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A masterclass in Data Science & Machine Learning with Python covering all essential concepts, Python & Statistics for Data Science, Probability & hypothesis testing, NumPy & Panda Data analysis, visualisation, and more, with two free e-books on Data Science and Data Analysis and downloadable cheat sheets for anyone with any skill set to use the application confidently as a professional. The course offers 23 CPD credits.

£12.99 inc. VAT
Course Access

1 year

Last Updated

July 28, 2023

Students Enrolled


Course Duration

22 hours, 50 minutes

Course Instructor

The course offers 23 CPD credits. Two downloadable books on Data  Science and Data Analysis, seven cheat sheets to use Python, NumPy,  Pandas and more.

Learn Python for Data Science & Machine Learning from A-Z, by Juan  Galvan, offers a practical and immersive experience where you'll become a  true Python maestro for Data Science and Machine Learning. Whether you're a complete beginner or possess some coding experience,  this course is expertly crafted to cater to all skill levels.

Our primary objective is to equip you with a deep understanding of Python programming for Data Science and Machine Learning and not only with a deep understanding of Python programming for Data Science and Machine Learning but also to guide you on the path to becoming a professional Data Scientist using Python. By the end of this course, you'll have the expertise to land your first job in this thrilling field.

We will explore some of the best and most crucial Python libraries for data science, including NumPy, Pandas, and Matplotlib.


simplifies mathematical and statistical operations and serves as the foundation for many features in the Pandas library.


is your go-to Python library for working effortlessly with data -  a vital tool in any Python data science project? And with Matplotlib,  you'll master the art of data visualisation, creating graphs akin to those found in Excel or Google Sheets.

Our approach combines hands-on projects with a solid theoretical foundation. Starting from the basics of Python Programming for Data Science, we'll guide you through to mastery. The Machine Learning segment of the course introduces you to the fundamentals of machine learning using Python. You'll delve into the distinctions between supervised and unsupervised learning, understand how statistical modelling relates to machine learning, and compare the two approaches.

The course covers 5 main areas:


This intro section fully introduces the Python for Data Science and Machine Learning course, the 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 fully introduces Data Analysis and Data Visualisation with Python with hands-on, step-by-step training.

  • Python Crash Course
  • NumPy Data Analysis
  • Pandas Data Analysis
  • Matplotlib
  • Seaborn
  • Plotly


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

After completing this course, you will be able to:

  • Become a professional Data Scientist, Data Engineer, Data Analyst, or Consultant
  • Create a resume and land your first job as a Data Scientist
  • Write complex Python programs for practical industry scenarios
  • Use NumPy for Numerical Data
  • Understand Supervised vs Unsupervised Machine Learning
  • Understand Machine Learning Concepts and Algorithms
  • Use Python to clean, analyse, and visualise data
  • Use Statistics for Data Science
  • Do data cleaning, processing, wrangling, and manipulation
  • Use Python for Data Science
  • Do Plotting in Python (graphs, charts, plots, histograms, etc.)
  • Use Machine Learning and its various practical applications
  • Understand  Regression, Classification, Clustering, and Sci-kit learn
  • Know K-Means Clustering
  • Build Custom Data Solutions
  • Test Probability and Hypothesis

This course is beneficial to :

This course benefits a wide range of individuals looking to excel in Data Science and Machine Learning using Python. Here are some of the critical audiences who will significantly benefit from enrolling in this course:

  1. Aspiring Data Scientists: If you dream of becoming a skilled and sought-after Data Scientist, this course will be your perfect stepping stone. It covers the essential Python programming skills and data analysis techniques crucial for a successful career in Data Science.
  2. Machine Learning Enthusiasts: If you are fascinated by the potential of Machine Learning and want to explore its applications, this course will provide you with a solid foundation. You'll learn about machine-learning algorithms and how to implement them using Python.
  3. Python Developers: For those already familiar with Python but looking to specialise in Data Science and Machine Learning, this course will take your Python skills to the next level. You'll learn to leverage Python's powerful libraries and tools for data manipulation, visualisation, and analysis.
  4. Big Data Engineers: Professionals working with Big Data can enhance their skill set by learning Python for Data Science. This course will equip you with the tools to handle and process data efficiently.
  5. IT Specialists and Database Developers: Understanding Data Science and Machine Learning using Python will give IT specialists and database developers a competitive edge in their respective domains. It opens up opportunities to work on data-driven projects and make data-informed decisions.
  6. Professionals Seeking Career Transition: If you want to switch careers and enter the booming field of Data Science and Machine Learning, this course will provide you with the necessary skills and knowledge to make a successful transition.
  7. Graduates and Students: For recent graduates or students interested in Data Science and Machine Learning, this course is a practical and comprehensive learning experience that complements academic knowledge with real-world applications.
  8. Anyone Curious About Data Science: If you are simply curious about Data Science and want to explore its possibilities, this course offers a beginner-friendly introduction to the field and helps you understand its relevance in various industries.

