Python Mastery for Data Science & Statistical Modeling: Basics to Advanced Applications in Data Analysis, Visualization
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
