Python Statistical Methods: Machine Learning & Data Science

Practical Statistics with Python for Data Science & Machine Learning 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.
Requirements
No background in statistics is needed, everything will be explained in this course. A basic knowledge in python is helpful.
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:· Data Types and Structures· Exploratory Data Analysis· Central Tendency Measures· Dispersion Measures· Visualizing Data Distributions· Correlation, Scatterplots, and Heat Maps· Data Distribution and Data Sampling· Data Scaling and Transformation· Data Scaling and Transformation· Confidence Intervals· Evaluation Metrics for Machine Learning· Model Validation Techniques in Machine LearningEnroll in the course and gain the essential knowledge of statistical methods for data science today!

Overview

Section 1: Introduction

Lecture 1 Overview of Course Curriculum

Lecture 2 Installing Jupyter Notebook Environment

Lecture 3 How to Download Exercises & Course Notebooks

Section 2: Data Types and Structures

Lecture 4 Built-in Data Structures - Tuple and List

Lecture 5 Built-in Data Structures - Dictionary and Set

Lecture 6 Numpy Arrays

Lecture 7 Pandas Series and Dataframes

Lecture 8 Data Types (Numeric or Categorical)

Lecture 9 Exercise: Create Data Structures in Python

Section 3: Exploratory Data Analysis (1): Central Tendency Measures

Lecture 10 Mean (Average)

Lecture 11 Weighted Average

Lecture 12 Median

Lecture 13 Population vs. Sample

Lecture 14 Application in Data Science

Lecture 15 Exercise: Calculate Central Tendency Measures

Section 4: Exploratory Data Analysis (2): Variability Measures

Lecture 16 Range

Lecture 17 Variance and Standard Deviation

Lecture 18 Percentile & Quartile

Lecture 19 Outlier – part 1

Lecture 20 Outlier – part 2

Lecture 21 Sampling Error

Lecture 22 Application in Data Science

Lecture 23 Exercise: Calculate Variability Measures

Section 5: Visualizing Data Distributions

Lecture 24 Box Plot

Lecture 25 Violin Plot

Lecture 26 Histogram and Density Plot

Lecture 27 Bar Plot for Categorical Data

Lecture 28 Pie Chart for Categorical Data

Lecture 29 Application in Data Science

Lecture 30 Exercise: Exploring Data Distribution

Section 6: Correlation, Scatterplots, and Heat Maps

Lecture 31 Correlation and Covariance Coefficients

Lecture 32 Correlation Using Scatter plot

Lecture 33 Mapping with Scatter plots

Lecture 34 Heat Maps

Lecture 35 Application in Data Science

Lecture 36 Exercise: Create Mapped Scatterplots and Heat Maps

Section 7: Capstone Project for Exploratory Analysis

Lecture 37 Project Description

Lecture 38 Solution walk-through of The Project

Section 8: Data Distributions and Data Sampling

Lecture 39 Random Sampling and Bias

Lecture 40 Central Limit Theorem

Lecture 41 Normal distribution

Lecture 42 Normality Tests for Real-World Data

Lecture 43 Skewed Data: Real-life Distributions

Lecture 44 Probability: A Practical Introduction

Lecture 45 Common Probability Distributions

Lecture 46 Exercise: Normal Distribution and Skewness

Section 9: Data Scaling and Transformation

Lecture 47 Data Scaling: Standardization

Lecture 48 Data Scaling: Normalization

Lecture 49 Log and Square Root Transformations

Lecture 50 Power Transformation (PowerTransformer)

Lecture 51 Application in Data Science

Lecture 52 Exercise: Data Scaling and Transformation

Section 10: Confidence Intervals (CI)

Lecture 53 C.I for Continuous Data

Lecture 54 C.I for Classification Data

Lecture 55 Bootstrapping For Unknown Distributions

Lecture 56 Nonparametric Confidence Interval with Bootstrapping

Lecture 57 Exercise: Create Confidence Interval

Section 11: Evaluation Metrics for Machine Learning

Lecture 58 Bias vs. Variance

Lecture 59 Overfitting and Underfitting

Lecture 60 Information Criteria for Model Selection

Lecture 61 Evaluation Metrics for Regression Models

Lecture 62 Evaluation Metrics for Classification Models _Part One

Lecture 63 Evaluation Metrics for Classification Models – Part Two

Lecture 64 Application in Data Science

Lecture 65 Exercise: Evaluating Machine Learning Models

Section 12: Model Validation Techniques in Machine Learning

Lecture 66 Hold Out Validation - Train/Test Split

Lecture 67 K-Fold Cross-Validation

Lecture 68 Leave-One-Out Cross-Validation (LOOCV)

Lecture 69 Application in Data Science

Lecture 70 Exercise: Validation Techniques in Machine Learning

Section 13: Final project

Lecture 71 Project Description

Lecture 72 Walk-through Solution of the Project – Part One

Lecture 73 Walk-through Solution of the Project – Part Two

Lecture 74 Walk-through Solution of the Project – Part Three

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