Description
Python Mastery for Data Statistics & Statistical Modeling course. Step into the world of data science and statistical modeling with our comprehensive course, Python for Data Science & Statistical Modeling. Whether you’re a beginner or looking to improve your skills, this course provides a structured path to mastering Python for data science and exploring the fascinating world of statistical modeling.
Module 1: Python Fundamentals for Data Science
Dive into the basics of Python for data science, where you’ll learn the essentials that will form the foundation of your data journey.
- Session 1: Introduction to Python and Data Science
- Session 2: Python Syntax & Control Flow
- Session 3: Data structures in Python
- Session 4: Introduction to Numpy and Pandas for data manipulation
Module 2: Essentials of Data Science 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 and Numpy
- Session 6: Data visualization with Matplotlib, Seaborn and Bokeh
- Session 7: Introduction to Scikit-Learn for machine learning in Python
Module 3: Mastering Probability, Statistics and Machine Learning
Gain deep knowledge of probability, statistics and their seamless integration with Python’s powerful machine learning capabilities.
- Session 8: The difference between probability and statistics
- Session 9: Theory of sets and probability models
- Session 10: Random variables and distribution
- Eleventh session: 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.
- The twelfth session: probabilities and statistical modeling in Python
- Session 13: Estimation techniques and maximum likelihood estimation
- Session 14: Logistic regression and KL divergence
- Session 15: Connecting probability, statistics and machine learning in Python
Module 5: Statistical modeling made easy
Simplify statistical modeling with Python, including summary statistics, hypothesis testing, correlation, and more.
- Session 16: An overview of summary statistics in Python
- Session 17: Introduction to hypothesis testing
- Session 18: null hypothesis and alternative with Python
- Session 19: Correlation and covariance in Python
Module 6: Implementation of statistical models
Go deeper into implementing statistical models with Python, including linear regression, multiple regression, and custom models.
- Session 20: Linear regression and coefficients
- Session 21: Correlation test in Python
- Session 22: Multiple regression and F test
- Session 23: Building customized statistical models with Python algorithms
Module 7: Capstone Projects and Real-World Applications
Test your skills with hands-on projects, case studies and real-world applications.
- Session 24: Small Projects Integrating Python, Data Science, and Statistics
- Session 25: Case study 1: Real applications of statistical models
- Session 26: Case Study 2: Python-Based Data Analysis and Visualization
Module 8: Conclusion and next steps
Conclude your journey with a summary of key concepts and guidance for advancing your data science career.
- Session 27: Summary and summary of key concepts
- Session 28: Continuing the learning path in data science and 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. Sign up now and start your journey to data-driven success!
Who will take this lesson?
- Aspiring data scientists
- Data analysts
- Business analysts
- Students pursuing careers in data-related fields
- Anyone interested in using Python for data insight
Why this course?
In today’s data-driven world, proficiency in Python and statistical modeling is a highly sought-after skill set. This course empowers you with the knowledge and hands-on experience needed to excel in data analysis, visualization and modeling using Python. Whether you want to start your career, enhance your current role or simply explore the world of data, this course will provide the foundation you need.
What you will learn in the Python Mastery for Data Statistics & Statistical Modeling course
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Thorough understanding of Python programming for data science and statistics
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Practical experience through practical projects and case studies
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The possibility of using statistical modeling techniques using Python
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Understand real-world applications in data analytics and machine learning
- Master Python grammar 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
- Log into Scikit-Learn for machine learning in Python
- Understand key concepts in probability and statistics
- Using statistical modeling techniques in real world scenarios
- Building custom statistical models using Python algorithms
- Conduct hypothesis testing and correlation analysis
- Implementation of linear and multiple regression models
- Work on hands-on projects and real-world case studies
This course is suitable for people who
- Python and data science beginners
- Python enthusiasts looking to apply data analysis skills
- Aspiring data scientists are looking for a solid foundation
- Professionals aiming to enhance their statistical modeling skills
Details of the Python Mastery for Data Statistics & Statistical Modeling course
- Publisher: Udemy
- teacher: AI Sciences
- Training level: beginner to advanced
- Training duration: 11 hours and 10 minutes
- Number of courses:
Python Mastery for Data Statistics & Statistical Modeling course topics
Python Mastery for Data Statistics & Statistical Modeling course prerequisites
- No prior knowledge or experience is required. Everything is explained from absolute basics.
Course images
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