data science

In the Learn Python for Data Science & Machine Learning from A-Z course, students will learn how to program using Python for Data Science and Machine Learning. The course includes how to analyze and visualize data as well as how to use it in a useful way.

The major goal of the instructor is to provide the education needed to become a professional Data Scientist with Python and understand the fundamentals of Python programming for Data Science and Machine Learning. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Learn Python for Data Science & Machine Learning from A-Z Course for INR 449.

Who all can opt for this course?

  • Students interested in learning about Python for Machine Learning & Data Science

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration22 hours
Student Enrollment112,438 students
InstructorJuan E. Galvan
Topics CoveredData Science, Machine Learning, Python and Statistics for Data Science, NumPy, Pandas, etc.
Course LevelBeginner
Total Student Reviews1,639

Learning Outcomes

  • Become a certified consultant, data scientist, data engineer, or data analyst
  • Learn how to manipulate, clean, process, and wrangle data
  • Creating a résumé and getting your first job as a Data Scientist
  • Python for Data Science
  • How to develop sophisticated Python applications for real-world business scenarios
  • Python plotting tutorial (graphs, charts, plots, histograms, etc)
  • Use NumPy to handle numerical data
  • Practical applications of Machine Learning
  • Differentiate between Supervised and Unsupervised Machine Learning
  • Understand Regression, Classification, Clustering and Sci-kit learn
  • Concepts of Machine Learning & Algorithms
  • K-Means clustering
  • Use Python to organize, examine, and display data
  • Building custom Data Solutions
  • Statistics for Data Science
  • Probability & hypotheses testing

Course Content

S.No.Module (Duration)Topics
1.Introduction (01 hour 11 minutes)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
2.Data Science & Machine Learning Concepts (01 hour 06 minutes)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
3.Python For Data Science (03 hours 35 minutes)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
4.Statistics for Data Science (01 hour 08 minutes)Intro To Statistics
Descriptive Statistics
Measure of Variability
Measure of Variability Continued
Measures of Variable Relationship
Inferential Statistics
Measure of Asymmetry
Sampling Distribution
5.Probability & Hypothesis Testing (20 minutes)What Exactly is Probability?
Expected Values
Relative Frequency
Hypothesis Testing Overview
6.NumPy Data Analysis (52 minutes)Intro NumPy Array Data Types
NumPy Arrays
NumPy Arrays Basics
NumPy Array Indexing
NumPy Array Computations
7.Pandas Data Analysis (33 minutes)Introduction to Pandas
Introduction to Pandas Continued
8.Python Data Visualization (46 minutes)Data Visualization Overview
Different Data Visualization Libraries in Python
Python Data Visualization Implementation
9.Machine Learning (26 minutes)Introduction To Machine Learning
10.Data Loading & Exploration (13 minutes)Exploratory Data Analysis
11.Data Cleaning (15 minutes)Feature Scaling
Data Cleaning
12.Feature Selecting and Engineering (06 minutes)Feature Engineering
13.Linear and Logistic Regression (49 minutes)Linear Regression Intro
Gradient Descent
Linear Regression + Correlation Methods
Linear Regression Implementation
Logistic Regression
14.K Nearest Neighbors (01 hour 36 minutes)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
15.Decision Trees (02 hours 51 minutes)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 the 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
16.Ensemble Learning and Random Forests (01 hour 44 minutes)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
17.Support Vector Machines (01 hour 41 minutes)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
[Project] Voice Gender Recognition using SVM
18.K-means (01 hour 00 minutes)Unsupervised Machine Learning Intro
Unsupervised Machine Learning Continued
Data Standardization
19.PCA (02 hours 00 minutes)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
20.Data Science Career (35 minutes)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

Resources Required

  • Basic computer literacy
  • Although it is not required, students would benefit from prior Python experience

Featured Review

Kumesh Rana (5/5): Excellent! One of the Best course for those who want to learn Data Science and make career in data Science. I highly recommend this course. thanks!


  • Moeen Khan (5/5): it was amazing experience best trainer taught with thorough details and excellent way of teaching
  • Muhammad Fazeel Uddin (5/5): the best instructor and the way of his explanation is simply amazing.
  • Brandon Freeman (4/5): This course is perhaps best for it’s introduction to supervised machine learning methods, and that it performs phenomenally well.
  • Muhammad Asif (4/5): This course is best suit for absolutely beginner to data science field.


  • Ini O. (2.5/5): The speaker repeats himself/the same information in different ways multiple times. It get repetitive and wastes time.
  • AISHA AHMED H. (2/5): In the beginning the course was good but when we dived into the details of machine learning, the lecturer was just explaining a bunch of line codes which for me is not the best way to go about when learning coding.
  • A B. (1/5): Explains too much on non-complex aspects, but only explains a little/briefly through the complex and important parts. Disappointing. Only using Google Collab as well. Some datasets and resources are not in sync with shown in lectures (especially section 15 onwards).
  • Kamal B. (1/5): Section 4 till Section 13, all were explained in a hurry. Very less content. And more of a slide reading. Any course without projects is incomplete and of no use. Multiple sections where Instructor had mentioned that ..”next lesson we’ll do project”…, the next lession was skipped / missing in the course.

About the Author

The instructor of this course is Juan E. Galvan who is a Digital Entrepreneur and Business Coach. With a 4.5 instructor rating and 18,285 reviews on Udemy, he offers 15 courses and has taught 513,290 students so far.

  • Juan runs a business since he was in elementary school.
  • His background is in technology, including programming, web development, digital marketing, and e-commerce.
  • He supports lifelong learning that offers the benefits of a university degree without the drawbacks of high expenses and ineffective teaching techniques.

Comparison Table

ParametersLearn Python for Data Science & Machine Learning from A-ZPython in Practice: 15 Projects to Master PythonLearn Data Science & Machine Learning with R from A-Z
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration23 hours21 hours28.5 hours
Rating4.4 /54.7 /54.8 /5
Student Enrollments112,43888,89394,925
InstructorsJuan E. GalvanRahul MulaJuan E. Galvan
Register HereApply Now!Apply Now!Apply Now!

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