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Learn Python for Data Science & Machine Learning from A-Z (Udemy.com)

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

Created by: Juan E. Galvan

Last updated January 2022

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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

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.3 out of 5higher than 17% of them
  • Reviews1,826#98 of 462 by number of reviews
  • Length22.9 hourslonger than 84% of them
  • Last updatedJanuary 2022earlier than 87% of them

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Course Description

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:

1: PYTHON FOR DS+ML COURSE INTRO

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

2: PYTHON DATA ANALYSIS/VISUALIZATION

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

3: MATHEMATICS FOR DATA SCIENCE

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

4:  MACHINE LEARNING

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

5: STARTING A DATA SCIENCE CAREER

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.

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Instructor Details

Juan E. Galvan

Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets.

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Reviews

4.3

1,826 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Shahzaib on 7/31/2026

This is an excellent course for anyone who wants to learn Python from A to Z for Data Science. The instructor explains concepts clearly and practically, making the course easy to follow even for beginners. The hands-on examples and practical exercises helped me understand Python programming, data analysis, and essential data science tools more effectively.

By Akshat on 1/5/2026

the thing is this app and the course that they provide are actually too good it's just one need to learn and watch and apply the things that they learn and if they do so it's enough similar to college degree, except that degree obv

By Joseph Barss on 7/4/2023

The course explains several python functions and packages very well, and also touches on statistical theory and professional development tips. The explanations of machine learning algorithms is in-depth, but the code files are available to download, which is very helpful.

By Swagat Datta Mazumder on 5/1/2023

The course was great and precise, it was exactly what I was looking for to upskill in a shorter span of time. Had a great learning experience on python and machine learning through this course. Would definitely recommend this to someone who wants to start out in the field of machine learning and python.

By Sael Flores on 1/20/2023

Buen curso, bastante contenido teórico y avanzado ya que explican como funcionan algunos algoritmos usados para analizar conjuntos. En cuanto a python aprendí librerías, funciones y sintaxis que no conocía. Contiene material interesante que puedes consultar en otros proyectos. en general buen curso y mas por el precio ya que lo obtuve gratis.

By Desty Muturi on 12/27/2022

I liked this class. It did a great job covering Python basics as well as the fundamentals in statistics, linear, algebra, and calculus. Several supervised learning ML algorithms were covered, as well as an overview of clustering and dimensionality reduction stuff for unsupervised learning. As a total novice in machine learning (but with a couple of years of data science work under my belt), I feel confident enough to head out from this course and build my own projects to buff up my resume before I apply for jobs. Fantastic! The only reason I didn't give this a full 5 stars is because of some missing course materials mentioned by others here. Also, some concepts were explained at a level too basic to understand subsequent lectures, so had to do a lot of YouTube and Google to fill in the blank. I expected to do that anyways though, so nothing too bad about that. Overall, I recommend this course if you are a beginner and willing to put in the work to get to the end. Thank you for this, much love and appreciation guys!

By Oscar Suarez Untiveros on 9/3/2021

I would like to include more libraries like Tensorflow and Cloud tools like Google Cloud Platform and integration with big data like SQL for data science with IBM db2 engine for example. I took EDX SQL for Data Science sincerely I would like to see a course seems like this here.

By Gerardo Luis Padilla on 8/24/2021

Buen curso en una vista general. Abarca muchos temas lo cual ayuda a tener un buen panorama del tema central en cuestión. Pero las clases están algo desordenadas y faltan varios proyectos mencionados. Pero en definitiva fue una gran experiencia y me servirá mucho

By John Ndungu on 8/10/2021

Yes this course is a good match for me. However, the mathematical concepts were explained too quickly so I'll have to do my own further studies to grasp those concepts properly.

By Martin carbonell on 7/23/2021

I'm getting a deeper understanding of what machine learning means, beyond the concept. You'll leave this course with a pretty good grasp of the steps, pros and cons, and techniques used in machine learning, and even you'd be able to choose the best model for your project. But I think you wont be able to implement it from scratch by only taking this course. Its a good kickstart to further learning. You dont get the notebooks used during the course, the good part is that you have to code everithing yourself and its part of the learning process. The bad part is that if you get some bug and you dont know how to fix it, you dont have anywhere to look up for the right code. I couldnt run the decision trees models from scratch for example, because I've got stuck with bugs. Thats the only thing I dindt like.

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Overall Score : 86 / 100