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Section 1: Introduction
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Practical Example: What You Will Learn in This Course
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What the Course Covers
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Download All Resources
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The Data Science Field – Various Data Science Disciplines
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Data Science and Business Buzzwords: Why Are There So Many?
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Business Analysis, Data Analysis, and Data Science: An Introduction
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Continuing with BI, ML, and AI
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Our Data Science Infographic Details
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The Data Science Field – Connecting Data Science Disciplines
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Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
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The Data Science Field – Benefits of Each Discipline
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The Reasoning Behind These Disciplines
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The Data Science Field – Popular Data Science Techniques
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Techniques for Working with Traditional Data
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Real-World Examples of Traditional Data
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Techniques for Working with Big Data
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Real-World Examples of Big Data
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Business Intelligence (BI) Techniques
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Real-World Examples of Business Intelligence (BI)
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Techniques for Working with Traditional Methods
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Real-World Examples of Traditional Methods
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Machine Learning (ML) Techniques
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Types of Machine Learning
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Real-World Examples of Machine Learning (ML)
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The Data Science Field – Popular Data Science Tools
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Programming Languages and Software Required Used in Data Science
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The Data Science Field – Careers in Data Science
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Finding a Job – What to Expect and What to Look Out For
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The Data Science Field – Debunking Common Misconceptions
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Denying Common Misconceptions
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Section 2: Statistics
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Population and Sample
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Statistics – Descriptive Statistics
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Types of Data
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Levels of Measurement
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Categorical Variables – Visualization Techniques
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Categorical Variables Exercise
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Numerical Variables – Frequency Distribution Table
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Numerical Variables Exercise
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Histogram
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Histogram Exercise
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Cross Tables and Scatter Plot
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Cross Tables and Scatter Plot Exercise
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Mean, Median and Mode
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Mean, Median and Mode Exercise
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Skewness
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Skewness Exercise
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Variance
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Variance Exercise
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Standard Deviation and Coefficient of Variation
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Standard Deviation
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Standard Deviation and Coefficient of Variation Exercise
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Covariance
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Covariance Exercise
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Coefficient of Correlation
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Correlation
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Coefficient of Correlation Exercise
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Statistics – Practical Example: Descriptive Statistics
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Practical Example: Descriptive Statistics
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Practical Example: Descriptive Statistics Exercise
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Statistics – Fundamentals of Inferential Statistics
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What is a Distribution
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Normal Distribution
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Standard Normal Distribution
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Standard Normal Distribution Exercise
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Central Limit Theorem
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Standard Error
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Estimator and Estimation
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Statistics – Inferential Statistics: Confidence Intervals
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What is a Confidence Interval?
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Confidence Interval; Known Population Variance; z-score
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Confidence Interval; Known Population Variance; z-score; Sports
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Confidence Interval Clarification
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Student's t Distribution
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Confidence Interval; Unknown Population Variance; t-score
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Confidence Interval; Unknown Population Variance; t-score; Sports
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Margin of Error
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Confidence Interval. Two Means. Dependent Sample
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Confidence Interval. Two Means. Dependent Sample Exercise
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Confidence Interval. Two Means. Independent Sample (Part 1)
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Confidence Interval. Two Means. Independent Sample (Part 1) Exercise
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Confidence Interval. Two Means. Independent Sample (Part 2)
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Confidence Interval. Two Means. Independent Sample (Part 2) Exercise
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Confidence Interval. Two Means. Independent Sample (Part 3)
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Statistics – Practical Example: Inferential Statistics
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Practical Example: Inferential Statistics
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Practical Example: Inferential Statistics Exercise
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Statistics – Hypothesis Testing
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Null vs Alternative Hypothesis
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Further Reading on Null and Alternative Hypothesis
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Null vs Alternative Hypothesis
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Rejection Region and Significance Level
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Type I Error and Type II Error
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Test for Mean. Population Variance Known
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Test for Mean. Population Variance Known Exercise
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Test for Mean. Population Variance Unknown
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Test for Mean. Population Variance Unknown Exercise
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Test for Mean. Dependent Sample
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Test for Mean. Dependent Sample Exercise
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Test for Mean. Independent Sample (Part 1)
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Test for Mean. Independent Sample (Part 1). Exercise
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Test for Mean. Independent Sample (Part 2)
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Test for Mean. Independent Sample (Part 2)
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Test for Mean. Independent Sample (Part 2) Exercise
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Statistics – Practical Example: Hypothesis Testing
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Practical Example: Hypothesis Testing
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Practical Example: Hypothesis Testing Exercise
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Section 3: Introduction to Python
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Introduction to Programming
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Why Python
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Why Jupyter?
