Statistics For Data Science and Machine Learning with Python

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Taher Assaf

7:20:38

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1. Introduction

1. Overview of Course Curriculum.mp4

04:41

2. Installing Jupyter Notebook Environment.mp4

03:27

3.1 Course Notebooks.zip

3. How to Download Exercises & Course Notebooks.mp4

04:08

2. Data Types and Structures

1. Built-in Data Structures - Tuple and List.mp4

04:59

2. Built-in Data Structures - Dictionary and Set.mp4

03:24

3. Numpy Arrays.mp4

04:58

4. Pandas Series and Dataframes.mp4

07:15

5. Data Types (Numeric or Categorical).mp4

06:09

6. Exercise Create Data Structures in Python.mp4

02:20

3. Exploratory Data Analysis (1) Central Tendency Measures

1. Mean (Average).mp4

06:27

2. Weighted Average.mp4

05:32

3. Median.mp4

04:19

4. Population vs. Sample.mp4

06:43

5. Application in Data Science.mp4

02:57

6. Exercise Calculate Central Tendency Measures.mp4

01:59

4. Exploratory Data Analysis (2) Variability Measures

1. Range.mp4

04:09

2. Variance and Standard Deviation.mp4

04:36

3. Percentile & Quartile.mp4

07:22

4. Outlier part 1.mp4

05:52

5. Outlier part 2.mp4

04:30

6. Sampling Error.mp4

06:34

7. Application in Data Science.mp4

04:55

8. Exercise Calculate Variability Measures.mp4

02:28

5. Visualizing Data Distributions

1. Box Plot.mp4

06:38

2. Violin Plot.mp4

04:13

3. Histogram and Density Plot.mp4

06:17

4. Bar Plot for Categorical Data.mp4

05:10

5. Pie Chart for Categorical Data.mp4

03:51

6. Application in Data Science.mp4

07:01

7. Exercise Exploring Data Distribution.mp4

02:58

6. Correlation, Scatterplots, and Heat Maps

1. Correlation and Covariance Coefficients.mp4

04:56

2. Correlation Using Scatter plot.mp4

06:31

3. Mapping with Scatter plots.mp4

03:59

4. Heat Maps.mp4

05:51

5. Application in Data Science.mp4

07:20

6. Exercise Create Mapped Scatterplots and Heat Maps.mp4

02:35

7. Capstone Project for Exploratory Analysis

1. Project Description.mp4

02:35

2. Solution walk-through of The Project.mp4

05:06

8. Data Distributions and Data Sampling

1. Random Sampling and Bias.mp4

06:38

2. Central Limit Theorem.mp4

06:21

3. Normal distribution.mp4

06:55

4. Normality Tests for Real-World Data.mp4

07:20

5. Skewed Data Real-life Distributions.mp4

09:46

6. Probability A Practical Introduction.mp4

03:14

7. Common Probability Distributions.mp4

09:36

8. Exercise Normal Distribution and Skewness.mp4

03:12

9. Data Scaling and Transformation

1. Data Scaling Standardization.mp4

09:06

2. Data Scaling Normalization.mp4

05:11

3. Log and Square Root Transformations.mp4

07:08

4. Power Transformation (PowerTransformer).mp4

07:46

5. Application in Data Science.mp4

07:51

6. Exercise Data Scaling and Transformation.mp4

02:54

10. Confidence Intervals (CI)

1. C.I for Continuous Data.mp4

07:41

2. C.I for Classification Data.mp4

05:39

3. Bootstrapping For Unknown Distributions.mp4

06:41

4. Nonparametric Confidence Interval with Bootstrapping.mp4

08:09

5. Exercise Create Confidence Interval.mp4

03:36

11. Evaluation Metrics for Machine Learning

1. Bias vs. Variance.mp4

07:11

2. Overfitting and Underfitting.mp4

13:07

3. Information Criteria for Model Selection.mp4

07:25

4. Evaluation Metrics for Regression Models.mp4

09:17

5. Evaluation Metrics for Classification Models _Part One.mp4

05:36

6. Evaluation Metrics for Classification Models Part Two.mp4

07:39

7. Application in Data Science.mp4

09:15

8. Exercise Evaluating Machine Learning Models.mp4

03:46

12. Model Validation Techniques in Machine Learning

1. Hold Out Validation - TrainTest Split.mp4

09:46

2. K-Fold Cross-Validation.mp4

07:54

3. Leave-One-Out Cross-Validation (LOOCV).mp4

05:32

4. Application in Data Science.mp4

07:55

5. Exercise Validation Techniques in Machine Learning.mp4

03:49

13. Final project

1. Project Description.mp4

08:17

2. Walk-through Solution of the Project Part One.mp4

07:10

3. Walk-through Solution of the Project Part Two.mp4

07:57

4. Walk-through Solution of the Project Part Three.mp4

09:33

Description

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

What You'll Learn?

You will learn to use data exploratory analysis in data science.
You will learn the most common data types such as continuous and categorical data.
You will learn the central tendency measures and the dispersion measures in statistics.
