Data Science & Analytics (Data Science)
In the digital world that we are living in today, data is what helps you make solid decisions. The rise in data analytics jobs and the promising job options for freshers makes it a very exciting course option. Taking admission in a Data Analytics online course is a step towards understanding the essence of this domain.
Understanding Data Analytics
Data analytics is all about analyzing raw data to come up with conclusions. It's a subset of business analytics. Tools like NVivo and KNIME are commonly used for managing and analyzing data.
How to become a Data Analyst?
To become a data analyst, you require a combination of education, skills and practical experience. Here is a basic guide on how can you become a data analyst:
This table provides a clear and organized pathway for you. Following these steps, continuously learning and adapting to new trends and technologies in the field will help you successfully have a career in data analytics.
| Category | Action Items | Details |
|---|---|---|
| Educational Background | A. Obtain a Bachelor’s Degree | - Opt for degrees in Statistics, Mathematics, Information Technology, or Business. |
| B. Take Relevant Courses | - Focus on courses in statistics, mathematics, computer science, and business. | |
| Technical Skill Development | A. Learn Programming Languages | - Become proficient in Python or R. |
| B. Master Database Management | - Learn SQL for effective database management. | |
| C. Learn Data Visualization Tools | - Familiarize with tools like Tableau or PowerBI. | |
| Knowledge in Data Analytics | A. Enroll in Online Courses and Certifications | - Choose platforms like Coursera, edX, or Udemy. |
| B. Understand Data Processing | - Learn about data cleaning, transformation, and processing. | |
| Practical Experience | A. Work on Real Projects | - Engage in real-world data analytics projects. |
| B. Create a Portfolio | - Showcase your data analytics projects and skills. | |
| Job Application Process | A. Prepare Resume and Cover Letter | - Highlight skills, education, and experience. |
| B. Apply for Entry-Level Positions or Internships | - Target relevant data analyst positions. | |
| Continuous Learning & Networking | A. Pursue Advanced Degrees or Certifications | - Consider further education in data analytics or related fields. |
| B. Join Data Analytics Forums and Groups | - Network with professionals and stay updated on industry trends. | |
| Career Advancement | A. Gain Experience in the Field | - Work in various roles to accumulate experience. |
| B. Continuously Update Skills | - Learn about the latest tools, technologies, and best practices in data analytics. |
Data Analytics Course: Candidates who are obsessed with data might find the data analytics course interesting and can also make a career out of it. Data analytics deals with analysing raw data to find trends and answer questions. A data analyst needs to deal with data throughout the Data Analysis pipeline. This means this course will help candidates learn how to work with data in various ways to reach a conclusion.
Candidates can remain assured that the job of a data analyst is quite important nowadays, This has come to into forefront more in today’s scenario as data has become a significant part of any industry to make strategic and informed decisions.
Talking about what course to pursue to become data analytics, then candidates can pursue BTech CSE, MSc Computer Science with data analytics and data science as major specializations at the UG level. At the PG level, candidates can take MSc in Data Analytics
Various online courses are also available these days. Candidates can learn all about data analytics courses online offered by Coursera, CareerFoundry, Udemy, etc.
Types of Data Analytics Courses
Data analytics UG and PG courses are of the following types:
UG: UG courses in Data Analysis can be pursued after 10+2. Check the details of the UG Data Analysis courses below:
|
Duration |
3 to 4 Years |
|
Eligibility |
10+2 from a recognised board with a minimum aggregate score. |
|
Admission Process |
Entrance Exams/Merit-based |
|
Fees |
INR 3,00,000 - 10,00,000 |
|
What After Bachelor Course? |
Data Scientist, Data Architect, Data Engineer, Data Analyst, Statistician |
|
Average Salary |
INR 4 - 6 lakhs |
PG: PG courses in Data Analysis can be pursued after completion of a UG course in Data Analysis. Check details of the PG Data Analysis courses below:
|
Duration |
2 Years |
|
Eligibility |
Bachelor of Data Analysis courses from a recognised university with a minimum aggregate score. |
|
Admission Process |
Entrance Exams/Merit-based |
|
Fees |
INR 30,000 - 6,00,000 |
|
What After Master Course? |
Data Scientist, Data Architect, Data Engineer, Data Analyst, Statistician |
|
Average Salary |
INR 4 - 6 lakhs |
Certificate courses: certificate courses can be pursued online or offline. These courses are good for a career boost.
