Data Analytics Course Fees in India – A Comprehensive Detail

Data has altered the way we perceive the world during the last ten years. The countless emails, texts, and YouTube videos we watch are included in the roughly 2.5 quintillion bytes of data generated every day globally. This data is produced by e-commerce companies, online banking, insurance, electrical transactions, social networking, and the migration of mass media to the internet. As a result, data analytics has widened its scope due to the massive amount of data generated by online interactions over the previous ten years. Given that India has the third-largest Internet user base worldwide, the discipline of data science is here to expand. By the end of 2022, 85% of businesses would have implemented big data and analytics technology, per a World Economic Forum (WEF) workforce prediction from 2018. In this article, we will discuss in great detail about the various facets of the Data Analytics Courses in India including average data analytics course fees, curriculum, duration of the training programs, and more.

Just to give you an idea, as per reports, In 2025, India will have a 32 percent global market share. India accounts for more than 6% of all job posts for data analytics globally.

What is Data Analytics?

Data Analytics includes the equipment, procedures, and methods for gathering, organizing, and storing data as well as data analysis and management. Applying statistical analysis and technology to data for identifying trends and resolving issues is the main goal of data analytics. Analytics also gives companies the ability to react swiftly to changing market trends and acquire an advantage over rival companies. The ultimate motive of Data Analytics is to improve corporate performance.

A real-life Example of a Data Analytics application

A very relatable example of Data Analytics in action is the tailored viewing suggestions offered by Netflix. What effect does this feature have on Netflix’s ability to succeed commercially and how does Netflix arrive at these recommendations?

As you might have imagined, data collecting is where it all begins. From its 163 million global customers, Netflix gathers a variety of information, including what viewers watch and when, what devices they use, whether they pause and resume a show, how they rate certain content, and precisely what they search for when seeking new stuff to watch.

Then, using data analytics, Netflix can connect all these different data points to develop a thorough viewing profile for each customer. The recommendation algorithm generates personalized (and remarkably accurate) recommendations for what the user would want to watch next based on significant trends and patterns within each user’s watching pattern.

The user experience is greatly impacted by this kind of personalized service; according to Netflix, personalized recommendations account for over 75% of viewing engagement. Looking at their income and usage numbers, you’ll notice that Netflix constantly dominates the global streaming market—and that they’re increasing year after year. This effective use of data analytics also greatly adds to the organization’s growth.

Applications of Data Analytics

Let’s discuss a few of the industries where data analytics is used:

Retail: Data analytics enables retailers to comprehend the demands and purchasing patterns of their customers to forecast trends, suggest new items, and grow their business.

2. Healthcare: Healthcare-related businesses look at patient data to provide possibly fatal diagnoses and treatments. Data analytics aid in the development of novel medication development strategies.

3. Manufacturing: With the use of data analytics, the manufacturing industry can find innovative ways to cut costs. They can address complicated supply chain problems, personnel shortages, and equipment failures.

4. Banking sector: Financial institutions and banking companies employ analytics to identify potential loan defaulters and customer attrition rates. Additionally, it aids in identifying fraudulent transactions.

5. Logistics: To create new business models and optimize routes, logistics organizations use data analytics. This guarantees that the delivery will arrive on time.

Data Analysis vs Data Analytics

Even though the terms “data analytics” and “data analysis” are occasionally used interchangeably, data analysis is a subset of “data analytics” that is concerned with examining, modifying, modeling, and deriving meaning from data. Data analytics encompasses data analysis as well as the procedures and equipment needed to complete it.

Business Analytics vs Data Analytics

Data analytics also includes business analytics. To help businesses make better decisions, business analytics employs data analytics techniques like data mining, statistical analysis, and predictive modeling

Data Analytics vs Data Science

Data scientists typically spend their efforts on generating broad insights, whereas data analysts concentrate on providing answers to specific queries.

While data science employs the data to forecast and/or comprehend the future, data analytics depicts the condition of reality as it is now or has been in the past.

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Who is a Data Analyst?

For stakeholders to understand data and apply it to make strategic business decisions, the data analyst acts as a gatekeeper for the organization’s data.

