Data science is a multidisciplinary field. It encompasses a wide range of topics.

Understanding of the data science field and the type of analysis carried out

• Mathematics
• Statistics
• Python
• Applying advanced statistical techniques in Python
• Data Visualization
• Machine Learning
• Deep Learning

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.

Moreover, our focus is to teach topics that flow smoothly and complement each other.

Audience for this course:

This course is designed for anyone with basic understanding of working with a Computer. There is no major prerequisite.

•  What will you learn in this course
• Understanding the field of Data Science
• Various Disciplines of Data Science
• Data Analysis & Data Analytics
• Business Analytics, Data Analytics & Data Science
• Understanding Business Intelligence, Machine Learning & Artifical Intelligence
• Breaking down Data Science into simple terms

• Understanding Traditional Data
• Understanding  Big Data
• Understanding Traditional Data Science
• Understanding Machine Learning
• Understand the differences in multiple Data Science Fields
• Popular tools in Data Science
• Career in Data Science
• Common misconceptions in the Data Science Field

• Understanding basic probability
• Computing expected values
• Frequency
• Events and Complement

• Fundamentals of Combinatorics
• Permutations
• Simple operations
• Solving variations with repetition
• Solving variations without repetition
• Solving combinations
• Symmetry of combinations
• Seperate space samples
• Case Study: Combinatorics in real life

• Sets and Events
• Ways Sets can interact
• Sets Intersection
• Union of Sets
• Mutually exclusive Sets
• Dependence and Independence of Sets
• The Conditional Probability Formula
• The Law of Total Probability
• The Additive Rule
• The Multiplication Law
• Bayes’ Law
• Case Study: Bayesian Inference

• Fundamentals of Probability Distributions
• Types of Probability Distributions
• Characteristics of Discrete Distributions
• Discrete Distributions: The Uniform Distribution
• Discrete Distributions: The Bernoulli Distribution
• Discrete Distributions: The Binomial Distribution
• Discrete Distributions: The Poisson Distribution
• Characteristics of Continuous Distributions
• Characteristics of Continuous Distributions
• Continuous Distributions: The Normal Distribution
• Continuous Distributions: The Standard Normal Distribution
• Continuous Distributions: The Standard Normal Distribution
• Continuous Distributions: The Students’ T Distribution
• Continuous Distributions: The Chi-Squared Distribution
• Continuous Distributions: The Exponential Distribution
• Continuous Distributions: The Logistic Distribution
• Case Study: Probability Distributions

• Introduction to Statistics
• Types of Data
• Levels of Measurement
• Categorical Variables – Visualization Techniques
• Categorical Variables – Visualization Techniques
• Numerical Variables – Frequency Distribution Table
• The Histogram
• Cross Tables and Scatter Plots
• Mean, median and mode
• Skewness
• Variance
• Standard Deviation and Coefficient of Variation
• Covariance
• Correlation Coefficient
• Correlation Coefficient Exercise
• Case Study: Descriptive Statistics

• Introduction to Statistics
• Types of Data
• Levels of Measurement
• Categorical Variables – Visualization Techniques
• Categorical Variables – Visualization Techniques
• Numerical Variables – Frequency Distribution Table
• The Histogram
• Cross Tables and Scatter Plots
• Mean, median and mode
• Skewness
• Variance
• Standard Deviation and Coefficient of Variation
• Covariance
• Correlation Coefficient
• Correlation Coefficient Exercise
• Case Study: Descriptive Statistics

• Understanding Confidence Intervals
• Confidence Intervals; Population Variance Known; z-score
• Confidence Interval Clarifications
• Student’s T Distribution
• Confidence Intervals; Population Variance Unknown; t-score
• Margin of Error
• Confidence intervals. Two means. Dependent samples
• Confidence intervals. Two means. Independent samples
• Case Study: Inferential Statistics

• Null vs Alternative Hypothesis
• Rejection Region and Significance Level
• Type I Error and Type II Error
• Test for the Mean. Population Variance Known
• p-value
• Test for the Mean. Population Variance Unknown
• Test for the Mean. Dependent Samples
• Test for the mean. Independent samples
• Case Study: Hypothesis Testing

• Introduction to Programming
• Why Python?
• Why Jupyter?
• Installing Python and Jupyter
• Understanding Jupyter’s Interface – the Notebook Dashboard
• Prerequisites for Coding in the Jupyter Notebooks
• Python 2 vs Python 3

• Variables
• Numbers and Boolean Values in Python
• Python Strings
• Python – Basic Python Syntax
• Using Arithmetic Operators in Python
• The Double Equality Sign
• How to Reassign Values
• Add Comments
• Understanding Line Continuation
• Indexing Elements
• Structuring with Indentation

• Comparison Operators
• Logical and Identity Operators
• Python – Conditional Statements
• The IF Statement
• The ELSE Statement
• The ELIF Statement
• Boolean Values

• Defining a Function in Python
• How to Create a Function with a Parameter
• Defining a Function in Python – Part II
• How to Use a Function within a Function
• Conditional Statements and Functions
• Functions Containing a Few Arguments
• Built-in Functions in Python
• Python Functions

•  Lists
• Using Methods
• List Slicing
• Tuples
• Dictionaries
• For Loops
• While Loops and Incrementing
• Lists with the range() Function
• Conditional Statements and Loops
• Conditional Statements, Functions, and Loops
• How to Iterate over Dictionaries

• Object Oriented Programming
• Modules and Packages
• What is the Standard Library?
• Importing Modules in Python

   

