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
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