Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!

What you will learn

Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant

Learn data cleaning, processing, wrangling and manipulation

How to create resume and land your first job as a Data Scientist

How to use Python for Data Science

How to write complex Python programs for practical industry scenarios

Learn Plotting in Python (graphs, charts, plots, histograms etc)

Learn to use NumPy for Numerical Data

Machine Learning and it’s various practical applications

Supervised vs Unsupervised Machine Learning

Learn Regression, Classification, Clustering and Sci-kit learn

Machine Learning Concepts and Algorithms

K-Means Clustering

Use Python to clean, analyze, and visualize data

Building Custom Data Solutions

Statistics for Data Science

Probability and Hypothesis Testing


Learn Python for Data Science & Machine Learning from A-Z

In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

We’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +

  • NumPy —  A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.
  • Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.

This Machine Learning with Python course dives into the basics of machine learning using Python. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!

Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.

The course covers 5 main areas:


This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.

  • Intro to Data Science + Machine Learning with Python
  • Data Science Industry and Marketplace
  • Data Science Job Opportunities
  • How To Get a Data Science Job
  • Machine Learning Concepts & Algorithms


This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.

  • Python Crash Course
  • NumPy Data Analysis
  • Pandas Data Analysis


This section gives you a full introduction to the mathematics for data science such as statistics and probability.

  • Descriptive Statistics
  • Measure of Variability
  • Inferential Statistics
  • Probability
  • Hypothesis Testing


This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.

  • Intro to Machine Learning
  • Data Preprocessing
  • Linear Regression
  • Logistic Regression
  • K-Nearest Neighbors
  • Decision Trees
  • Ensemble Learning
  • Support Vector Machines
  • K-Means Clustering
  • PCA


This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

  • Creating a Resume
  • Creating a Cover Letter
  • Personal Branding
  • Freelancing + Freelance websites
  • Importance of Having a Website
  • Networking

By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.





Who is This Course For?

Data Science + Machine Learning Marketplace

Data Science Job Opportunities

Data Science Job Roles

What is a Data Scientist?

How To Get a Data Science Job

Data Science Projects Overview

Data Science & Machine Learning Concepts

Why We Use Python?

What is Data Science?

What is Machine Learning?

Machine Learning Concepts & Algorithms

What is Deep Learning?

Machine Learning vs Deep Learning

Python For Data Science

What is Programming?

Why Python for Data Science?

What is Jupyter?

What is Google Colab?

Python Variables, Booleans and None

Getting Started with Google Colab

Python Operators

Python Numbers & Booleans

Python Strings

Python Conditional Statements

Python For Loops and While Loops

Python Lists

More about Lists

Python Tuples

Python Dictionaries

Python Sets

Compound Data Types & When to use each one?

Python Functions

Object Oriented Programming in Python

Statistics for Data Science

Intro To Statistics

Descriptive Statistics

Measure of Variability

Measure of Variability Continued

Measures of Variable Relationship

Inferential Statistics

Measure of Asymmetry

Sampling Distribution

Probability & Hypothesis Testing

What is Exactly is Probability?

Expected Values

Relative Frequency

Hypothesis Testing Overview

NumPy Data Analysis

Intro NumPy Array Data Types

NumPy Arrays

NumPy Arrays Basics

NumPy Array Indexing

NumPy Array Computations


Pandas Data Analysis

Introduction to Pandas

Introduction to Pandas Continued

Python Data Visualization

Data Visualization Overview

Different Data Visualization Libraries in Python

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Python Data Visualization Implementation

Machine Learning

Introduction To Machine Learning

Data Loading & Exploration

Exploratory Data Analysis

Data Cleaning

Feature Scaling

Data Cleaning

Feature Selecting and Engineering

Feature Engineering

Linear and Logistic Regression

Linear Regression Intro

Gradient Descent

Linear Regression + Correlation Methods

Linear Regression Implementation

Logistic Regression

K Nearest Neighbors

KNN Overview

parametric vs non-parametric models

EDA on Iris Dataset

The KNN Intuition

Implement the KNN algorithm from scratch

Compare the result with the sklearn library

Hyperparameter tuning using the cross-validation

The decision boundary visualization

Manhattan vs Euclidean Distance

Feature scaling in KNN

Curse of dimensionality

KNN use cases

KNN pros and cons

Decision Trees

Decision Trees Section Overview

EDA on Adult Dataset

What is Entropy and Information Gain?

The Decision Tree ID3 algorithm from scratch Part 1

The Decision Tree ID3 algorithm from scratch Part 2

The Decision Tree ID3 algorithm from scratch Part 3

ID3 – Putting Everything Together

Evaluating our ID3 implementation

Compare with Sklearn implementation

Visualizing the tree

Plot the features importance

Decision Trees Hyper-parameters


[Optional] Gain Ration

Decision Trees Pros and Cons

[Project] Predict whether income exceeds $50K/yr – Overview

Ensemble Learning and Random Forests

Ensemble Learning Section Overview

What is Ensemble Learning?

What is Bootstrap Sampling?

What is Bagging?

Out-of-Bag Error (OOB Error)

Implementing Random Forests from scratch Part 1

Implementing Random Forests from scratch Part 2

Compare with sklearn implementation

Random Forests Hyper-Parameters

Random Forests Pros and Cons

What is Boosting?

AdaBoost Part 1

AdaBoost Part 2

Support Vector Machines

SVM Outline

SVM intuition

Hard vs Soft Margins

C hyper-parameter

Kernel Trick

SVM – Kernel Types

SVM with Linear Dataset (Iris)

SVM with Non-linear Dataset

SVM with Regression

SMV – Project Overview


Unsupervised Machine Learning Intro

Unsupervised Machine Learning Continued

Representing Clusters


PCA Section Overview

What is PCA?

PCA Drawbacks

PCA Algorithm Steps (Mathematics)

Covariance Matrix vs SVD

PCA – Main Applications

PCA – Image Compression

PCA Data Preprocessing

PCA – Biplot and the Screen Plot

PCA – Feature Scaling and Screen Plot

PCA – Supervised vs Unsupervised

PCA – Visualization

Data Science Career

Creating A Data Science Resume

Data Science Cover Letter

How to Contact Recruiters

Getting Started with Freelancing

Top Freelance Websites

Personal Branding

Networking Do’s and Don’ts

Importance of a Website