Learn Python basics with beginner-friendly tutorials, examples, and exercises. Master Python programming concepts like print function, variables, comments, indentation and more. Perfect for students and professionals starting their Python journey.
Weeks 1-4: Basic Math Concepts & Python Basics
Week 1: Introduction to Python & Arithmetic Operations
Week 2: Operators and Variables
Lecture 4: Operators: Logical Operators, Identity Operators, Membership Operators Lecture 5: Variables Lecture 6: Data Types
Week 3: Control Flow
Lecture 7: Conditional Statements (if, else, elif) Lecture 8: Conditional Statements (if, else, elif) Lecture 9: Looping Statements (for loop, while loop) Lecture 9: Looping Statements (for loop, while loop)
Week 4: Functions
Lecture 11: Functions: Defining and Calling Functions Lecture 12: Function Parameters and Return Values
Lecture 3: Python math functions (abs, round, pow, etc.)
Week 3: Linear Equations and Loops
Lecture 1: Introduction to linear equations, solving equations using Python
Lecture 2: Loops in Python (for, while)
Lecture 3: Applying loops to solve basic math problems (factorials, sequences)
Week 4: Basic Data Structures and Lists
Lecture 1: Introduction to lists, list operations (indexing, slicing)
Lecture 2: Using lists to store and manipulate mathematical data
Lecture 3: Practical exercises (using loops with lists, storing multiple values)
Weeks 5-8: Statistics Concepts
Week 5: Introduction to Statistics
Lecture 1: Descriptive statistics overview (mean, median, mode)
Lecture 2: Calculating measures of central tendency using Python
Lecture 3: Introduction to numpy library for numerical operations
Week 6: Variability and Spread
Lecture 1: Measures of dispersion (variance, standard deviation, range)
Lecture 2: Python code for calculating variance and standard deviation
Lecture 3: Introduction to probability and its applications in statistics
Week 7: Probability Distributions
Lecture 1: Introduction to probability distributions (normal, binomial)
Lecture 2: Generating and visualizing probability distributions using Python
Lecture 3: Introduction to matplotlib for visualizations
Week 8: Hypothesis Testing
Lecture 1: Introduction to hypothesis testing, null and alternative hypotheses
Lecture 2: t-tests and p-values in Python using scipy
Lecture 3: Analyzing results and interpreting statistical significance
Weeks 9-12: Data Visualization
Week 9: Basic Data Visualization in Python
Lecture 1: Introduction to matplotlib, creating basic plots (line, scatter)
Lecture 2: Plot customization (titles, labels, legends)
Lecture 3: Introduction to seaborn for statistical visualizations
Week 10: Visualizing Distributions and Relationships
Lecture 1: Histograms, bar plots, and box plots using matplotlib and seaborn
Lecture 2: Visualizing relationships between variables using scatter plots and pair plots
Lecture 3: Creating subplots and grid plots for multi-plot visualizations
Week 11: Advanced Visualization Techniques
Lecture 1: Heatmaps and correlation matrices using seaborn
Lecture 2: Time series data visualization using matplotlib
Lecture 3: Plotly and interactive visualizations
Week 12: Projects in Data Visualization
Lecture 1: Building a data dashboard using matplotlib
Lecture 2: Case study on visualizing real-world data (student project)
Lecture 3: Group presentations of visualization projects
Weeks 13-16: Data Analysis and Applications
Week 13: Introduction to Data Analysis
Lecture 1: Understanding datasets and data types (categorical, numerical)
Lecture 2: Importing and exploring datasets in Python (pandas library)
Lecture 3: Data cleaning techniques (handling missing data, outliers)
Week 14: Data Transformation and Aggregation
Lecture 1: Filtering, sorting, and transforming data in pandas
Lecture 2: Grouping and aggregating data for analysis
Lecture 3: Real-world application: Analyzing a dataset (e.g., student performance)
Week 15: Advanced Data Analysis Techniques
Lecture 1: Introduction to machine learning concepts (linear regression)
Lecture 2: Applying linear regression to datasets using Python
Lecture 3: Evaluating the results of regression models
Week 16: Final Project and Review
Lecture 1: Final project overview (students choose a dataset for analysis)
Lecture 2: Group work on final projects (Python code implementation)
Lecture 3: Final project presentations and course review
This plan integrates math, statistics, and Python programming for hands-on learning and data analysis.