Data Science


Python for Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

4.61 (95796 reviews)



25 hours


May 2020

Last Update
Regular Price

What you will learn

Use Python for Data Science and Machine Learning

Use Spark for Big Data Analysis

Implement Machine Learning Algorithms

Learn to use NumPy for Numerical Data

Learn to use Pandas for Data Analysis

Learn to use Matplotlib for Python Plotting

Learn to use Seaborn for statistical plots

Use Plotly for interactive dynamic visualizations

Use SciKit-Learn for Machine Learning Tasks

K-Means Clustering

Logistic Regression

Linear Regression

Random Forest and Decision Trees

Natural Language Processing and Spam Filters

Neural Networks

Support Vector Machines


Are you ready to start your path to becoming a Data Scientist! 

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

  • Programming with Python
  • NumPy with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Web scraping with python
  • Connect Python to SQL
  • Use matplotlib and seaborn for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Neural Nets and Deep Learning
  • Support Vector Machines
  • and much, much more!

Enroll in the course and become a data scientist today!


Python for Data Science and Machine Learning Bootcamp
Python for Data Science and Machine Learning Bootcamp
Python for Data Science and Machine Learning Bootcamp
Python for Data Science and Machine Learning Bootcamp


Course Introduction

Introduction to the Course

Course Help and Welcome

Course FAQs

Environment Set-Up

Python Environment Setup

Jupyter Overview

Updates to Notebook Zip

Jupyter Notebooks

Optional: Virtual Environments

Python Crash Course

Welcome to the Python Crash Course Section!

Introduction to Python Crash Course

Python Crash Course - Part 1

Python Crash Course - Part 2

Python Crash Course - Part 3

Python Crash Course - Part 4

Python Crash Course Exercises - Overview

Python Crash Course Exercises - Solutions

Python for Data Analysis - NumPy

Welcome to the NumPy Section!

Introduction to Numpy

Numpy Arrays

Quick Note on Array Indexing

Numpy Array Indexing

Numpy Operations

Numpy Exercises Overview

Numpy Exercises Solutions

Python for Data Analysis - Pandas

Welcome to the Pandas Section!

Introduction to Pandas


DataFrames - Part 1

DataFrames - Part 2

DataFrames - Part 3

Missing Data


Merging Joining and Concatenating


Data Input and Output

Python for Data Analysis - Pandas Exercises

Note on SF Salary Exercise

SF Salaries Exercise Overview

SF Salaries Solutions

Ecommerce Purchases Exercise Overview

Ecommerce Purchases Exercise Solutions

Python for Data Visualization - Matplotlib

Welcome to the Data Visualization Section!

Introduction to Matplotlib

Matplotlib Part 1

Matplotlib Part 2

Matplotlib Part 3

Matplotlib Exercises Overview

Matplotlib Exercises - Solutions

Python for Data Visualization - Seaborn

Introduction to Seaborn

Distribution Plots

Categorical Plots

Matrix Plots


Regression Plots

Style and Color

Seaborn Exercise Overview

Seaborn Exercise Solutions

Python for Data Visualization - Pandas Built-in Data Visualization

Pandas Built-in Data Visualization

Pandas Data Visualization Exercise

Pandas Data Visualization Exercise- Solutions

Python for Data Visualization - Plotly and Cufflinks

Introduction to Plotly and Cufflinks

Plotly and Cufflinks

Python for Data Visualization - Geographical Plotting

Introduction to Geographical Plotting

Choropleth Maps - Part 1 - USA

Choropleth Maps - Part 2 - World

Choropleth Exercises

Choropleth Exercises - Solutions

Data Capstone Project

Welcome to the Data Capstone Projects!

911 Calls Project Overview

911 Calls Solutions - Part 1

911 Calls Solutions - Part 2

Bank Data

Finance Data Project Overview

Finance Project - Solutions Part 1

Finance Project - Solutions Part 2

Finance Project - Solutions Part 3

Introduction to Machine Learning

Welcome to the Machine Learning Section!

Link for ISLR

Supervised Learning Overview

Evaluating Performance - Classification Error Metrics

Evaluating Performance - Regression Error Metrics

Machine Learning with Python

Linear Regression

Linear Regression Theory

model_selection Updates for SciKit Learn 0.18

Linear Regression with Python - Part 1

Linear Regression with Python - Part 2

Linear Regression Project Overview

Linear Regression Project Solution

Cross Validation and Bias-Variance Trade-Off

Bias Variance Trade-Off

Logistic Regression

Logistic Regression Theory

Logistic Regression with Python - Part 1

Logistic Regression with Python - Part 2

Logistic Regression with Python - Part 3

Logistic Regression Project Overview

Logistic Regression Project Solutions

K Nearest Neighbors

KNN Theory

KNN with Python

KNN Project Overview

KNN Project Solutions

Decision Trees and Random Forests

Introduction to Tree Methods

Decision Trees and Random Forest with Python

Decision Trees and Random Forest Project Overview

Decision Trees and Random Forest Solutions Part 1

Decision Trees and Random Forest Solutions Part 2

Support Vector Machines

SVM Theory

Support Vector Machines with Python

SVM Project Overview

SVM Project Solutions

K Means Clustering

K Means Algorithm Theory

K Means with Python

K Means Project Overview

K Means Project Solutions

Principal Component Analysis

Principal Component Analysis

PCA with Python

Recommender Systems

Recommender Systems

Recommender Systems with Python - Part 1

Recommender Systems with Python - Part 2

Natural Language Processing

Natural Language Processing Theory

NLP with Python - Part 1

NLP with Python - Part 2

NLP with Python - Part 3

NLP Project Overview

NLP Project Solutions

Neural Nets and Deep Learning

Download TensorFlow Notebooks Here

Welcome to the Deep Learning Section!

