Statistics For Data Science and Machine Learning with Python

سرفصل های دوره

Practical Statistics with Python for Data Science & Machine Learning Statistical Modeling Using Sci-kit Learn and Scipy

1. Introduction

1. Overview of Course Curriculum

2. Installing Jupyter Notebook Environment

3.1 Course Notebooks.zip

3. How to Download Exercises & Course Notebooks

2. Data Types and Structures

1. Built-in Data Structures - Tuple and List

2. Built-in Data Structures - Dictionary and Set

3. Numpy Arrays

4. Pandas Series and Dataframes

5. Data Types (Numeric or Categorical)

6. Exercise Create Data Structures in Python

3. Exploratory Data Analysis (1) Central Tendency Measures

1. Mean (Average)

2. Weighted Average

3. Median

4. Population vs. Sample

5. Application in Data Science

6. Exercise Calculate Central Tendency Measures

4. Exploratory Data Analysis (2) Variability Measures

1. Range

2. Variance and Standard Deviation

3. Percentile & Quartile

4. Outlier part 1

5. Outlier part 2

6. Sampling Error

7. Application in Data Science

8. Exercise Calculate Variability Measures

5. Visualizing Data Distributions

1. Box Plot

2. Violin Plot

3. Histogram and Density Plot

4. Bar Plot for Categorical Data

5. Pie Chart for Categorical Data

6. Application in Data Science

7. Exercise Exploring Data Distribution

6. Correlation, Scatterplots, and Heat Maps

1. Correlation and Covariance Coefficients

2. Correlation Using Scatter plot

3. Mapping with Scatter plots

4. Heat Maps

5. Application in Data Science

6. Exercise Create Mapped Scatterplots and Heat Maps

7. Capstone Project for Exploratory Analysis

1. Project Description

2. Solution walk-through of The Project

8. Data Distributions and Data Sampling

1. Random Sampling and Bias

2. Central Limit Theorem

3. Normal distribution

4. Normality Tests for Real-World Data

5. Skewed Data Real-life Distributions

6. Probability A Practical Introduction

7. Common Probability Distributions

8. Exercise Normal Distribution and Skewness

9. Data Scaling and Transformation

1. Data Scaling Standardization

2. Data Scaling Normalization

3. Log and Square Root Transformations

4. Power Transformation (PowerTransformer)

5. Application in Data Science

6. Exercise Data Scaling and Transformation

10. Confidence Intervals (CI)

1. C.I for Continuous Data

2. C.I for Classification Data

3. Bootstrapping For Unknown Distributions

4. Nonparametric Confidence Interval with Bootstrapping

5. Exercise Create Confidence Interval

11. Evaluation Metrics for Machine Learning

1. Bias vs. Variance

2. Overfitting and Underfitting

3. Information Criteria for Model Selection

4. Evaluation Metrics for Regression Models

5. Evaluation Metrics for Classification Models _Part One

6. Evaluation Metrics for Classification Models Part Two

7. Application in Data Science

8. Exercise Evaluating Machine Learning Models

12. Model Validation Techniques in Machine Learning

1. Hold Out Validation - TrainTest Split

2. K-Fold Cross-Validation

3. Leave-One-Out Cross-Validation (LOOCV)

4. Application in Data Science

5. Exercise Validation Techniques in Machine Learning

13. Final project

1. Project Description

2. Walk-through Solution of the Project Part One

3. Walk-through Solution of the Project Part Two

4. Walk-through Solution of the Project Part Three

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