|
Duration |
Varies |
|
Eligibility |
Any |
|
Fees |
INR 30,000 - 6,00,000 |
|
What After Master Course? |
Data Scientist, Data Architect, Data Engineer, Data Analyst, Statistician |
|
Average Salary |
INR 4 - 6 lakhs |
Popular Data Analysis Courses
Check the popular UG, PG and diploma courses in Data Analysis below:
|
UG |
PG |
Diploma |
|---|---|---|
|
BSc (Hons) Data Science & Analytics |
MSc in Computer Science with Specialization in Data Analytics |
PG Diploma in Data Science & Analytics |
|
BTech CSE (Specialization in Data Analytics) |
MSc Data Science and Analytics |
Diploma in Data Analytics with Python (Online) |
|
BTech CSE (Specialization in Big Data Analytics) |
MSc Data Analytics Program |
Diploma in Data Analytics |
|
BTech CSE (Specialization in Big Data Analytics in association with IBM) |
MBA Data Science and Analytics |
Post Graduate Diploma in Data Analytics |
|
BTech Data Science & Data Analytics |
MSc Data Analytics |
Post Graduate Diploma in Data Analytics in Economics |
|
UG Program in Digital Marketing and Data Analytics |
|
Data Analytics diploma programs are accessible at both the undergraduate and graduate levels. Students can study BTech Data Science and Data Analytics at the undergraduate level to get a degree in Data Analytics.
Popular PG programs include MBA Data Science and Analytics, MSc Data Science and Analytics, and others. .
Syllabus – Important Facts for Data Analytics Course
- Data Analytics course: The conditions set out by each college for admission to this course are different based on the mode of study, and the type of study. A Candidate interested in Data Analytics must meet the requirements set out by the universities. The following are the most typical requirements for admission to Big Data Analytics colleges in India:
- Candidates must have completed Class 12 in Science or Commerce with Mathematics to be considered for undergraduate studies.
- For admission to postgraduate studies, students must have a bachelor's degree or equivalent in Engineering, Mathematics, Computer Science, Finance, or Economics.
- Bachelor in Data Science The Bachelor of Science in Data Science is a three-year full-time program. It involves subjects like Computer Science, Business Analytics, and Artificial Intelligence. Data Science is the study that entails the application of statistics, big data analytics, machine learning, and other related concepts to comprehend an issue or occurrence in relation to a set of factual data.
- Scope in India: Data analytics graduates are in demand in practically every industry. Data Analytics is an excellent job choice for students who want to make a huge shift in their professions and earn a lot of money. Candidates with a thorough understanding of the sector can also look into other career options.
- Masters in Data Analytics: It is the best program offered and taken by students all over India. Masters in Data Analytics is a six-trimester interdisciplinary postgraduate degree program in data analytics. This program is for working professionals and recent graduates interested in pursuing a career in the lucrative field of data analytics.
Data Analytics Courses in IITs and IIMs
Executive PG Program from IIT Bangalore: IIITB offers an Executive PGP in Data Science with a specialised area. IIT B provides five specialisations, as well as industry coaching, targeted career assistance, and the opportunity to master 14+ programming tools and languages. The duration is for a period of 12 months.
- General examination pattern: Candidates who want to study Data Analytics at an Indian institution must pass an admission exam. For the PG specialisation in Data Analytics, students can apply by taking several entrance exams like GATE, CAT, or JEE Main exams.
List of Core Subjects in Data Analytics
Data Analytics course consists of the following core subjects:
|
Subject title |
Subject details |
|---|---|
|
Machine Learning |
It is a branch of computer science that enables computers to learn without being explicitly programmed. The subject involves:
|
|
Data Visualisations |
The subject helps in understanding the graphical depiction of information and data is known as data visualisation. The subject involves: Data visualisation tools to examine and comprehend trends, outliers, and patterns in data by employing visual components like charts, graphs, and maps.
|
|
Python Programming, |
This course will be of great interest to anybody who wants to fully understand the fundamental components of computer programming using the Python programming language.
