Python Mastery for Data, Statistics & Statistical Modeling

Python Mastery for Data, Statistics & Statistical Modeling, available at $19.99, with 267 lectures, and has 16 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners in Python and Data Science or Python Enthusiasts looking to apply skills in Data Analysis or Aspiring Data Scientists seeking a strong foundation or Professionals aiming to enhance their statistical modeling skills It is particularly useful for Beginners in Python and Data Science or Python Enthusiasts looking to apply skills in Data Analysis or Aspiring Data Scientists seeking a strong foundation or Professionals aiming to enhance their statistical modeling skills.

Enroll now: Python Mastery for Data, Statistics & Statistical Modeling

Summary

Title: Python Mastery for Data, Statistics & Statistical Modeling

Price: $19.99

Number of Lectures: 267

Number of Published Lectures: 266

Number of Curriculum Items: 267

Number of Published Curriculum Objects: 266

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Python for Data Science and Data Analysis

Lecture 1: Link to the Python codes for the projects and the data

Lecture 2: Introduction: About the Tutor and AI Sciences

Lecture 3: Introduction: Introduction To Instructor

Lecture 4: Introduction: Focus of the Course-Part 1

Lecture 5: Introduction: Focus of the Course- Part 2

Lecture 6: Basics of Programming: Understanding the Algorithm

Lecture 7: Basics of Programming: FlowCharts and Pseudocodes

Lecture 8: Basics of Programming: Example of Algorithms- Making Tea Problem

Lecture 9: Basics of Programming: Example of Algorithms-Searching Minimun

Lecture 10: Basics of Programming: Example of Algorithms-Searching Minimun Quiz

Lecture 11: Basics of Programming: Example of Algorithms-Sorting Problem

Lecture 12: Basics of Programming: Example of Algorithms-Searching Minimun Solution

Lecture 13: Basics of Programming: Sorting Problem in Python

Lecture 14: Why Python and Jupyter Notebook: Why Python

Lecture 15: Why Python and Jupyter Notebook: Why Jupyter Notebooks

Lecture 16: Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anaconda

Lecture 17: Installation of Anaconda and IPython Shell: Your First Python Code- Hello World

Lecture 18: Installation of Anaconda and IPython Shell: Coding in IPython Shell

Lecture 19: Variable and Operator: Variables

Lecture 20: Variable and Operator: Operators

Lecture 21: Variable and Operator: Variable Name Quiz

Lecture 22: Variable and Operator: Bool Data Type in Python

Lecture 23: Variable and Operator: Comparison in Python

Lecture 24: Variable and Operator: Combining Comparisons in Python

Lecture 25: Variable and Operator: Combining Comparisons Quiz

Lecture 26: Python Useful function: Python Function- Round

Lecture 27: Python Useful function: Python Function- Round Quiz

Lecture 28: Python Useful function: Python Function- Round Solution

Lecture 29: Python Useful function: Python Function- Divmod

Lecture 30: Python Useful function: Python Function- Is instance and PowFunctions

Lecture 31: Python Useful function: Python Function- Input

Lecture 32: Control Flow in Python: If Python Condition

Lecture 33: Control Flow in Python: if Elif Else Python Conditions

Lecture 34: Control Flow in Python: if Elif Else Python Conditions Quiz

Lecture 35: Control Flow in Python: if Elif Else Python Conditions Solution

Lecture 36: Control Flow in Python: More on if Elif Else Python Conditions

Lecture 37: Control Flow in Python: More on if Elif Else Python Conditions Quiz

Lecture 38: Control Flow in Python: More on if Elif Else Python Conditions Solution

Lecture 39: Control Flow in Python: Indentations

Lecture 40: Control Flow in Python: Indentations Quiz

Lecture 41: Control Flow in Python: Indentations Solution

Lecture 42: Control Flow in Python: Comments and Problem Solving Practice With If

