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Python Mastery for Data, Statistics & Statistical Modeling
Rating: 4.4 out of 5(8 ratings)
106 students

Python Mastery for Data, Statistics & Statistical Modeling

Python Mastery for Data Science & Statistical Modeling: Basics to Advanced Applications in Data Analysis, Visualization
Created byAI Sciences, AI Sciences Team
Last updated 12/2025
English

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

Course content

3 sections • 266 lectures • 28h 7m total length

  • Link to the Python codes for the projects and the data0:16
  • Introduction: About the Tutor and AI Sciences11:54
  • Introduction: Introduction To Instructor2:19
  • Introduction: Focus of the Course-Part 110:54
  • Introduction: Focus of the Course- Part 27:41
  • Basics of Programming: Understanding the Algorithm12:28
  • Basics of Programming: FlowCharts and Pseudocodes9:49
  • Basics of Programming: Example of Algorithms- Making Tea Problem12:33
  • Basics of Programming: Example of Algorithms-Searching Minimun15:47
  • Basics of Programming: Example of Algorithms-Searching Minimun Quiz0:52
  • Basics of Programming: Example of Algorithms-Sorting Problem7:19
  • Basics of Programming: Example of Algorithms-Searching Minimun Solution3:24
  • Basics of Programming: Sorting Problem in Python10:34
  • Why Python and Jupyter Notebook: Why Python8:59
  • Why Python and Jupyter Notebook: Why Jupyter Notebooks12:52
  • Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anaconda4:23
  • Installation of Anaconda and IPython Shell: Your First Python Code- Hello World9:11
  • Installation of Anaconda and IPython Shell: Coding in IPython Shell7:13
  • Variable and Operator: Variables15:54
  • Variable and Operator: Operators13:38
  • Variable and Operator: Variable Name Quiz5:02
  • Variable and Operator: Bool Data Type in Python6:06
  • Variable and Operator: Comparison in Python7:19
  • Variable and Operator: Combining Comparisons in Python11:01
  • Variable and Operator: Combining Comparisons Quiz3:59
  • Python Useful function: Python Function- Round5:37
  • Python Useful function: Python Function- Round Quiz1:29
  • Python Useful function: Python Function- Round Solution4:41
  • Python Useful function: Python Function- Divmod4:28
  • Python Useful function: Python Function- Is instance and PowFunctions6:07
  • Python Useful function: Python Function- Input8:48
  • Control Flow in Python: If Python Condition12:06
  • Control Flow in Python: if Elif Else Python Conditions8:45
  • Control Flow in Python: if Elif Else Python Conditions Quiz1:36
  • Control Flow in Python: if Elif Else Python Conditions Solution3:54
  • Control Flow in Python: More on if Elif Else Python Conditions11:01
  • Control Flow in Python: More on if Elif Else Python Conditions Quiz0:50
  • Control Flow in Python: More on if Elif Else Python Conditions Solution3:54
  • Control Flow in Python: Indentations13:22
  • Control Flow in Python: Indentations Quiz1:05
  • Control Flow in Python: Indentations Solution2:41
  • Control Flow in Python: Comments and Problem Solving Practice With If16:50
  • Control Flow in Python: While Loop8:23
  • Control Flow in Python: While Loop break Continue12:12
  • Control Flow in Python: While Loop break Continue Quiz1:20
  • Control Flow in Python: While Loop break Continue Solution4:44
  • Control Flow in Python: For Loop8:15
  • Control Flow in Python: For Loop Quiz1:01
  • Control Flow in Python: For Loop Solution3:03
  • Control Flow in Python: Else In For Loop9:48
  • Control Flow in Python: Loops Practice-Sorting Problem12:23
  • Function and Module in Python: Functions in Python8:38
  • Function and Module in Python: DocString8:23
  • Function and Module in Python: Input Arguments8:52
  • Function and Module in Python: Multiple Input Arguments9:43
  • Function and Module in Python: Multiple Input Arguments Quiz1:11
  • Function and Module in Python: Multiple Input Arguments Solution4:08
  • Function and Module in Python: Ordering Multiple Input Arguments7:09
  • Function and Module in Python: Output Arguments and Return Statement7:19
  • Function and Module in Python: Function Practice-Output Arguments and Return Statement13:45
  • Function and Module in Python: Variable Number of Input Arguments7:48
  • Function and Module in Python: Variable Number of Input Arguments Quiz1:23
  • Function and Module in Python: Variable Number of Input Arguments Solution3:47
  • Function and Module in Python: Variable Number of Input Arguments as Dictionary8:05
  • Function and Module in Python: Variable Number of Input Arguments as Dictionary Quiz0:55
  • Function and Module in Python: Variable Number of Input Arguments as Dictionary Solution3:47
  • Function and Module in Python: Default Values in Python11:30
  • Function and Module in Python: Modules in Python5:28
  • Function and Module in Python: Making Modules in Python15:43
  • Function and Module in Python: Function Practice-Sorting List in Python27:29
  • String in Python: Strings9:30
  • String in Python: Multi Line Strings5:50
  • String in Python: Indexing Strings14:08
  • String in Python: Indexing Strings Quiz1:02
  • String in Python: Indexing Strings Solution4:21
  • String in Python: String Methods14:56
  • String in Python: String Methods Quiz1:03
  • String in Python: String Methods Solution2:06
  • String in Python: String Escape Sequences10:08
  • String in Python: String Escape Sequences Quiz1:02
  • String in Python: String Escape Sequences Solution3:28
  • Data Structure: Introduction to Data Structure6:46
  • Data Structure: Defining and Indexing10:26
  • Data Structure: Insertion and Deletion7:29
  • Data Structure: Insertion and Deletion Quiz1:07
  • Data Structure: Insertion and Deletion Solution2:18
  • Data Structure: Python Practice-Insertion and Deletion6:35
  • Data Structure: Python Practice-Insertion and Deletion Quiz0:56
  • Data Structure: Python Practice-Insertion and Deletion Solution1:02
  • Data Structure: Deep Copy or Reference Slicing8:25
  • Data Structure: Deep Copy or Reference Slicing Quiz1:20
  • Data Structure: Deep Copy or Reference Slicing Solution1:44
  • Data Structure: Exploring Methods Using TAB Completion7:22
  • Data Structure: Data Structure Abstract Ways6:32
  • Data Structure: Data Structure Practice19:39
  • Data Structure: Data Structure Practice Quiz1:49
  • Data Structure: Data Structure Practice Solution6:15

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

Who this course is for:

  • Beginners in Python and Data Science
  • Python Enthusiasts looking to apply skills in Data Analysis
  • Aspiring Data Scientists seeking a strong foundation
  • Professionals aiming to enhance their statistical modeling skills

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