The subject involves:
|
|
Operating Systems
|
The subject helps students to get familiar with the identification of operating systems. The students will learn about the major functionalities of operating systems, Resource management, Process management, storage, and memory. |
Typical Elective Subjects for Data Analytics
|
Subject title |
Subject details |
|---|---|
|
Business Intelligence |
Students learn about data mining, process analysis, and performance benchmarking. Subject Involves:
|
|
Internet of Things |
This course covers the basics of the Internet of Things and teaches students how to program IoT boards. Students learn to program a variety of IoT development programs. |
Detailed Syllabus for Data Analytics
The disciplines taught as part of the Data Analytics program may differ between colleges. Students can review the table below for a list of some of the most popular subjects covered in the Data Analytics Course.
|
Semester |
Core/Elective |
Subject Title |
Subject Details |
|---|---|---|---|
|
1 |
|
Principles of Data Analytics |
To lay a solid basis for data analytics and associated application areas, as well as to comprehend the underlying key principles and new technologies in the field. The subject involves
|
|
|
Statistical Methods using R |
This course involves how to use R to display and analyse data, as well as how to convey statistical results correctly. |
|
|
|
Python for Data Analytics |
The goal of this course is to offer a thorough understanding of the Python programming concepts needed for Data Analytics. |
|
|
2
|
|
Mathematical Foundation for Data Analytics |
The theory of Data Science relies heavily on linear algebra. The goal of this course is to introduce the fundamental concepts of vector spaces, linear algebra, and the usage of linear algebra in Data Science applications. |
|
|
Database Technologies |
The primary goal of this subject is to provide students with a foundational understanding of database principles as well as practical experience with them.
The subject involves
|
|
|
|
Data Mining |
The subject involves preprocessing and analysing data, selecting appropriate models and algorithms for specific applications, and fostering research interest in data mining advancements.
|
|
|
3 |
|
Artificial Intelligence |
This subject will; help gain a better understanding of the issues involved in defining and simulating perception, identify problems where AI is needed and the various methods available, compare and contrast different AI techniques, define and explain learning algorithms, and gain additional experience in the analysis and evaluation of complex systems. |
|
|
Regression Modelling |
Students will learn how to evaluate the relationship between variables in a data set and a continuous response variable in this course. Students will learn how to use the R software to fit basic and multiple linear regression models. |
|
|
|
Big Data Analytics |
The goal of the course is to provide students with an understanding of how Big Data is expanding in real-time applications and how it is manipulated utilising emerging technologies. The subject involves:
|
|
|
4 |
|
Machine Learning |
The subject involves dealing with various Modelling Processes. Understanding of Training Model, validating model, predicting New observations, supervised learning Algorithms. |
|
|
Natural Language Processing |
The subject involves: Application of NLP, Syntax processing, Semantic Analysis, Pragmatic and Discourse Processing: Analysis, Perception |
|
|
5 |
|
Data Visualization |
The subject helps in the understanding the graphical depiction of information and data is known as data visualisation. The subject involves: Data visualisation tools to examine and comprehend trends, outliers, and patterns in data by employing visual components like charts, graphs, and maps.
|
|
|
Neural Networks and Deep Learning |
The subject familiarises the students with the neural networks and deep learning as fundamental concepts. The subject involves
|
|
|
6 |
|
Project Work |
The project will teach you about deep learning's capabilities, problems, and repercussions, as well as equip you to contribute to the creation of cutting-edge technology. It paves the road for the students to get the information and abilities they will need to apply data analytics in their work, to advance their technical career, and take the first step into the data analytics world. |
The Data Science & Analytics course fees in India vary depending on the course level, institution, specialization, mode of study, and facilities offered. Since Data Science & Analytics is available as undergraduate, postgraduate, diploma, and certification programmes, the total cost can differ considerably.
1. Data Science & Analytics Fees in India
| Course / Institution Type | Approx. Fees |
|---|---|
| Government/Public Institutions | ₹30,000 – ₹3 lakh |
| State Universities | ₹50,000 – ₹4 lakh |
| Private Colleges/Universities | ₹2 lakh – ₹8 lakh |
| Reputed Private Universities | ₹3 lakh – ₹10 lakh+ |
| Premium Institutions | ₹5 lakh – ₹15 lakh+ |
| B.Sc. Data Science | ₹1.5 lakh – ₹6 lakh |
| B.Tech. Data Science | ₹2 lakh – ₹12 lakh+ |
| BCA/BBA with Data Analytics | ₹1 lakh – ₹6 lakh |
| MSc Data Science/Analytics | ₹1 lakh – ₹8 lakh+ |
| PG Diploma in Data Science | ₹50,000 – ₹5 lakh+ |
| Online/Distance Programmes | ₹30,000 – ₹4 lakh+ |
These are broad estimates for India, and actual fees can vary significantly between institutions.