Here are some examples of typical jobs and duties for a data analyst:

• Utilize data visualization software to oversee the distribution of customer satisfaction surveys and report on the outcomes.
• Develop needs, provide success criteria, manage and carry out analytical projects, and assess outcomes in collaboration with business line owners.
• Keep an eye on procedures, systems, and practices to spot areas for improvement.
• To define concepts and assess needs and functional requirements, proactively interacting with stakeholders, business units, technical teams, and support teams.
• Convert crucial inquiries into specific analytical tasks.
• assemble fresh information to address client inquiries, compile and organize information from many sources
• Using reports and/or interactive dashboards, use analytical techniques and tools to extract and deliver fresh insights to clients.
• Transform data and concepts into visualizations.
• Work together to identify the best product solutions with the team’s data scientists.
• Creating, testing, and maintaining backend code.
• Create data procedures, specify data quality standards, and put data quality processes in place.
• Take charge of the codebase and provide suggestions for refactoring and improvements.
• Create tools and models for data validation to make sure the data being captured is accurate.
• Work as a team to assess and analyze important data that will be utilized to influence future business plans

Data Analytics Trends in India

The exponential growth of digital technology over the past ten years has altered the universe’s geography. In 1995, the total capacity of the digital cosmos was somewhere about 140 billion gigabytes. This amount increased to 50 trillion gigabytes in 2020. To handle this digital society, millions of new workers would be required, and businesses would compete for hundreds of thousands of workers. With 600 companies, India is currently one of the top 10 nations for big data analytics. The Indian industry for big data analytics is currently worth $ 2 billion and is anticipated to grow to $ 16 billion by 2025.

Data Analytics Jobs in India

While the supply of data science professionals has only grown by 19% in India, demand has increased by 400%. 21% of analytics positions in India are open to young graduates. Employers include companies like Tech Mahindra, TCS, Genpact, Wipro, and HCL Infosystems.

At present, India accounts for more than 6% of all job posts for data analytics globally. By the end of 2022, 85% of businesses would have implemented big data and analytics technology, per a World Economic Forum (WEF) workforce prediction from 2018. In 2025, India will have a 32 percent global market share. There are now 97000 open positions in India, 24% of which are centered in Bengaluru. Mumbai and Gurgaon are not far behind. 

Below listed are some of the profiles, that a Data Analyst works as:

• IT systems analysts utilize and create solutions to address issues in information technology.

• Healthcare data analysts have the chance to raise people’s quality of life by assisting researchers and clinicians in solving difficulties they face every day. The volume of information flowing from the healthcare sector is expanding quickly.

• Operations analysts are employed internally by large corporations and focus on corporate operations such as internal reporting systems, product manufacturing, and distribution.

• Data scientists gather and examine data. Strong data analytics knowledge, including software tools, programming languages, and data visualization abilities, are required for this position.

• Data engineers are responsible for optimizing the infrastructure that supports various data analytics operations, with a concentration on big quantities of data.

• Data analytics consultants‘ job is to provide the firm with insights to help it run more efficiently. The emphasis is on projects with a distinct beginning and end because they work for numerous companies in a shorter amount of time.

What is the average salary of Data Analysts in India?

In India, a data analyst typically earns between INR 3 and INR 4 lakhs annually. An annual salary of up to INR 6,03,120 is possible for a data analyst with 5 to 9 years of experience. The top paying positions for data analysts are data scientist and data science engineer, with typical yearly salaries of INR 6–8 lakhs.

Data Analytics Courses in India

It frequently happens that we are unable to get employment right away after finishing an undergraduate or graduate course. The fundamental cause of this is frequently a lack of exposure to a real-world, professional workplace. Any full-time school will undoubtedly teach the theories and concepts pertinent to the discipline properly, but occasionally we may not have the chance to engage with the business world and gain insight into the kinds of abilities that are in demand from a career standpoint. There are numerous organizations and online platforms in India that offer data analyst training and employment to fill this void.

Candidates who want to own their firm enroll in these courses. Classes in data analytics aid in obtaining consumer insights from a huge consumer dataset. Students who want to work as data scientists or analysts can pursue a B.Tech CSE, an M.Sc in Data Analytics, or an M.Sc in Computer Science with an emphasis in data analytics and data science. For positions as data analysts, you need a bachelor’s degree, which typically takes 3–4 years to complete.

To learn the fundamentals of data analytics, they can also enroll in online data analytics courses on Coursera, CareerFoundry, or Udemy. They can also earn globally recognized data analytics certifications that cover the tools and programming languages necessary for data analysis, such as Big Data, Python, and Machine Learning.