• The Linear Regression Model
• Correlation vs Regression
• Geometrical Representation of the Linear
  Regression Model
• Python Packages Installation

• First Regression in Python
• Using Seaborn for Graphs
• How to Interpret the Regression Table
• Decomposition of Variability
• What is the OLS?
• R-Squared

• Multiple Linear Regression
• Adjusted R-Squared
• Test for Significance of the Model
• OLS Assumptions
• A1: Linearity
• A2: No Endogeneity
• A3: Normality and Homoscedasticity
• A4: No Autocorrelation
• A5: No Multicollinearity
• Dealing with Categorical Data – Dummy Variables
• Making Predictions with the Linear Regression

• What is sklearn and How is it Different from Other Packages
• Simple Linear Regression with sklearn
• Simple Linear Regression with sklearn – A StatsModels-like Summary Table
• Simple Linear Regression with sklearn –
• Multiple Linear Regression with sklearn
• Calculating the Adjusted R-Squared in sklearn
• Feature Selection (F-regression)
• Creating a Summary Table with p-values
• Feature Scaling (Standardization)
• Feature Selection through Standardization of Weights
• Predicting with the Standardized Coefficients
• Underfitting and Overfitting
• Train – Test Split Explained

• Introduction to Logistic Regression
• A Simple Example in Python
• Logistic vs Logit Function
• Building a Logistic Regression
• Understanding Logistic Regression Tables
• Binary Predictors in a Logistic Regression
• Calculating the Accuracy of the Model
• Underfitting and Overfitting
• Testing the Model

• Introduction to Cluster Analysis
• Some Examples of Clusters
• Difference between Classification and Clustering
• Math Prerequisites

• K-Means Clustering

• A Simple Example of Clustering

• Clustering Categorical Data

• How to Choose the Number of Clusters

• Pros and Cons of K-Means Clustering

• To Standardize or not to Standardize

• Relationship between Clustering and Regression

• Market Segmentation with Cluster Analysis

• How is Clustering Useful?

• What is a matrix?

• Scalars and Vectors

• Linear Algebra and Geometry

• Arrays in Python – A Convenient Way To Represent Matrices

• What is a Tensor?

• Addition and Subtraction of Matrices

• Errors when Adding Matrices

• Transpose of a Matrix

• Dot Product

• Dot Product of Matrices

• Why is Linear Algebra Useful?

• Introduction to Neural Networks

• Training the Model

• Types of Machine Learning

• The Linear Model (Linear Algebraic Version)

• The Linear Model with Multiple Inputs

• The Linear model with Multiple Inputs and Multiple Outputs

• Graphical Representation of Simple Neural Networks

• What is the Objective Function?

• Common Objective Functions: L2-norm Loss

• Common Objective Functions: Cross-Entropy Loss

• Optimization Algorithm: 1-Parameter Gradient Descent

• Optimization Algorithm: n-Parameter Gradient Descent

• How to Install TensorFlow 2.0

• TensorFlow Outline and Comparison with Other Libraries

• TensorFlow 1 vs TensorFlow 2

• Types of File Formats Supporting TensorFlow

• Outlining the Model with TensorFlow 2

• Interpreting the Result and Extracting the Weights and Bias

• Customizing a TensorFlow 2 Model

• What is a Layer?

• What is a Deep Net?

• Digging into a Deep Net

• Non-Linearities and their Purpose

• Activation Functions

• Activation Functions: Softmax Activation

• Backpropagation

• Backpropagation picture

• Backpropagation – A Peek into the
  Mathematics of Optimization

• What is Overfitting?

• Underfitting and Overfitting for Classification

• What is Validation?

• Training, Validation, and Test Datasets

• N-Fold Cross Validation

• Early Stopping or When to Stop Training

• Stochastic Gradient Descent

• Problems with Gradient Descent

• Momentum

• Learning Rate Schedules, or How to Choose the Optimal Learning Rate

• Learning Rate Schedules Visualized

• Adaptive Learning Rate Schedules (AdaGrad and RMSprop )

• Adam (Adaptive Moment Estimation)

• Preprocessing Introduction

• Types of Basic Preprocessing

• Standardization

• Preprocessing Categorical Data

• Binary and One-Hot Encoding

• MNIST: The Dataset

• MNIST: How to Tackle the MNIST

• MNIST: Importing the Relevant Packages and Loading the Data

• MNIST: Preprocess the Data – Create a Validation Set and Scale It

• MNIST: Preprocess the Data – Shuffle and Batch

• MNIST: Outline the Model

• MNIST: Select the Loss and the Optimizer

• MNIST: Learning

• MNIST: Testing the Model

• Business Case: Exploring the Dataset and Identifying Predictors

• Business Case: Outlining the Solution

• Business Case: Balancing the Dataset

• Business Case: Preprocessing the Data

• Business Case: Load the Preprocessed Data

• Business Case: Learning and Interpreting the Result

• Business Case: Setting an Early Stopping Mechanism

• Business Case: Testing the Model

• Summary on What You’ve Learned

• What’s Further out there in terms of Machine Learning

• DeepMind and Deep Learning

• An overview of CNNs

• An Overview of RNNs

• An Overview of non-NN Approaches

• What are Data, Servers, Clients, Requests, and Responses

• What are Data Connectivity, APIs, and Endpoints?

• Taking a Closer Look at APIs

• Communication between Software Products through Text Files

• Software Integration – Explained

• Case Study – Preprocessing the ‘Absenteeism_data’
• Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
• Case Study – Loading the ‘absenteeism_module’
• Case Study – Analyzing the Predicted Outputs