2. Fees According to Specialization
Data Science & Analytics programmes may offer different areas of specialization, such as:
- Data Science: ₹1 lakh – ₹10 lakh+
- Business Analytics: ₹50,000 – ₹8 lakh+
- Data Analytics: ₹50,000 – ₹7 lakh+
- Artificial Intelligence & Machine Learning: ₹1 lakh – ₹10 lakh+
- Big Data Analytics: ₹1 lakh – ₹8 lakh+
- Predictive Analytics: ₹75,000 – ₹7 lakh+
- Data Engineering: ₹1 lakh – ₹10 lakh+
3. Additional Expenses
Apart from tuition fees, students may also need to consider:
- Hostel and accommodation
- Food and mess charges
- Examination and registration fees
- Books and study materials
- Laptop or computer
- Software and cloud-computing resources
- Data science tools and certifications
- Project expenses
- Workshops and training
- Internship-related expenses
For students staying in a hostel, accommodation and living expenses can broadly add ₹50,000–₹2 lakh+ per year, depending on the institution and city.
4. Factors Affecting Data Science & Analytics Fees
The overall course cost depends on several factors:
Course Level: A bachelor's degree, master's degree, postgraduate diploma, and short-term certification can have very different fee structures.
Type of Institution: Government and public institutions generally have lower tuition fees, while private universities may charge considerably more.
Specialization: Programmes covering advanced areas such as AI, machine learning, big data, and data engineering may have higher fees.
Infrastructure: Access to high-performance computing, cloud platforms, analytics software, laboratories, and industry projects can influence the cost.
Placement and Industry Exposure: Institutions offering strong internship, industry-training, and placement support may have higher fees.
5. Scholarships and Financial Assistance
Students may be able to reduce their education costs through:
- Merit-based scholarships
- Government scholarships
- State-level scholarships
- University scholarships
- Need-based financial assistance
- Education loans
Eligibility and scholarship benefits depend on the institution and applicable schemes.
Data Science & Analytics offers career opportunities in data analysis, business intelligence, artificial intelligence, machine learning, finance, marketing, operations, technology, healthcare, and consulting. Graduates can work with companies that use data to understand customers, improve processes, reduce costs, and make better business decisions.
1. Major Job Roles
| Job Role | Main Responsibilities |
|---|---|
| Data Analyst | Collect, clean, analyze, and interpret data to support business decisions |
| Data Scientist | Develop statistical and machine learning models to solve complex problems |
| Business Analyst | Analyze business requirements, processes, and data to improve performance |
| Business Intelligence Analyst | Create reports, dashboards, and insights using business data |
| Data Engineer | Build and maintain systems and pipelines used to collect and process data |
| Machine Learning Engineer | Develop, train, and deploy machine learning models |
| AI Engineer | Build AI-based applications and intelligent systems |
| Analytics Consultant | Help organizations use data and analytics to solve business problems |
| Financial Data Analyst | Analyze financial and market data for business and investment decisions |
| Marketing Analyst | Study customer behaviour, campaigns, and marketing performance |
| Product Analyst | Use data to improve products, features, and user experiences |
| Risk Analyst | Identify and evaluate financial, operational, or business risks |
| Operations Analyst | Analyze business operations and identify opportunities for improvement |
| Data Visualization Specialist | Convert complex data into understandable charts and dashboards |
| Research Analyst | Conduct data-based research and prepare analytical reports |
2. Data Analyst
A Data Analyst works with structured and unstructured data to identify trends and generate useful insights.
Typical responsibilities include:
- Collecting and cleaning data
- Performing statistical analysis
- Preparing reports
- Creating dashboards
- Identifying trends and patterns
- Supporting business decisions
- Working with Excel, SQL, Python, R, Power BI, or Tableau
3. Data Scientist
Data Scientists use advanced statistical methods and machine learning techniques to solve complex business and technical problems.