Data Analytics Course Fees:

Data Analytics course fees depend on the type of course, you are pursuing. A Certificate course in Data Analytics is a lot more affordable than the PostGraduate Degree in Data Analytics:

• Data Analytics Course Fees in Certification level is  INR 500 – 10,000
• Data Analytics Course Fees in Diploma level is  INR 9,300 – 2, 00, 000                                              
• Data Analytics Course Fees at the Undergraduate level are INR 90,000 – 21, 00,000
• Data Analytics Course Fees at the Postgraduate level are INR 20,000 – 23,00,000

Data Analytics Course Fees: Online vs Offline Course Fee 

The Course fee generally depends on the duration of the courses, the distinctive features that are provided by each institute, and the post-course assistance. Both online and offline institutes offer high-grade coaching, so, in most cases, the syllabus isn’t the deciding factor in the difference in data analytics course fees for different institutes.. However, practical projects and access to learning portals, notes, slides, and study materials can be major factors for the difference in data analytics course fees.

The best Data Analyst Courses in India

Let us divide data Analyst courses into 3 broad categories – Certification, Diploma, and Masters in Data Analytics:

Certification Courses

1. Become a Python Dat Analyst – Udemy –  Data Analytics Course fee INR 8,000
2. Data Analyst with Python Coursera – Free Enrollment
3. Introduction to Data Analytics – IIT Madras – Free Enrollment

Diploma Courses

1. Diploma in Data Analytics – Maharshi Dayanand University – Rohtak – Data Analytics Course Fees INR 30,000
2. Post Graduate Diploma in Business Analytics (PGDBA) – IIM Calcutta, IIM Kharagpur, and ISI – Data Analytics Course Fees INR 23,23,000
3. PGDM (Big Data Analytics) – Goa Institute of Management – Data Analytics Course Fees INR 11,22,000

Masters Courses

1. MBA in Business Analytics (MBA- BA) – IIM Bangalore – Data Analytics Course Fees INR 25,00,000
2. MBA (Analytics) – IIM Kashipur – Data Analytics Course Fees INR 15,42,000
3. MBA (Business Analytics) – Symbiosis Center for IT – Data Analytics Course Fees INR 14,06,000

Modules Covered In the Data Analytics Course

1. Data Structures and Algorithms
2. Business Fundamentals
3. Data Visualization
4. Simulation
5. Marketing Analytics
6. Retail Analytics
7. Probability and Statistics
8. Text Analytics
9. Supply Chain Analytics
10. Statistical Analysis
11. Machine Learning
12. Pricing Analysis
13. Statistical Analysis
14. Machine Learning
15. Pricing Analytics
16. Customer Analytics
17. Relational Database Management Systems
18. Data Collection
19. Forecasting Analytics
20. Optimization
21. Social Network Analytics

Tools that are employed in the Data Analytics course

The tasks of data processing, data manipulation, data analysis, and pattern and trend identification are greatly facilitated by the tools used for data analysis. Top tools for data analysis include:

• Business Analytics with Excel

It is the most well-known spreadsheet program in the world. It also has computation and graphing features that are excellent for data analysis. No matter your area of expertise or additional software you might want, Excel is a standard in the industry. Its useful built-in features include form design tools and pivot tables (for sorting or tallying data). It also provides a wide range of additional features that simplify data manipulation.

• Python

Python is an essential tool for every data analyst and has a wide range of applications. It places a higher priority on readability than more sophisticated languages, and because of its widespread use in the computer industry, many programmers are already familiar with it. Additionally, Python is quite adaptable, with a vast selection of resource libraries suitable for a wide range of diverse data analytics jobs.

• R programming

R is a well-known open-source programming language, much like Python. Software for statistical and data analysis is frequently made with it. Python’s syntax is simpler than R’s, but R’s learning curve is more challenging. However, it is widely used for data visualization and was created expressly to handle complex statistical computing tasks. Similar to Python, R has a network of open-source software called CRAN (the Comprehensive R Archive Network), which contains more than 10,000 packages.

• Jupyter Notebook

An open-source web program called Jupyter Notebook enables you to create interactive documents. These incorporate narrative text, equations, live programming, and visualizations. It’s an excellent tool for displaying work as a data analytics tool: Over 40 languages, including Python and R, are supported by Jupyter Notebook, which runs in the browser. Additionally, it connects with big data platforms like Apache Spark and provides a variety of outputs, including HTML, pictures, and videos.

• Apache Spark

With the use of the software framework Apache Spark, data scientists and analysts can quickly analyze enormous data volumes. It was initially created in 2012 and then donated to the charitable Apache Software Foundation. Spark is a distributed analytics framework created to examine unstructured large-scale data sets. While other frameworks are similar (like Apache Hadoop), Spark is incredibly quick. It is therefore frequently employed in the creation of machine learning models that require a lot of data.

