Python for Data Science Certification

Module 1: Introduction to Python for Data Science

• Python in data science
• Data science workflow
• Python ecosystem
• Development environments
• Jupyter Notebook
• Common applications

Module 2: Python Programming Fundamentals

• Variables
• Data types
• Operators
• Expressions
• Input and output
• Python syntax

Module 3: Python Data Structures

• Lists
• Tuples
• Dictionaries
• Sets
• Indexing
• Slicing

Module 4: Control Flow

• Conditional statements
• For loops
• While loops
• Nested conditions
• Loop control
• Comprehensions

Module 5: Functions & Modules

• Defining functions
• Parameters
• Return values
• Lambda functions
• Modules
• Packages

Module 6: File & Data Handling

• Text files
• CSV files
• JSON data
• Reading files
• Writing files
• Exception handling

Module 7: NumPy Fundamentals

• NumPy arrays
• Array creation
• Indexing and slicing
• Array operations
• Data types
• Mathematical functions

Module 8: NumPy for Numerical Analysis

• Vectorization
• Broadcasting
• Aggregations
• Statistical operations
• Reshaping
• Matrix operations

Module 9: Pandas Fundamentals

• Series
• DataFrames
• Creating DataFrames
• Importing datasets
• Selecting data
• Filtering data

Module 10: Data Manipulation with Pandas

• Sorting
• Grouping
• Aggregation
• Merging
• Joining
• Pivot tables

Module 11: Data Cleaning & Preparation

• Missing values
• Duplicate records
• Data type conversion
• Outlier handling
• String cleaning
• Data validation

Module 12: Exploratory Data Analysis

• Dataset profiling
• Descriptive statistics
• Distribution analysis
• Correlation
• Pattern identification
• Insight generation

Module 13: Data Visualization with Matplotlib

• Plotting fundamentals
• Line charts
• Bar charts
• Histograms
• Scatter plots
• Chart customization

Module 14: Advanced Data Visualization

• Box plots
• Heatmaps
• Distribution plots
• Multivariate visualization
• Visual comparison
• Data storytelling

Module 15: Statistics with Python

• Descriptive statistics
• Probability concepts
• Statistical distributions
• Sampling
• Correlation
• Hypothesis testing concepts

Module 16: Feature Engineering

• Feature creation
• Categorical encoding
• Scaling
• Normalization
• Feature selection
• Data transformation

Module 17: Machine Learning Fundamentals

• Machine learning concepts
• Supervised learning
• Unsupervised learning
• Training and testing
• Features and targets
• ML workflow

Module 18: Regression with Python

• Linear regression
• Multiple regression
• Model training
• Prediction
• Regression metrics
• Model interpretation

Module 19: Classification with Python

• Logistic regression
• Decision trees
• Random forests
• K-nearest neighbors
• Classification metrics
• Model comparison

Module 20: Clustering & Unsupervised Learning

• Clustering concepts
• K-Means
• Hierarchical clustering
• Cluster evaluation
• PCA concepts
• Segmentation applications

Module 21: Model Evaluation & Optimization

• Accuracy
• Precision and recall
• F1 score
• Confusion matrix
• Cross-validation
• Hyperparameter tuning

Module 22: Practical Python Data Science Project

• Dataset preparation
• Exploratory analysis
• Data visualization
• Feature engineering
• Model development
• Results presentation