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Installing Python and Jupyter
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Understanding the Jupyter Interface – Notebook Dashboard
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Prerequisites for Coding in Jupyter Notebook
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Jupyter Interface
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Python – Variables and Data Types
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Variables
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Numbers and Boolean Values with Python
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Python Strings
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Python – Basic Python Syntax
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Using Arithmetic Operators with Python
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Double Equal Sign
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How to Reassign Values
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Understanding Line Continuation
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Indexing Elements
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Indentation for Grouping
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Python – Other Python Operators
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Comparison Operators
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Logical and Identity Operators
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Python – Conditional Statements
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IF Statement
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ELSE Statement
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ELIF Statement
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Notes on Boolean Values
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Python – Python Functions
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Defining a Function with Python
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How to Create a Function with Parameters
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Defining a Function with Python – Part II
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How to Use a Function Within a Function
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Conditional Statements and Functions
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Functions Containing Multiple Arguments
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Built-In Functions in Python
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Python Functions
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Python – Sequences
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Using Methods
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Slicing Lists
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Tuples
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Dictionaries
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Python – Iteration
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For Loops
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While Loops and Incrementing
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Lists with range() Function
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Conditional Statements and Loops
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Conditional Statements, Functions, and Loops
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How to Iterate over Dictionaries
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Python – Advanced Python Tools
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Object-Oriented Programming
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Modules and Packages
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What is the Standard Library?
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Importing Modules with Python
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Section 4: Advanced Statistical Methods in Python
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Introduction to Regression Analysis
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Advanced Statistical Methods – Linear Regression
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Linear Regression Model
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Correlation vs Regression
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Geometric Representation of the Linear Regression Model
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Installing Python Packages
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First Regression with Python
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First Regression Exercise with Python
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Using Seaborn for Graphics
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How to Interpret a Regression Table
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Decomposition of Variability
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What is OLS?
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R-Squared
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Advanced Statistical Methods – Multiple Linear Regression
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Multiple Linear Regression
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Adjusted R-Squared
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Multiple Linear Regression Exercise
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Model Significance Test (F-Test)
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OLS Assumptions
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A1: Linearity
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A2: No Endogeneity
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A3: Normality and Homoscedasticity
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A4: No Autocorrelation
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A4: No Autocorrelation
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A5: No Multicollinearity
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A5: No Multicollinearity
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Dealing with Categorical Data – Dummy Variables
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Making Predictions with Linear Regression
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Advanced Statistical Methods – Logistic Regression
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Introduction to Logistic Regression
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Simple Example with Python
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Logistic Function vs. Logit
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Building Logistic Regression
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Building Logistic Regression – Exercise
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Invaluable Encoding Tips
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Understanding the Logistic Regression Table
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Understanding the Logistic Regression Table – Exercise
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What Odds Really Mean
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Binary Predictor in Logistic Regression
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Binary Predictor in Logistic Regression – Exercise
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Calculating Model Accuracy
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Underfitting and Overfitting
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Testing the Model
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Testing the Model – Exercise
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Advanced Statistical Methods – Cluster Analysis
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Introduction to Cluster Analysis
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Some Cluster Examples
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Difference Between Classification and Clustering
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Mathematical Prerequisites
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Advanced Statistical Methods – K-Means Clustering
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K-Means Clustering
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Simple Example of Clustering
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Simple Example of Clustering – Exercise
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Clustering Categorical Data
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Clustering Categorical Data
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How to Choose the Number of Clusters
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How to Choose the Number of Clusters – Exercise
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Pros and Cons of K-Means Clustering
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To Standardize or Not to Standardize
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Relationship Between Clustering and Regression
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Market Segmentation with Cluster Analysis (Part 1)
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Market Segmentation with Cluster Analysis (Part 2)
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How is Clustering Useful?