Introduction to Artificial Neural Networks (ANN)

Installing Tensorflow

Perceptron Model

Neural Networks

Activation Functions

Multi-Class Classification Considerations

Cost Functions and Gradient Descent


TensorFlow vs Keras

TF Syntax Basics - Part One - Preparing the Data

TF Syntax Basics - Part Two - Creating and Training the Model

TF Syntax Basics - Part Three - Model Evaluation

TF Regression Code Along - Exploratory Data Analysis

TF Regression Code Along - Exploratory Data Analysis - Continued

TF Regression Code Along - Data Preprocessing and Creating a Model

TF Regression Code Along - Model Evaluation and Predictions

TF Classification Code Along - EDA and Preprocessing

TF Classification - Dealing with Overfitting and Evaluation

TensorFlow 2.0 Project Options Overview

TensorFlow 2.0 Project Notebook Overview

Keras Project Solutions - Dealing with Missing Data

Keras Project Solutions - Dealing with Missing Data - Part Two

Keras Project Solutions - Categorical Data

Keras Project Solutions - Data PreProcessing

Keras Project Solutions - Data PreProcessing

Keras Project Solutions - Creating and Training a Model

Keras Project Solutions - Model Evaluation


Big Data and Spark with Python

Welcome to the Big Data Section!

Big Data Overview

Spark Overview

Local Spark Set-Up

AWS Account Set-Up

Quick Note on AWS Security

EC2 Instance Set-Up

SSH with Mac or Linux

PySpark Setup

Lambda Expressions Review

Introduction to Spark and Python

RDD Transformations and Actions


Bonus Lecture


T9 October 2020

An informative, comprehensive and concise explanation of Python, albeit a bit rush for complete beginners.

Leon7 October 2020

The course is fairly good. It explains the basic theory of data science and gives you basic understanding of this world, and the technologies in it. I feel that Jose is one of the only people that is teaching these subjects in a fairly fast way that is understandable. I enjoyed the course and I feel that it educated me. The only thing that I would improve is to add a few lectures about some common topics that the course didn't address like how to pick a machine learning algorithm, how to choose the variables for the models and how to explore the data. I mean, all of this was mentioned in few words throughout the course but I feel that these topic should have a bit more emphasis and some explanations around, at least some rules of thumb or general thinking direction and not just 'I always like to do this check when I start exploring the data' Overall a good course and I feel that it was worth the money.

Kylene6 October 2020


Jonathan6 October 2020

I was almost a beginner in Python, however the gradual approach adopted for the topics presentation permitted me to properly learn some fundamental tools of Python. The approach to machine learning section is gradual as well, providing the necessary theoretichal tool to study and go deep into the math beyond the algorithm and the lybraries explained. However, the course mantains an application-focused and practical approch, consenting to immediately understand how to apply the presented tools. The final project at each session allowing to immediatly test yourself, also proiding the opportunity to build a small personal portfolio. It definitly worth the price I paid, for both the quality of the contents and the supporting service. Definitely reccommended to beginner and intermediate Python users who want to learn Machine Learning.

Ameya5 October 2020

I just want to say thanks to Jose for designing this course and for everything that I have learnt so far through his other course on Python.

Andy25 February 2020

This was an exceptional course, perfect for anyone with basic python skills who wants to build a Data Science foundation. Very well-organized and accessible course covering core python data structures, visualization libraries, and machine learning tools and concepts.

arnaud25 February 2020

Excellent course for beginners in ML. The course is well structured, explanations are clear, exercises are not so challenging but that's enough to apply the concepts exposed during the lectures. I'd also recommend reading the extra documentation : the ISLR book as suggested by Jose. That gives complementary information regarding the statiscal methods. Thanks!

Badri24 February 2020

The video instruction is very useful to learn about the Python environment. The instructor speaks a little fast, but that is a minor issue; I can watch the video again.

Chase23 February 2020

Didn't expect this course to be laid out so nicely. Everything so far has been planned out and instructions are beyond expectation! A+ job!

Fabio23 February 2020

Nice introduction, clear explanations, great examples, go over a significant material at a comfortable pace and goads you into making specific forays into frameworks documentation etc. Quite well organized material

D'enda22 February 2020

This course should be renamed 'python tools for data science'. Haven't learnted much data science but been told real good like how to pretty up a basic line plot. Kind of seems like the dude just looked at necessary python libaries for data science, youtube'd them shits and bundled them up. I'll use this course for a reference when I need to pretty up a line. Good on him for making a dime. Over 300k users, dude should start some Udemy infomercials on how to teach shit you don't know much about and make 3 million dollars doing it.

Subinoy22 February 2020

The basics are taken care of pretty quickly. That's a good thing about it. Being a experienced developer, I can just shoot through these very quickly and concentrate on the actual meaty parts.

Cristian22 February 2020

Awesome course, recommended to everyone and well structured. Jose Portilla thanks and congratulations for such a great job.

Lucero20 February 2020

Always happy to learn from Jose, I am actually learning more than what I learned in my master's. I will use all this new knowledge to pursue a new job position.

Ruchika17 February 2020

felt a bit confident while practicing it side ways on colab or jupiter notebook,nd well the respected sir is explaining quite effectively and smoothly


Expired6/28/201997% OFF


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