Python for Data Science
Learn Python for Data Science from basics to practical data analysis using Pandas, NumPy, visualization, and exploratory data analysis (EDA).
Course Overview
Course Modules Module 1: Python Programming Basics Variables & Data Types Control Flow Functions & Modules Lists, Dicts & Tuples File Handling Error Handling Module 2: Data Manipulation with Pandas DataFrames & Series Data Cleaning Merging & Joining GroupBy Operations Time Series Data Missing Value Treatment Module 3: Scientific Computing with NumPy Arrays & Matrices Mathematical Operations Broadcasting Linear Algebra Random Number Generation Performance Optimization Module 4: Data Visualization Matplotlib Fundamentals Seaborn Statistical Plots Plotly Interactive Charts Chart Selection Guide Storytelling with Data Dashboard Basics Module 5: Exploratory Data Analysis EDA Process Descriptive Statistics Correlation Analysis Outlier Detection Feature Distribution Insights Generation Module 6: Capstone Project Dataset Selection Data Cleaning EDA & Visualization Statistical Analysis Key Findings Report Presentation Capstone Project Conduct a complete EDA project on a real-world dataset with visualizations, statistics, and a written report.
Learning Outcomes
Write Python code for data manipulation and analysis.
Use Pandas and NumPy for scientific computing.
Create compelling data visualizations.
Conduct thorough exploratory data analysis.
Extract meaningful insights from complex datasets.
Prerequisites
Participants should have: Basic computer literacy High school level mathematics Analytical mindset and curiosity
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