Statistics & Probability for Data Science – 25+ Projects

سرفصل های دوره

Gain control over your data with Math & Stats foundation required for Data Science, Machine Learning & Deep learning

1. Section 1 Foundation of Statistics

1. Introduction to Statistics

2. Types of Statistical Analysis - Descriptive Statistics

3. Types of Statistical Analysis - Inferential Statistics

4. How Statistics and Machine Learning are Related

5. Understanding the Types of Data

6. Sampling Techniques

7. Descriptive Statistics - Measure of Central Tendency

8. Descriptive Statistics - Measures of Dispersion - Range & Interquartile Range

9. Descriptive Statistics - Measures of Dispersion - Variance & Standard Deviation

10. Hands On - Exercise with Python

11. Descriptive Statistics - Measures of Shape

12. Descriptive Statistics - Measures of Position

13. Descriptive Statistics - Standard Scores

14. Descriptive Statistics - Hands On

15. Problem Statement - Wine Reviews Data Set Analysis

16. Solution for Project 1

17. Project 2 - Customer Income Data Analysis

18. Solution for Project 2

19. Project 3 - US Arrests Dataset

20. Solution for Project 3 - US Arrests Dataset

21. Project 4 - BigMart Sales data analysis

22. Solution for Big Mart Data Analysis

2. Section 2 Exploratory Data Analysis

1. Introduction to Exploratory Data Analysis

2. Types of Data Analysis

3. Univariate Non Graphical EDA & Outlier Analysis

4. Univariate Graphical EDA & Hands On

5. Multivariate Non Graphical EDA

6. Multi variate Graphical EDA

7. Steps in EDA

8. Summary of Graphical EDA Techniques

9. Hands On EDA on Titanic Data Set

10. Project 5 - Crimes in Boston City

11. Project 5 - Solution

12. Project 6 - PUBG Game Analysis

13. Project 6 - PUBG Game Analysis - Solution

14. Project 7 - FIFA Game Analysis

15. Project 7 - Solution

16. Project 8 - Covid19 Data Analysis

17. Project 8 Solution

3. Section 3 - Probability

1. Introduction to Probability

2. Key Terminology of Probability

3. Rules of Probability

4. Marginal Probability , Joint Probability

5. Disjoint Events and Non Disjoint events

6. Independent and Dependent events

7. Product Rule of Dependent & Independent Events

8. Task with Manifold Bank and compute probability

9. Bayes Theorem

10. Bayes Theorem in Data Science

11. Hands On Bayes Algorithm in Python

12. Random Variables

13. Various Distribution functions

14. Hands ON Generate the Discrete & Continuous Random numbers

15. Central Limit Theorem and Hands On

16. Applications of Probability Distributions

17. Hands On Transform the data to get Normal Distribution curve

18. Example Problems for Probability

19. Project 9 - Cars Dataset & Solution

20. Hands On - Bayes Theorem

21. Project 10 - Hands On - Normal Distribution & CDF

4. Section 4 - Inferential Statistics

1. Introduction to Inferential Statistics

2. Key Terminology of Inferential Statistics

3. Hands On - Population & Sample

4. Types of Statistical Inference

5. Confidence Interval - Margin of Error - Confidence Interval Estimation

6. Demo - Margin of Error and Confidence Interval

7. Hypothesis Testing & Steps of Hypothesis testing

8. ZTest and Example Problem

9. ZTest Solution Hands On

10. 1 Sample t-test

11. 1 sample t-test Hands On

12. 2 Sample t-test

13. 2 sample t-test Hands On

14. Paired Sample t-test

15. Hands On - Paired Sample t-test

16. Chi-Square Goodness of Fit

17. Hands On - Chi Square test

18. Anova

19. Hands On - Anova

20. Project 11 - Inferential Statistics - cars

21. Project 11 - Solution

22. Project 12 - Blood Pressure health dataset

23. Project 12 - Solution

24. Project 13 - Students admissions dataset

25. Project 13 - Solution

5. Section 5 - Linear Regression

1. Introduction to Regression , What , Why and Types of Problem we can solve

2. Assumptions of Linear Regression

3. Intuition of Linear Regression

4. Linear Regression with Normal Equation

5. Apply Linear Regression using Sklearn - Hands On

6. Checking Assumption of Linear Regression - Hands On

7. How Good is your fit

8. How Minimisation of Error is performed - Gradient Descent

9. Gradient Descent Hands On Part 1

10. Gradient Descent Hands On Part 2

11. Project 14 - Hands On - Implementation of Linear Regression using StatsModels

12. Project 14 - Solution

13. Project 15 - Salary Prediction Problem Statement

14. Project 15 - Solution

15. Project 16 - House Price Prediction Dataset

16. Project 16 - Solution

17. Project 17 - Medical Cost Prediction

18. Project 17 Solution

19. Project 18 - Company Profit prediction

20. Project 18 - Solution

6. Section 6 - Logistic Regression

1. Introduction to Logistic Regression

2. Hands On - Logistic Regression Plot

3. Assumptions of Logistic Regression

4. Logistic Regression from Scratch

5. Project 19 - Diabetes Prediction

6. Project 19 - Solution

7. Project 20 - Heart Disease Prediction

8. Project 20 - Solution

9. Project 21 - Titanic Survival Dataset

10. Project 21 - Solution

11. Project 22 - Nursery Student Dataset

12. Project 22 - Solution

7. Section 7 - Miscellaneous Stats Concepts in Machine Learning Areas

1. Resampling Technique

2. Cross validation Techniques Hands On

3. Project 23 - Flight Price Prediction

4. Project 23 Solution

5. Project 24 - Concrete Compressive Strength

6. Project 24 - Solution

7. Project 25 - US Baseball Salary prediction

8. Project 25 - Solution

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تولید کننده: Udemy-Training