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EXERCISE: Species Segmentation with Cluster Analysis (Part 1)
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EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
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Advanced Statistical Methods – Other Types of Clustering
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Types of Clustering
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Dendrogram
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Heatmaps
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Section 5: Mathematics
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What is a Matrix?
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Scalars and Vectors
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Linear Algebra and Geometry
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Arrays with Python – A Convenient Way to Represent Matrices
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What is a Tensor?
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Matrix Addition and Subtraction
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Errors When Adding Matrices
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Transpose of a Matrix
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Dot Product
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Why is Linear Algebra Useful?
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Section 6: Deep Learning
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What to Expect from This Section?
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What is Machine Learning
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Deep Learning – Introduction to Neural Networks
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Introduction to Artificial Neural Networks
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Training the Model
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Types of Machine Learning
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Linear Model (Linear Algebra Version)
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Linear Model with Many Inputs
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Linear Model with Many Inputs and Many Outputs
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Graphical Representation of a Simple Artificial Neural Network
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What is an Objective Function?
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Common Objective Function: L2-norm Loss
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Common Objective Function: Cross Entropy
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Optimization Algorithm: 1-Parameter Gradient Descent
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Optimization Algorithm: n-Parameter Gradient Descent
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Deep Learning – How to Build an Artificial Neural Network from Scratch with NumPy
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Basic NN Example (Part 1)
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Basic NN Example (Part 2)
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Basic NN Example (Part 3)
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Basic NN Example (Part 4)
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Basic NN Example Exercise
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Deep Learning – TensorFlow: Introduction
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How to Install TensorFlow
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Notes on Installing Packages in Anaconda
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TensorFlow Outline and Logic
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Actual Introduction to TensorFlow
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File Format Types, Supported Tensors
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Basic NN Example with TF: Input, Output, Target, Weights, Bias
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Basic NN Example with TF: Loss Function and Gradient Descent
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Basic NN Example with TF: Model Output
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Basic NN Example with TF Exercise
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Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
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What is a Layer?
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What is a Deep Net?
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Digging into a Deep Net
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Non-Linearity and Its Purpose
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Activation Functions
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Activation Functions: Softmax Activation
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Backpropagation
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Visualizing Backpropagation
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Backpropagation – Peeking at the Mathematics of Optimization
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Deep Learning – Overfitting
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What is Overfitting?
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Underfitting and Overfitting for Classification
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What is Validation?
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Training, Validation, and Test Data Sets
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N-Fold Cross-Validation
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Early Stopping or When to Stop Training
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Deep Learning – Initialization
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What is Initialization?
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Simple Initialization Types
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Advanced Methods – Glorot (Xavier) Initialization
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Deep Learning – Exploring Gradient Descent and Learning Rate Schedules
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Stochastic Gradient Descent
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Problems with Gradient Descent
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Momentum
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Learning Rate Schedule, or How to Choose the Optimal Learning Rate
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Learning Rate Schedule Visualized
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Adaptive Learning Rate Schedule (AdaGrad and RMSprop)
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Adam (Adaptive Moment Estimation)
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Deep Learning – Preprocessing
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Introduction to Preprocessing
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Types of Basic Preprocessing
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Standardization
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Categorical Data Preprocessing
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Binary and One-Hot Encoding
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Deep Learning – Classification on the MNIST Dataset
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MNIST: What is the MNIST Dataset?
