Unlock the world of data science and statistical modeling with our comprehensive course, Python for Data Science & Statistical Modeling.

Whether you're a novice or looking to enhance your skills, this course provides a structured pathway to mastering Python for data science and delving into the fascinating world of statistical modeling.

Module 1: Python Fundamentals for Data Science

Dive into the foundations of Python for data science, where you'll learn the essentials that form the basis of your data journey.

• Session 1: Introduction to Python & Data Science

• Session 2: Python Syntax & Control Flow

• Session 3: Data Structures in Python

• Session 4: Introduction to Numpy & Pandas for Data Manipulation

Module 2: Data Science Essentials with Python

Explore the core components of data science using Python, including exploratory data analysis, visualization, and machine learning.

• Session 5: Exploratory Data Analysis with Pandas & Numpy

• Session 6: Data Visualization with Matplotlib, Seaborn & Bokeh

• Session 7: Introduction to Scikit-Learn for Machine Learning in Python

Module 3: Mastering Probability, Statistics & Machine Learning

Gain in-depth knowledge of probability, statistics, and their seamless integration with Python's powerful machine learning capabilities.

• Session 8: Difference between Probability and Statistics

• Session 9: Set Theory and Probability Models

• Session 10: Random Variables and Distributions

• Session 11: Expectation, Variance, and Moments

Module 4: Practical Statistical Modeling with Python

Apply your understanding of probability and statistics to build statistical models and explore their real-world applications.

• Session 12: Probability and Statistical Modeling in Python

• Session 13: Estimation Techniques & Maximum Likelihood Estimate

• Session 14: Logistic Regression and KL-Divergence

• Session 15: Connecting Probability, Statistics & Machine Learning in Python

Module 5: Statistical Modeling Made Easy

Simplify statistical modeling with Python, covering summary statistics, hypothesis testing, correlation, and more.

• Session 16: Overview of Summary Statistics in Python

• Session 17: Introduction to Hypothesis Testing

• Session 18: Null and Alternate Hypothesis with Python

• Session 19: Correlation and Covariance in Python

Module 6: Implementing Statistical Models

Delve deeper into implementing statistical models with Python, including linear regression, multiple regression, and custom models.

• Session 20: Linear Regression and Coefficients

• Session 21: Testing for Correlation in Python

• Session 22: Multiple Regression and F-Test

• Session 23: Building Custom Statistical Models with Python Algorithms

Module 7: Capstone Projects & Real-World Applications

Put your skills to the test with hands-on projects, case studies, and real-world applications.

• Session 24: Mini-projects integrating Python, Data Science & Statistics

• Session 25: Case Study 1: Real-world applications of Statistical Models

• Session 26: Case Study 2: Python-based Data Analysis & Visualization

Module 8: Conclusion & Next Steps

Wrap up your journey with a recap of key concepts and guidance on advancing your data science career.

• Session 27: Recap & Summary of Key Concepts

• Session 28: Continuing Your Learning Path in Data Science & Python