Course Overview
Data is the currency of the modern enterprise. Companies across the globe collect petabytes of data, but need specialized professionals to extract patterns, build insights, and guide commercial decisions. This offline Data Science course at 3Stack Academy transforms you into a clean, system-level data wrangler.
Rather than lecturing dry math theory, we focus on actual datasets: active client briefings, real consumer transactions, and server log directories. You will build high-speed arrays in NumPy, model relational tables using Pandas, construct gorgeous interactive scatter/line/bar plots in Matplotlib & Seaborn, and learn statistical probability modeling (Hypothesis testing, distributions) to support analytical claims.
Course Syllabus
Module 01: Data Science Kickstart — Step into the World of Data & Analytics
- What is Data Science? • Data Science Life Cycle • Real-World Applications
- Roles in Data Science • Tools & Technologies • Industry Use Cases
- Setting Up Anaconda • Working with Jupyter Notebook
Module 02: Python for Data Science — Strengthen Your Programming Foundation
- Python Refresher • Variables & Data Types • Operators • Conditional Statements
- Loops • Functions • Lists, Tuples, Sets & Dictionaries
- File Handling • Exception Handling
Module 03: NumPy for Numerical Computing — Work Efficiently with Numbers and Multidimensional Data
- Introduction to NumPy • Creating Arrays • Array Indexing & Slicing
- Array Operations • Mathematical Functions • Statistical Operations
- Random Module • Reshaping Arrays • Broadcasting
Module 04: Pandas for Data Analysis — Transform Raw Data into Structured Information
- Introduction to Pandas • Series • DataFrames • Reading CSV & Excel Files
- Data Selection & Filtering • Sorting Data • Handling Missing Values
- Grouping & Aggregation • Merging & Joining DataFrames
Module 05: Data Visualization — Turn Complex Data into Powerful Visual Stories
- Introduction to Data Visualization • Matplotlib • Line Charts • Bar Charts • Pie Charts
- Histograms • Scatter Plots • Box Plots
- Introduction to Seaborn • Heatmaps • Choosing the Right Visualization
Module 06: Data Cleaning & Preprocessing — Prepare Raw Data for Accurate Analysis
- Understanding Data Quality • Identifying Duplicate Records • Handling Missing Data
- Data Transformation • Cleaning Inconsistent Data
- Categorical Data Encoding • Feature Scaling • Preparing Analysis-Ready Datasets
Module 07: Exploratory Data Analysis (EDA) — Discover Patterns, Trends and Insights Hidden in Data
- Descriptive Statistics • Data Distribution • Correlation Analysis • Detecting Outliers
- Feature Relationships • Univariate Analysis • Bivariate Analysis • Multivariate Analysis
- Identifying Trends & Patterns • Generating Business Insights
Module 08: SQL for Data Science — Extract and Analyze Data Directly from Databases
- Database Fundamentals • Tables & Relationships • SELECT Queries • Filtering Data
- Aggregate Functions • GROUP BY • HAVING • SQL Joins • Subqueries
- Data Analysis Using SQL • Connecting Python with Databases
Module 09: Machine Learning Fundamentals — Take Your First Step into Predictive Analytics & AI
- Introduction to Machine Learning • Types of Machine Learning • Supervised vs Unsupervised Learning
- Features & Target Variables • Training and Testing Data • Introduction to Scikit-learn
- Building Basic ML Models • Model Prediction • Model Evaluation • Understanding Model Performance