Lecture 43: Control Flow in Python: While Loop

Lecture 44: Control Flow in Python: While Loop break Continue

Lecture 45: Control Flow in Python: While Loop break Continue Quiz

Lecture 46: Control Flow in Python: While Loop break Continue Solution

Lecture 47: Control Flow in Python: For Loop

Lecture 48: Control Flow in Python: For Loop Quiz

Lecture 49: Control Flow in Python: For Loop Solution

Lecture 50: Control Flow in Python: Else In For Loop

Lecture 51: Control Flow in Python: Loops Practice-Sorting Problem

Lecture 52: Function and Module in Python: Functions in Python

Lecture 53: Function and Module in Python: DocString

Lecture 54: Function and Module in Python: Input Arguments

Lecture 55: Function and Module in Python: Multiple Input Arguments

Lecture 56: Function and Module in Python: Multiple Input Arguments Quiz

Lecture 57: Function and Module in Python: Multiple Input Arguments Solution

Lecture 58: Function and Module in Python: Ordering Multiple Input Arguments

Lecture 59: Function and Module in Python: Output Arguments and Return Statement

Lecture 60: Function and Module in Python: Function Practice-Output Arguments and Return Statement

Lecture 61: Function and Module in Python: Variable Number of Input Arguments

Lecture 62: Function and Module in Python: Variable Number of Input Arguments Quiz

Lecture 63: Function and Module in Python: Variable Number of Input Arguments Solution

Lecture 64: Function and Module in Python: Variable Number of Input Arguments as Dictionary

Lecture 65: Function and Module in Python: Variable Number of Input Arguments as Dictionary Quiz

Lecture 66: Function and Module in Python: Variable Number of Input Arguments as Dictionary Solution

Lecture 67: Function and Module in Python: Default Values in Python

Lecture 68: Function and Module in Python: Modules in Python

Lecture 69: Function and Module in Python: Making Modules in Python

Lecture 70: Function and Module in Python: Function Practice-Sorting List in Python

Lecture 71: String in Python: Strings

Lecture 72: String in Python: Multi Line Strings

Lecture 73: String in Python: Indexing Strings

Lecture 74: String in Python: Indexing Strings Quiz

Lecture 75: String in Python: Indexing Strings Solution

Lecture 76: String in Python: String Methods

Lecture 77: String in Python: String Methods Quiz

Lecture 78: String in Python: String Methods Solution

Lecture 79: String in Python: String Escape Sequences

Lecture 80: String in Python: String Escape Sequences Quiz

Lecture 81: String in Python: String Escape Sequences Solution

Lecture 82: Data Structure: Introduction to Data Structure

Lecture 83: Data Structure: Defining and Indexing

Lecture 84: Data Structure: Insertion and Deletion

Lecture 85: Data Structure: Insertion and Deletion Quiz

Lecture 86: Data Structure: Insertion and Deletion Solution

Lecture 87: Data Structure: Python Practice-Insertion and Deletion

Lecture 88: Data Structure: Python Practice-Insertion and Deletion Quiz

Lecture 89: Data Structure: Python Practice-Insertion and Deletion Solution

Lecture 90: Data Structure: Deep Copy or Reference Slicing

Lecture 91: Data Structure: Deep Copy or Reference Slicing Quiz

Lecture 92: Data Structure: Deep Copy or Reference Slicing Solution

Lecture 93: Data Structure: Exploring Methods Using TAB Completion

Lecture 94: Data Structure: Data Structure Abstract Ways

Lecture 95: Data Structure: Data Structure Practice

Lecture 96: Data Structure: Data Structure Practice Quiz

Lecture 97: Data Structure: Data Structure Practice Solution

Chapter 2: Mastering Probability & Statistic Python (Theory & Projects)

Lecture 1: Link to the Python codes for the projects and the data

Instructors

AI Sciences
AI Experts & Data Scientists |4+ Rated | 168+ Countries
AI Sciences Team
Support Team AI Sciences

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 0 votes
5 stars: 0 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu