Data Analytics Syllabus 2025 – Full Curriculum, Subjects, Topics & PDF
Explore the complete Data Analytics syllabus 2025 with semester-wise subjects, topic descriptions, marks distribution, tools covered, specialization options, recommended books, preparation tips, and a free downloadable syllabus 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.
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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!