• SAS

Statistical Analysis System, or SAS for short, is a well-known commercial set of business intelligence and data analytics tools. The SAS Institute created it in the 1960s, and it has changed ever since. Today, client profiling, reporting, data mining, and predictive modeling are its key uses. Software designed for the enterprise market is typically more reliable, adaptable, and simple to use for big businesses. This is because their levels of in-house programming ability frequently vary.

• Microsoft Power BI

Power BI is a relative newcomer to the market for data analytics solutions with a lifespan of fewer than ten years. It was first developed as an Excel plug-in but was later updated as a standalone suite of corporate data analysis tools in the early 2010s. With a short learning curve, Power BI users can easily build interactive visual reports and dashboards. Its strong data connectivity is its key selling point; it works well with Excel as well as text files, SQL servers, and cloud sources like Google and Facebook analytics.

• Tableau

One of the top commercial data analytics tools is Tableau, which allows you to build interactive visualizations and dashboards without a lot of coding knowledge. The suite is incredibly user-friendly and handles massive volumes of data better than many other BI tools. It offers a drag-and-drop interface that is visual (another definite advantage over many other data analysis tools). However, Tableau is only capable of so much since it lacks a scripting layer. For making more intricate calculations or data pre-processing, for example.

• KNIME

KNIME (Konstanz Information Miner), is a cloud-based, data integration platform. Software engineers from Konstanz University in Germany created it in 2004. Although KNIME was initially developed for the pharmaceutical sector, its ability to compile data from several sources into a single system has led to its use in other fields. These consist of machine learning, business intelligence, and consumer analysis.

• QlikView

Business intelligence, data visualization, and data analytics tool QlikView aid in accelerating the value of businesses. Some numerous products and services may be used for nothing or a 30-day free trial.

• RapidMiner

A tool for analyzing data and creating machine learning models is called RapidMiner. RapidMiner has several products, including Studio, GO, Server, Real-Time Scoring, and Radoop.

• Splunk

Splunk is a program used to examine data collected from websites and applications. The Splunk features that are temporarily free to use include Splunk Free, Splunk Enterprise, and Splunk Cloud.

Eligibility for applying to the Data Analytics course

• Non-IT Professionals in sectors such as banking, HR, marketing and sales, etc.
• BI Professionals
• Data Analytics Professionals
• Project Managers
• Software Developers
• Information Architects
• Undergraduates and freshers can apply for the course as well

Prerequisites to apply for Data Analytics courses:

There is no precondition to participating in this training as a result. However, if you have the following skill sets, it would be highly appreciated. – 

• Analytical skills
• good problem-solving and critical thinking abilities
• Communication Skills

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FAQs

1. Are Data Analyst courses held online or offline?

To learn the fundamentals of data analytics, they can also enroll in online data analytics courses on Coursera, CareerFoundry, or Udemy. They can also earn globally recognized data analytics certifications that cover the tools and programming languages necessary for data analysis, such as Python, Big Data, and Machine Learning.

The major websites that provide online data analytics courses include Coursera, Udemy, edX, and SimpliLearn. The majority of the data analytics courses are free and may be started right away.

Along with full-term MBA and Diploma Analytics Courses, IIMs also provide online short-term Data Analytics Courses. The greatest data analytics programs are provided by IIM Calcutta. Depending on the course, the length can range from a few months to a maximum of two years.

2. Can I learn big data analytics without any coding experience?

Big data didn’t exist until analysts and researchers did, which explains why the duties of data analysts are well-defined. Data analysts don’t need advanced coding skills, but they should be conversant with analytics, data visualization, and data management technologies. Does that mean a person who is not proficient in programming cannot be a data scientist or a big data analyst? The quick answer to this question is “no”; big data analysts can now easily study and examine data utilizing a range of open source technologies that don’t require coding experience.

3. How long does it take after doing data analytics courses to become a data analyst?

An individual can become a data analyst in three to six months if they possess all the fundamental abilities. A bachelor’s degree will also take three to four years to finish, while a master’s degree will take two years. Depending on the institute, the duration for students choosing a diploma or postgraduate diploma will range from 8 months to 1 year.

Conclusion

With 96% of firms expecting to hire data analysts, there is no doubt that there is a demand for data analytics courses. By 2022, the data analyst will be one of the most in-demand positions due to the world’s ongoing digitization with estimated employment of around $20 billion. Make the most of this digital wave by enrolling in one of these Data Analytics courses.