of human life relies on technology to function better, the amount of data generated is unimaginable. Now, companies are using this information to their advantage. They are using it to develop their products, market them, and target customers better.

Some of the biggest companies, who are pioneers or market leaders, have been able to make it big by being data-driven in taking business decisions. That is why it makes perfect sense to have a career in business analytics.

What exactly do we mean by data analytics for business?

By taking raw data and by uncovering patterns with the help of insights that are given within the data, the business finds and deploys the best strategies to engage the customers. In addition, as mentioned above, the information they unearth goes into their new product development and other improving other areas of the business.

To implement this process, companies employ data scientists and analyst who uses specialised tools to find relevant information from the raw data. But successful operations necessarily require a data analytics-based foundation which also has an inclusive data strategy.

Is a strategy backed by data analysis good for business?

Of course, it is good for business, as it will ensure you make a more informed decision rather than going ahead with your instinct. This way, as a business owner, you will make the correct decision regarding products, investments, and company operations. Now, this is true for businesses of every scale. Big corporations have spent billions on big data and AI to improve their customer experience and deliver the exact product per market requirements.

For instance, Netflix was able to deliver House of Cards and Arrested Development after studying the data insights. Likewise, Coca-Cola used data analytics to understand their consumer demography and how and why its target ads were performing. 

What else does data analysis do?

Apart from helping measure the key performance indicators, data analytics helps in many other ways possible. Some of the main ones are given below:

• Help to mitigate fraud and other risks: Data analytics help reduce fraud and theft, unused receivables, staff safety, and legal responsibility.
• Improve security: Data analytics allow organisations to recognise potential fraudulent behaviour and trace perpetrators after identifying them.
• Streamline the organisation’s operational behaviour: Data analytics can help in designing operations and controlling them effectively so as to reduce the wastage of human effort and achieve operational excellence.
• Provide quick customised service: Companies have started using advanced analytics instead of excel sheets and SQL databases to handle quantitative data. This helps organisations to attend to their customers in real-time. In addition, this helps to build a loyal fan base.

How to implement data analytics in business?

If you want to become a data analyst in India, then you need to enrol in a good data analytics course with placement. After registering for the course, you will learn techniques that will help you leverage data analytics for business insights and help your company grow. Here’s how you will implement data analytics to help in the advancement of your business.

• You need to decide which key metrics to use: As business analysts conducting business intelligence projects, you need to understand the conversion of prospective leads into customers and their conversion rates. You must also understand the customer spending data, review your advertising strategy, and avoid common revenue model development mistakes.
• Create relevant dashboards for data visualisation: Dashboards are required for explaining statistics to your non-tech personnel so that they can understand all the essential points in a conference or meeting and comprehend what the key areas of focus are. This will help the workforce plan in a better way.
• Picking the correct tool for data analytics: Apart from understanding the metrics, you also need to understand which data analytics tool provides deeper insights and a better perspective of tracking customer data. 

Imarticus data analytics course, Post Graduate Program in Data Analytics & Machine Learning trains you to handle every aspect of data analytics and take advantage of the best analytics and big data analytics tool. It is one of the few data analytics courses with placement that can accentuate a person’s career and help them grow and prosper in their jobs.

To sum up, as time progresses and the inclusion of technology in human lives increases, the need for data analysis and management will amplify. Therefore, it is almost certain that a career in business analytics is feasible and has the prospect of growth in the coming decades.

Data Analytics Training Takes Center Stage: Structure and Placement

In recent times, especially after the boom in internet usage and the popularity of smartphones, the amount of data generated each year has increased significantly. Now, businesses are utilising this information to their advantage. They are using it for product development, marketing and whatnot.

However, to understand the raw data, one needs the help of a data However, to understand the raw data, one needs the help of a data analyst. And, owing to the demand for such professionals, various institutions are organising relevant courses to provide adequate training and help you become a data analyst.