Python Mastery for Data, Statistics & Statistical Modeling
Python Mastery for Data Science & Statistical Modeling: Basics to Advanced Applications in Data Analysis, Visualization
IT & Software,Other IT & Software,Data Analysis
Lectures -190
Duration -25 hours
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Course 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
Goals
What will you learn in this course:
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
Prerequisites
What are the prerequisites for this course?
No prior knowledge or experience is required. Everything is explained from absolute basics.
Curriculum
Check out the detailed breakdown of what’s inside the course
Python for Data Science and Data Analysis
61 Lectures
- Introduction: About the Tutor and AI Sciences 11:54 11:54
- Introduction: Introduction To Instructor 02:19 02:19
- Introduction: Focus of the Course-Part 1 10:54 10:54
- Introduction: Focus of the Course- Part 2 07:41 07:41
- Basics of Programming: Understanding the Algorithm 12:28 12:28
- Basics of Programming: FlowCharts and Pseudocodes 09:49 09:49
- Basics of Programming: Example of Algorithms- Making Tea Problem 12:33 12:33
- Basics of Programming: Example of Algorithms-Searching Minimun 15:47 15:47
- Basics of Programming: Example of Algorithms-Sorting Problem 07:19 07:19
- Basics of Programming: Sorting Problem in Python 10:34 10:34
- Why Python and Jupyter Notebook: Why Python 08:59 08:59
- Why Python and Jupyter Notebook: Why Jupyter Notebooks 12:52 12:52
- Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anaconda 04:23 04:23
- Installation of Anaconda and IPython Shell: Your First Python Code- Hello World 09:11 09:11
- Installation of Anaconda and IPython Shell: Coding in IPython Shell 07:13 07:13
- Variable and Operator: Variables 15:54 15:54
- Variable and Operator: Operators 13:38 13:38
- Variable and Operator: Variable Name Quiz 05:02 05:02
- Variable and Operator: Bool Data Type in Python 06:06 06:06
- Variable and Operator: Comparison in Python 07:19 07:19
- Variable and Operator: Combining Comparisons in Python 11:01 11:01
- Variable and Operator: Combining Comparisons Quiz 03:59 03:59
- Python Useful function: Python Function- Round 05:37 05:37
- Python Useful function: Python Function- Divmod 04:28 04:28
- Python Useful function: Python Function- Is instance and PowFunctions 06:07 06:07
- Python Useful function: Python Function- Input 08:48 08:48
- Control Flow in Python: If Python Condition 12:06 12:06
- Control Flow in Python: if Elif Else Python Conditions 08:45 08:45
- Control Flow in Python: More on if Elif Else Python Conditions 11:01 11:01
- Control Flow in Python: Indentations 13:22 13:22
- Control Flow in Python: Comments and Problem Solving Practice With If 16:50 16:50
- Control Flow in Python: While Loop 08:23 08:23
- Control Flow in Python: While Loop break Continue 12:12 12:12
- Control Flow in Python: For Loop 08:15 08:15
- Control Flow in Python: Else In For Loop 09:48 09:48
- Function and Module in Python: Functions in Python 08:38 08:38
- Function and Module in Python: DocString 08:23 08:23
- Function and Module in Python: Input Arguments 08:52 08:52
- Function and Module in Python: Multiple Input Arguments 09:43 09:43
- Function and Module in Python: Ordering Multiple Input Arguments 07:09 07:09
- Function and Module in Python: Output Arguments and Return Statement 07:19 07:19
- Function and Module in Python: Function Practice-Output Arguments and Return Statement 13:45 13:45
- Function and Module in Python: Variable Number of Input Arguments 07:48 07:48
- Function and Module in Python: Variable Number of Input Arguments as Dictionary 08:05 08:05
- Function and Module in Python: Default Values in Python 11:30 11:30
- Function and Module in Python: Modules in Python 05:28 05:28
- Function and Module in Python: Making Modules in Python 15:43 15:43
- Function and Module in Python: Function Practice-Sorting List in Python 27:29 27:29
- String in Python: Strings 09:30 09:30
- String in Python: Multi Line Strings 05:50 05:50
- String in Python: Indexing Strings 14:08 14:08
- String in Python: String Methods 14:56 14:56
- String in Python: String Escape Sequences 10:08 10:08
- Data Structure: Introduction to Data Structure 06:46 06:46
- Data Structure: Defining and Indexing 10:26 10:26
- Data Structure: Insertion and Deletion 07:29 07:29
- Data Structure: Python Practice-Insertion and Deletion 06:35 06:35
- Data Structure: Deep Copy or Reference Slicing 08:25 08:25
- Data Structure: Exploring Methods Using TAB Completion 07:22 07:22
- Data Structure: Data Structure Abstract Ways 06:32 06:32
- Data Structure: Data Structure Practice 19:39 19:39
Mastering Probability & Statistic Python (Theory & Projects)
82 Lectures
Statistics: Statistical Modeling Made Easy for ALL
47 Lectures
Instructor Details
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