Course Syllabus · Data Analytics

Uncodemy Data Analytics Course Syllabus Explained in Detail

SECTION 01Course Overview

Uncodemy's Data Analytics course is designed to be 100% practical and project-based, preparing you for real-world data analysis roles. The curriculum is progressive — each module builds on the previous one, making it suitable for beginners with no prior coding experience.

• Duration: 2-6 months (flexible learning)
• Fee: Starting from ₹15,500
• Mode: Classroom & Online batches available
• Schedule: Weekday & weekend batches
• Tools Covered: 14+ tools including Python, SQL, Power BI, Tableau, and Generative AI tools

SECTION 02Module 1: Foundation & Core Tools

Python for Data Analytics

Python is the primary language taught. You'll learn:

• Variables, data types, and operators
• Loops, functions, and object-oriented programming
• Essential libraries: NumPy (numerical operations) and Pandas (data manipulation)
• Data cleaning and preparation techniques

SQL for Data Management

SQL is covered extensively for database querying:

• Writing complex queries (JOINs, subqueries, window functions)
• Database design and optimization
• Data extraction and transformation

Advanced Excel & Power Query

Master Excel for data analysis including:

• Power Query for data transformation
• Advanced formulas and functions
• Pivot tables and data modeling

SECTION 03Module 2: Statistics & Data Visualization

Statistical Analysis

Understanding statistics is crucial for data analytics. The syllabus covers:

• Descriptive statistics (mean, median, mode, standard deviation)
• Probability distributions and hypothesis testing
• Correlation and regression analysis
• Statistical methods applied to business problems

Data Visualization Tools

Tableau and Power BI are core visualization tools taught:

• Creating interactive dashboards
• Data storytelling and visualization best practices
• DAX (Data Analysis Expressions) for Power BI
• Real-world dashboard projects

SECTION 04Module 3: Machine Learning & Predictive Analytics

Machine Learning Fundamentals

The course introduces machine learning algorithms and their business applications:

• Supervised learning: Regression and Classification
• Unsupervised learning: Clustering (k-means)
• Model evaluation and validation techniques
• Scikit-learn for building ML models

Predictive Analytics

You'll learn to:

• Build predictive models for business forecasting
• Apply time-series analysis
• Create revenue forecasting models using Power BI and Python

SECTION 05Module 4: Generative AI & Emerging Technologies

The course includes training on AI tools relevant to data analytics:

• ChatGPT for data analysis assistance
• GitHub Copilot for coding support
• Power BI Copilot for dashboard creation
• Claude for document and data analysis
• Big Data concepts (Hadoop, Spark)
• Cloud computing platforms
• Understanding data pipelines

SECTION 06Module 5: R Programming

R is included as an additional tool for statistical analysis:

• R programming fundamentals
• Data manipulation with R
• Statistical modeling in R

SECTION 07Tools Covered

You work hands-on with 14 tools across the curriculum:

SECTION 08Projects & Portfolio

The course emphasizes project-based learning throughout:

Capstone Project

A final capstone project integrates all skills learned. Students work on real datasets and business problems, creating a portfolio piece to showcase to employers.

SECTION 09Career Support

The course includes dedicated career support:

Resume & Interview Preparation

• Resume building and optimization
• Mock interviews with industry professionals
• Behavioral training for IT roles

Placement Assistance

• 100% job placement support offered
• Access to 14+ hiring partners
• Networking opportunities with industry professionals
• Ongoing career support beyond graduation

SECTION 10Course Duration & Fees

SECTION 11Test yourself — Syllabus Quiz

SECTION 12Frequently asked questions

SECTION 13Related reads