Data Analytics Syllabus 2025 – Full Curriculum, Subjects, Topics & PDF

Data Analytics is one of the most sought-after programs in today’s data-driven world. Whether you’re an aspiring data analyst, business intelligence expert, or data engineer, a formal course in Data Analytics builds a strong foundation in statistics, data mining, machine learning, and data visualization. This guide provides you with the complete Data Analytics syllabus, including semester-wise breakdown, marks distribution, syllabus PDF, recommended books, and tips for mastering this field.

Data Analytics involves the extraction of useful insights from raw data using statistical, computational, and visual techniques. It is used in almost every industry — from finance and marketing to healthcare and government — to support decision-making and optimize operations. Whether pursued as a Diploma, B.Sc. in Data Analytics, BCA with Data Analytics, or M.Sc./PG Diploma, the core syllabus remains quite similar, focusing on programming, data handling, business intelligence, and real-world analytics.

Data Analytics Course Overview

Feature	Details
Course Name	Data Analytics
Levels Offered	Diploma, UG (B.Sc/BCA), PG (M.Sc/PG Diploma), Certification
Duration	6 months (certification) to 2 years (PG) or 3 years (UG)
Eligibility	10+2 for UG | Graduation (with math/stats) for PG
Mode	Online / Offline
Assessment Type	Semester-wise Exams, Projects, Viva, Assignments
Key Skills Taught	Python, SQL, R, Statistics, Machine Learning, Data Visualization
Careers After Course	Data Analyst, Business Analyst, Data Scientist, BI Developer

Semester-Wise Data Analytics Syllabus

The syllabus may vary slightly by institution, but below is a standard structure followed by most reputed colleges and universities in India.

Semester 1: Foundations of Data Analytics

Subject	Topics Covered	Marks
Introduction to Data Science	Definition, Data Science Lifecycle, Importance, Use Cases	100
Programming with Python	Variables, Data Structures, Functions, Pandas, NumPy	100
Basics of Statistics	Mean, Median, Mode, Standard Deviation, Probability, Distributions	100
Database Management Systems	SQL, ER Models, Relational Models, Joins, Query Optimization	100
Communication Skills	Writing Reports, Presenting Data, Business Communication	50

Semester 2: Intermediate Data Handling & Analysis

Subject	Topics Covered	Marks
Exploratory Data Analysis	Data Cleaning, Feature Engineering, Outlier Detection, Missing Values	100
Data Visualization	Matplotlib, Seaborn, Plotly, Dashboard Designing	100
Advanced Excel for Analytics	Pivot Tables, Conditional Formatting, Macros, VLOOKUP, Charts	100
Python for Data Analysis	Regex, File Handling, APIs, Web Scraping, NumPy Advanced	100
Industry Project I	Real-time Project using Python and Excel	100

Semester 3: Advanced Analytics & Machine Learning

Subject	Topics Covered	Marks
Machine Learning Basics	Supervised vs Unsupervised, Linear Regression, k-NN, Decision Trees	100
R Programming for Analytics	Data Frames, ggplot2, dplyr, Statistical Analysis using R	100
Time Series Analysis	Trends, Seasonality, Forecasting, ARIMA, Exponential Smoothing	100
Data Warehousing & ETL	OLAP, OLTP, ETL Pipelines, Tools like Talend/SSIS	100
Mini Project	Machine Learning Model Building & Evaluation	100

Semester 4: Big Data, Business Intelligence & Capstone

Subject	Topics Covered	Marks
Big Data & Hadoop	HDFS, MapReduce, Pig, Hive, Spark Basics	100
Business Intelligence Tools	Power BI, Tableau, Data Studio, KPI Dashboard Development	100
Cloud Analytics	AWS S3, Google BigQuery, Azure ML Overview	100
Data Ethics & Governance	Privacy, Security, GDPR, Data Governance Framework	100
Capstone Project	End-to-End Analytics Solution on Real Dataset	150

Data Analytics Syllabus PDF Download

To download the Data Analytics Syllabus PDF for offline use, click the link below: Download Full Data Analytics Syllabus PDF

Recommended Books for Data Analytics

Book Title	Author	Recommended For
Python for Data Analysis	Wes McKinney	Python, Pandas, NumPy
Data Science from Scratch	Joel Grus	Beginners
An Introduction to Statistical Learning	Gareth James et al.	Machine Learning
R for Data Science	Hadley Wickham & Garrett Grolemund	R Programming
Storytelling with Data	Cole Nussbaumer Knaflic	Data Visualization
Big Data: Principles and Paradigms	Rajkumar Buyya	Big Data and Hadoop
Data Smart	John Foreman	Excel & Business Analytics

How to Prepare for Data Analytics Course

• Master Programming: Focus on Python and R — they are essential.
• Practice SQL: Real-world data retrieval is incomplete without SQL mastery.
• Hands-on Projects: Work on real datasets using Kaggle or UCI repository.
• Explore Visualization Tools: Gain proficiency in Power BI, Tableau, or Google Data Studio.
• Revise Statistics Often: Stats is the backbone of all analytics tasks.
• Join Communities: Reddit, StackOverflow, Kaggle Discussions, and LinkedIn groups are great for exposure.
• Online Certifications: Complement your course with Coursera/edX/Udemy analytics certifications.

Specializations in Data Analytics

Specialization	Description
Business Analytics	Use data to drive business decisions, KPIs, ROI measurement
Data Engineering	Build and maintain data pipelines, ETL processes, and storage systems
Financial Analytics	Apply data analysis to financial models, forecasting, risk assessment
Marketing Analytics	Use data to understand customer behavior and optimize campaigns
Healthcare Analytics	Analyze patient data, diagnosis trends, hospital operations
AI & Predictive Analytics	Use ML for predictive modeling, classification, and real-time decision systems

Career Opportunities After Data Analytics Course

Role	Average Salary (INR)
Data Analyst	₹4–6 LPA
Business Analyst	₹5–8 LPA
Data Scientist	₹7–12 LPA
Data Engineer	₹6–10 LPA
BI Developer	₹5–9 LPA
Machine Learning Engineer	₹8–14 LPA

Conclusion

The Data Analytics syllabus is designed to offer a balanced mix of theory, practical application, and project-based learning. With an increasing demand for data-driven decision-making across industries, pursuing a Data Analytics program gives you a competitive edge in the job market. From Python programming to advanced machine learning models, and from SQL queries to data visualization dashboards, the syllabus covers everything required to transform raw data into meaningful business insights. Download the syllabus PDF, grab the recommended books, and start building your analytics career today!