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MNIST: How to Handle MNIST
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MNIST: Relevant Packages
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MNIST: Model Outline
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MNIST: Loss and Optimization Algorithm
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Calculating Model Accuracy
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MNIST: Batching and Early Stopping
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MNIST: Learning
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MNIST: Results and Testing
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MNIST: Exercise
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MNIST: Solution
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Deep Learning – Business Case Example
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Business Case: Getting Acquainted with the Dataset
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Business Case: Breaking Down the Solution
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The Importance of Working with Balanced Datasets
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Business Case: Preprocessing
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Business Case: Preprocessing Exercise
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Creating a Data Provider
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Business Case: Model Outline
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Business Case: Optimization
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Business Case: Interpretation
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Business Case: Testing the Model
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Business Case: Comments on Homework
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Business Case: Final Exercise
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Deep Learning – Conclusion
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Summary of What You Have Learned
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What Lies Beyond in Terms of Machine Learning
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CNN Overview
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DeepMind and Deep Learning
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RNN Overview
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Overview of Non-NN Approaches
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Download All Resources
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Software Integration
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What are Data, Server, Client, Request, and Response
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What are Data Connectivity, API, and Endpoints?
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Taking a Closer Look at APIs
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Communication Between Software Products via Text Files
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Software Integration
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Case Study – What's Next in the Course?
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Game Plan for This Python, SQL, and Tableau Business Exercise
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Business Task
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Introducing the Dataset
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Case Study – Preprocessing the 'Absenteeism_data'
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What to Expect from the Following Section?
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Importing Absenteeism Data with Python
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Checking the Dataset Contents
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Introduction to Terms with Multiple Meanings
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What is Regression Analysis – A Quick Refresh
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Using a Statistical Approach Toward the Solution Exercise
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Dropping Columns from a DataFrame with Python
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EXERCISE – Dropping Columns from a DataFrame with Python
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SOLUTION – Dropping Columns from a DataFrame with Python
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Analyzing the Reasons for Missing Values
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Obtaining Dummies from a Single Feature
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EXERCISE – Obtaining Dummies from a Single Feature
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SOLUTION – Getting Dummies from a Single Feature
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Dropping Dummy Variables from the Dataset
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More on Dummy Variables: A Statistical Perspective
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Classifying Various Reasons for Absence
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Using .concat() in Python
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EXERCISE – Using .concat() with Python
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SOLUTION – Using .concat() with Python
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Rearranging Columns in a Pandas DataFrame with Python
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EXERCISE – Rearranging Columns in a Pandas DataFrame with Python
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SOLUTION – Rearranging Columns in a Pandas DataFrame with Python
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Creating Checkpoints While Coding in Jupyter
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EXERCISE – Creating Checkpoints While Coding in Jupyter
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SOLUTION – Creating Checkpoints While Coding in Jupyter
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Analyzing Dates from the Initial Dataset
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Extracting Month Values from the "Date" Column
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Extracting the Day of the Week from the "Date" Column
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EXERCISE – Removing the "Date" Column
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Analyzing Several "Reason" Columns for This Exercise
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Working on "Education", "Children", and "Pets"
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Final Notes from This Section
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Case Study – Applying Machine Learning to Create the 'absenteeism_module'
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Exploring the Problem with a Machine Learning Mindset
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Creating a Target for Logistic Regression
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Selecting Inputs for Logistic Regression
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Standardizing the Data
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Separating Data for Training and Testing
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Fitting the Model and Assessing Its Accuracy
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Creating a Summary Table with Coefficients and Intercept
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Interpreting Coefficients for Our Problem
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Standardizing Only Numerical Variables (Creating a Custom Scaler)
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Interpreting Logistic Regression Coefficients
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Backward Elimination or How to Simplify Your Model
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Testing the Model We Built
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Saving the Model and Preparing It for Use
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ARTICLE – Notes on 'pickle'
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EXERCISE – Saving the Model (and Scaler)
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Preparing for Model Deployment via Module
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Case Study – Loading the 'absenteeism_module'
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Are You Sure You're Ready?
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Deploying the 'absenteeism_module' – Part I
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Deploying the 'absenteeism_module' – Part II
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Exporting the Derived Dataset as *.csv
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Case Study – Analyzing the Predicted Output in Tableau
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EXERCISE – Age vs Probability
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Analyzing Age vs Probability in Tableau
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EXERCISE – Reason vs Probability
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Analyzing Reason vs Probability in Tableau
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EXERCISE – Transportation Cost vs Probability
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Analyzing Transportation Cost vs Probability in Tableau
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Finished