In summary, whether you are a newcomer to the world of programming or an experienced professional seeking to expand your skill set, this course is tailored to meet your needs.

Why choose this course?

  • Developed and delivered by Data Science Expert
  • Get Instant E-certificate
  • Entirely online
  • Self-paced learning and laptop, tablet, and smartphone-friendly
  • 24/7 Support

Career Prospect of this course :

Completing the "Learn Python for Data Science & Machine Learning from A-Z" course opens up many promising career prospects across various industries. As organisations increasingly rely on data-driven decision-making and machine learning technologies, the demand for professionals with expertise in Python for Data Science and Machine Learning continues to grow. Here is a list of career prospects that become attainable with the skills gained from this course:

  1. Data Scientist: As a Data Scientist, you will be responsible for collecting, analysing, and interpreting large datasets to extract valuable insights and drive informed business decisions. You'll use Python and its libraries to preprocess data, build predictive models, and perform statistical analysis.
  2. Machine Learning Engineer: Machine Learning Engineers design and develop machine learning models and algorithms. With Python as your foundation, you can create, optimise, and deploy these models to solve complex problems and build intelligent systems.
  3. Data Analyst: As a Data Analyst, you will use Python to explore and visualise data, identifying trends, patterns, and key insights. Your analytical skills will help businesses make data-driven decisions and improve overall performance.
  4. Business Intelligence Analyst: In this role, you'll leverage Python to transform raw data into actionable reports and dashboards. Business Intelligence Analysts are crucial in providing meaningful data visualisations to support business strategy and decision-making.
  5. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines and databases. Python proficiency will enable you to handle data processing and integration tasks efficiently.
  6. Research Scientist: Research Scientists use Python to experiment, simulate, and analyse data in various scientific and academic fields. Your skills will enable you to explore complex data sets and contribute to research advancements.
  7. AI Developer: As an AI Developer, you'll work with Python to implement and optimise machine learning algorithms and AI applications. You'll contribute to developing cutting-edge technologies like natural language processing and computer vision.
  8. Marketing Analyst: Analysts leverage Python to analyse customer data, segment markets, and create targeted marketing campaigns. Your insights will be crucial in optimising marketing strategies for higher customer engagement and conversion rates.
  9. Financial Analyst: In finance, Python is used for data analysis, portfolio optimisation, risk assessment, and fraud detection. As a Financial Analyst, your Python skills will enhance financial modelling and decision-making processes.
  10. Healthcare Data Analyst: Healthcare organisations rely on Python to analyse patient data, develop predictive models for disease outcomes, and improve healthcare delivery. Your skills can contribute to advancements in medical research and patient care.
  11. E-commerce Analyst: E-commerce companies use Python to analyse customer behaviour, recommend products, and optimise pricing strategies. As an E-commerce Analyst, your expertise will enhance customer experience and boost sales.
  12. Data Visualisation Specialist: As a Data Visualisation Specialist, you'll use Python libraries like Matplotlib and Seaborn to create engaging and informative visualisations that convey complex data quickly.

These career prospects represent just a fraction of the opportunities that await you upon completing this course