They may work on:
- Predictive modelling
- Machine learning
- Statistical analysis
- Customer behaviour prediction
- Forecasting
- Recommendation systems
- Data-driven decision-making
4. Business Analyst
Business Analysts connect business requirements with data and technology. They study business processes and use data to identify problems and opportunities.
They can work on:
- Business performance analysis
- Process improvement
- Requirement gathering
- Market analysis
- Reporting
- Decision support
- Business strategy
5. Business Intelligence and Reporting
Business Intelligence professionals transform business data into useful dashboards and reports.
They commonly work with:
- Power BI
- Tableau
- SQL
- Excel
- Data warehouses
- Business reporting systems
Possible roles include BI Analyst, BI Developer, Reporting Analyst, and Data Visualization Specialist.
6. Artificial Intelligence and Machine Learning
Students with strong programming and mathematical skills can move into AI and Machine Learning careers.
Possible roles include:
- Machine Learning Engineer
- AI Engineer
- AI Data Scientist
- Machine Learning Researcher
- Computer Vision Engineer
- Natural Language Processing Engineer
These professionals develop systems that can learn from data and make predictions or automate tasks.
7. Data Engineering
Data Engineers build the infrastructure required to collect, store, process, and deliver data.
Their work can involve:
- Data pipelines
- Databases
- Cloud platforms
- Data warehouses
- ETL/ELT processes
- Big data technologies
- Data integration
8. Finance and Risk Analytics
The finance sector uses data extensively for forecasting, risk management, customer analysis, and fraud detection.
Career opportunities include:
- Financial Data Analyst
- Risk Analyst
- Fraud Analyst
- Investment Analytics Professional
- Credit Risk Analyst
- Quantitative Analyst
9. Marketing and Customer Analytics
Data professionals can help companies understand customers and measure marketing performance.
They may analyze:
- Customer behaviour
- Sales trends
- Advertising campaigns
- Website traffic
- Customer segmentation
- Customer retention
- Product demand
Common roles include Marketing Analyst, Customer Analytics Analyst, Digital Analytics Specialist, and CRM Analyst.
10. Product and Operations Analytics
Data Analytics is also widely used for improving products and business operations.
Possible roles include:
- Product Analyst
- Operations Analyst
- Supply Chain Analyst
- Pricing Analyst
- Revenue Analyst
- Business Operations Analyst
Professionals in these roles use data to improve efficiency, reduce costs, understand demand, and support strategic decisions.
11. Industries Hiring Data Science Professionals
Data Science & Analytics graduates can work across many industries:
- Information Technology
- Banking and Financial Services
- E-commerce
- Healthcare
- Telecommunications
- Manufacturing
- Retail
- Consulting
- Insurance
- Automotive
- Logistics and Supply Chain
- Media and Entertainment
- Education
- Government and public-sector organizations
12. Skills That Improve Career Opportunities
Important technical and professional skills include:
- Statistics and probability
- Python
- R
- SQL
- Excel
- Power BI
- Tableau
- Machine Learning
- Data Visualization
- Database Management
- Cloud Computing
- Big Data Technologies
- Business Communication
- Problem-solving
- Critical Thinking
13. Career Growth
A typical career path may look like:
Junior Data Analyst → Data Analyst → Senior Data Analyst → Analytics Manager → Data/Analytics Lead
For a technical path:
Junior Data Scientist → Data Scientist → Senior Data Scientist → Lead Data Scientist → Data Science Manager
Experienced professionals can also move into consulting, product management, research, analytics leadership, or specialized AI/ML roles.
14. Higher Studies After Data Science & Analytics
Students can pursue further education in areas such as:
- MSc in Data Science
- MSc in Data Analytics
- M.Tech in Data Science
- MBA in Business Analytics
- Artificial Intelligence and Machine Learning
- Big Data Analytics
- Statistics
- Data Engineering
- PhD in Data Science, AI, Statistics, or related fields
FAQ's
Are you interested in this Course?
Top Colleges
Arkay College of Engineering and Technology
Nizamabad, Telangana
East Point College of Medical Sciences and Research Center, Bangalore
Bengaluru, Karnataka
Jamia Millia Islamia New Delhi
New Delhi, Delhi
Dwarka Dass Memorial Sai Law College, Hamirpur
Hamirpur, Himachal Pradesh
JIST Jorhat
Jorhat, Assam
