Python for Data Science Syllabus

Part I: Introduction

• Introduction to Specialization
• Data Science & Jupyter Notebook
• Python Basics & Functions
• Types and Sequences
• More on Strings
• Reading and Writing CSV files
• Dates and Times
• Advanced Python Objects, map()
• Lambda and List Comprehensions
• NumPy Basics

Part II: Data Cleansing and Processing with Pandas

• The Series Data Structure
• Querying a Series
• Data Frame Structure
• Indexing, Loading, Querying
• Missing Values
• Merging Data Frames
• Group by, Scales, Pivot Tables
• Pandas Idioms

Part III: Statistical Techniques

• Distributions
• Hypothesis Testing

Data Visualization and Representation

Part I: Basic Plots

• Matplotlib Architecture
• Basic Plotting
• Scatterplots
• Line Plots
• Bar Charts

Part II: Visualization Techniques

• Subplots
• Histograms
• Box Plots
• Plotting with Pandas

Machine Learning

Part I: Fundamentals

• Introduction to ML
• Scikit-Learn Basics
• K-Nearest Neighbours

Part II: Supervised Learning

• Overfitting and Underfitting
• Linear Regression
• Logistic Regression
• SVMs
• Cross-Validation
• Decision Trees

Part III: Model Selection and Evaluation

• Confusion Matrix
• Evaluation Metrics
• Precision-recall, ROC Curves
• Optimizing for Metrics

Part IV: Advanced Learning (Optional)

• Naive Bayes
• Random Forests
• Gradient Boosted Trees
• Neural Networks
• Data Leakage
• Clustering

Text Classification (Optional)

• Text Mining Basics
• Regex & Text Handling
• NLTK for NLP
• Text Classification
• Feature Extraction
• Naive Bayes, SVM
• Sentiment Analysis Case Study
• YOLO Case Study
• Automation Case Study