Udemy - Python Mastery For Data Statistics & Statistical Modeling Free Download

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

What you’ll learn
Solid grasp of Python programming for Data Science & Statistics
Practical experience through hands-on projects and case studies
Ability to apply Statistical Modeling techniques using Python
Understanding of real-world applications in Data Analysis and Machine Learning

Requirements
No prior knowledge or experience is required. Everything is explained from absolute basics.

Description
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

Join us on this transformative learning adventure, where you’ll gain the skills and knowledge to excel in data science, statistical modeling, and Python. Enroll now and embark on your path to data-driven success!

Who Should Take This Course?

Aspiring Data Scientists

Data Analysts

Business Analysts

Students pursuing a career in data-related fields

Anyone interested in harnessing Python for data insights

Why This Course?

In today’s data-driven world, proficiency in Python and statistical modeling is a highly sought-after skillset. This course empowers you with the knowledge and practical experience needed to excel in data analysis, visualization, and modeling using Python. Whether you’re aiming to kickstart your career, enhance your current role, or simply explore the world of data, this course provides the foundation you need.

What You Will Learn:

This course is structured to take you from Python fundamentals to advanced statistical modeling, equipping you with the skills to:

Master Python syntax and data structures for effective data manipulation

Explore exploratory data analysis techniques using Pandas and Numpy

Create compelling data visualizations using Matplotlib, Seaborn, and Bokeh

Dive into Scikit-Learn for machine learning in Python

Understand key concepts in probability and statistics

Apply statistical modeling techniques in real-world scenarios

Build custom statistical models using Python algorithms

Perform hypothesis testing and correlation analysis

Implement linear and multiple regression models

Work on hands-on projects and real-world case studies

Keywords:

Python for Data Science, Statistical Modeling, Data Analysis, Data Visualization, Machine Learning, Pandas, Numpy, Matplotlib, Seaborn, Bokeh, Scikit-Learn, Probability, Statistics, Hypothesis Testing, Regression Analysis, Data Insights, Python Syntax, Data Manipulation

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