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Module 1: Introduction to Data Science & Analytics
What is Data Science & Data Analytics? Understanding the Data Ecosystem Career Scope & Industry Applications Overview of Data Science Tools & Technologies Setting up Jupyter Notebook and GitHub
4
Module 2: Python for Data Science
Python Basics: Variables, Loops, Functions, and Lists Working with Libraries – NumPy, Pandas Data Structures & Operations File Handling and Data Import/Export Hands-on Practice: Python Data Projects
3
Module 3: Data Analysis & Data Cleaning
Data Wrangling Techniques Handling Missing Values & Outliers Data Transformation & Normalization Exploratory Data Analysis (EDA) Real-World Data Cleaning Project
2
- 3.1
  Numpy – arrays and arrays operations, Indexing and Slicing, Aggregate functions, Saving & Loading Data.
- 3.2
  Pandas – Importing and Exporting Data, Inspection & Exploration, Data Transformation
Module 4: Data Visualization & Reporting
Data Visualization with Matplotlib & Seaborn Dashboard Creation in Power BI & Tableau Visual Storytelling with Data Business Insights from Data Visualization Mini Project: Sales Dashboard & KPI Analysis
4
Module 5: SQL for Data Analytics
SQL Basics & Query Writing Joins, Subqueries & Aggregate Functions Database Management with MySQL Connecting SQL with Python Real-time Project: Company Sales Database
3
Module 6: Statistics and Probability
3

OffOn

Curriculum

Seaborn: Heatmap, Boxplot, Pairplot, Countplot

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