You will learn the concepts of population data vs sample data.
You will learn what random sampling means and how it affects data analysis.
You will learn about outliers and sampling errors and how they are related to data analysis.
You will learn how to visualize data distribution using boxplots, violin plots, histograms, and density plots.
You will learn how to visualize categorical data using bar plots and pie charts.
You will learn how to calculate correlation and covariance between features in the dataset.
You will learn how to visualize a correlation matrix using heat maps.
You will learn the most common probability distributions such as normal distribution and binomial distribution.
You will learn how to perform normality tests to check for deviation from normality.
You will learn how to test skewed distributions in real-world data.
You will learn how to standardize and normalize data to have the same scale.
You will learn how to transform skewed data to be normally distributed using different transformation methods such as log, square root, and power transformation
You will learn how to calculate confidence intervals for statistical estimates such as model accuracy.
You will learn bootstrapping in statistics and how it is used in machine learning.
You will learn how to evaluate machine learning models.
You will practically understand the concepts of bias and variance in data modeling.
You will understand what we mean by underfitting and overfitting in machine leaning and statistical modeling.
You will learn the most common evaluation metrics for regression models in machine learning.
You will learn the evaluation metrics for classification models.
You will learn how to validate predictive machine learning such as regression and classification models.
You will learn how to use different validation techniques for machine learning such as hold-out validation and cross-validation techniques.

Who is this for?

This course is for students who want to learn statistics from data science perspective.

What You Need to Know?

No background in statistics is needed, everything will be explained in this course. A basic knowledge in python is helpful.

More details

Description
This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning!
Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning.
Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning. However, this fact makes it more difficult for learners to study statistics because they are not sure where to start and what are the most relevant topics of statistics for data science.
This course comes to close this gap.
This course is designed for both beginners with no background in statistics for data science or for those looking to extend their knowledge in the field of statistics for data science.
I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single topic.
In this comprehensive course, I will guide you to learn the most common and essential methods of statistics for data analysis and data modeling.
My course is equivalent to a college-level course in statistics for data science and machine learning that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With 77 HD video lectures, many exercises, and two projects with solutions.
All materials presented in this course are provided in detailed downloadable notebooks for every lecture.
Most students focus on learning python codes for data science, however, this is not enough to be a proficient data scientist. You also need to understand the statistical foundation of python methods. Models and data analysis can be easily created in python, but to be able to choose the correct method or select the best model you need to understand the statistical methods that are used in these models. Here are a few of the topics that you will be learning in this comprehensive course:
Â· Data Types and Structures
Â· Exploratory Data Analysis
Â· Central Tendency Measures
Â· Dispersion Measures
Â· Visualizing Data Distributions
Â· Correlation, Scatterplots, and Heat Maps
Â· Data Distribution and Data Sampling
Â· Data Scaling and Transformation
Â· Data Scaling and Transformation
Â· Confidence Intervals
Â· Evaluation Metrics for Machine Learning
Â· Model Validation Techniques in Machine Learning

Enroll in the course and gain the essential knowledge of statistical methods for data science today!
Who this course is for:
This course is for students who want to learn statistics from data science perspective.

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