Industry Expert Instructor

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Course Content

    • Who is This Course For? 00:03:00
    • Data Science + Machine Learning Marketplace 00:07:00
    • Data Science Job Opportunities 00:04:00
    • Data Science Job Roles 00:10:00
    • What is a Data Scientist? 00:17:00
    • How To Get a Data Science Job 00:18:00
    • Data Science Projects Overview 00:12:00
    • Why We Use Python? 00:03:00
    • What is Data Science? 00:13:00
    • What is Machine Learning? 00:14:00
    • Machine Learning Concepts & Algorithms 00:15:00
    • What is Deep Learning? 00:10:00
    • Machine Learning vs Deep Learning 00:11:00
    • What is Programming? 00:06:00
    • Why Python for Data Science? 00:05:00
    • What is Jupyter? 00:04:00
    • What is Google Colab? 00:03:00
    • Python Variables, Booleans and None 00:12:00
    • Getting Started with Google Colab 00:09:00
    • Python Operators 00:25:00
    • Python Numbers & Booleans 00:08:00
    • Python Strings 00:13:00
    • Python Conditional Statements 00:14:00
    • Python For Loops and While Loops 00:08:00
    • Python Lists 00:05:00
    • More about Lists 00:15:00
    • Python Tuples 00:11:00
    • Python Dictionaries 00:20:00
    • Python Sets 00:10:00
    • Compound Data Types & When to use each one? 00:13:00
    • Python Functions 00:14:00
    • Object Oriented Programming in Python 00:19:00
    • Python Data Science Handbook 00:00:00
    • Intro To Statistics 00:07:00
    • Descriptive Statistics 00:07:00
    • Measure of Variability 00:12:00
    • Measure of Variability Continued 00:10:00
    • Measures of Variable Relationship 00:08:00
    • Inferential Statistics 00:15:00
    • Measure of Asymmetry 00:02:00
    • Sampling Distribution 00:08:00
    • What Exactly is Probability? 00:04:00
    • Expected Values 00:03:00
    • Relative Frequency 00:05:00
    • Hypothesis Testing Overview 00:09:00
    • Intro NumPy Array Data Types 00:12:00
    • NumPy Arrays 00:08:00
    • NumPy Arrays Basics 00:12:00
    • NumPy Array Indexing 00:09:00
    • NumPy Array Computations 00:06:00
    • Broadcasting 00:05:00
    • Introduction to Pandas 00:16:00
    • Introduction to Pandas Continued 00:18:00
    • Python Data analysis book 00:00:00
    • Data Visualization Overview 00:25:00
    • Different Data Visualization Libraries in Python 00:13:00
    • Python Data Visualization Implementation 00:08:00
    • Introduction To Machine Learning 00:26:00
    • Exploratory Data Analysis 00:13:00
    • Feature Scaling 00:08:00
    • Data Cleaning 00:08:00
    • Feature Engineering 00:06:00
    • Linear Regression Intro 00:08:00
    • Gradient Descent 00:06:00
    • Linear Regression + Correlation Methods 00:27:00
    • Linear Regression Implementation 00:05:00
    • Logistic Regression 00:03:00
    • KNN Overview 00:03:00
    • parametric vs non-parametric models 00:03:00
    • EDA on Iris Dataset 00:22:00
    • The KNN Intuition 00:02:00
    • Implement the KNN algorithm from scratch 00:12:00
    • Compare the result with the sklearn library 00:04:00
    • Hyperparameter tuning using the cross-validation 00:11:00
    • The decision boundary visualization 00:05:00
    • Manhattan vs Euclidean Distance 00:11:00
    • Feature scaling in KNN 00:06:00
    • Curse of dimensionality 00:08:00
    • KNN use cases 00:04:00
    • KNN pros and cons 00:06:00
    • Decision Trees Section Overview 00:04:00
    • EDA on Adult Dataset 00:17:00
    • What is Entropy and Information Gain? 00:22:00
    • The Decision Tree ID3 algorithm from scratch Part 1 00:12:00
    • The Decision Tree ID3 algorithm from scratch Part 2 00:08:00
    • The Decision Tree ID3 algorithm from scratch Part 3 00:04:00
    • ID3 – Putting Everything Together 00:21:00
    • Evaluating our ID3 implementation 00:17:00
    • Compare with sklearn implementation 00:09:00
    • Visualizing the tree 00:10:00
    • Plot the features importance 00:06:00
    • Decision Trees Hyper-parameters 00:12:00
    • Pruning 00:17:00
    • [Optional] Gain Ration 00:03:00
    • Decision Trees Pros and Cons 00:08:00
    • [Project] Predict whether income exceeds $50K/yr – Overview 00:03:00
    • Ensemble Learning Section Overview 00:04:00
    • What is Ensemble Learning? 00:13:00
    • What is Bootstrap Sampling? 00:08:00
    • What is Bagging? 00:05:00
    • Out-of-Bag Error (OOB Error) 00:08:00
    • Implementing Random Forests from scratch Part 1 00:23:00
    • Implementing Random Forests from scratch Part 2 00:06:00
    • Compare with sklearn implementation 00:09:00
    • Random Forests Hyper-Parameters 00:04:00
    • Random Forests Pros and Cons 00:05:00
    • What is Boosting? 00:05:00
    • AdaBoost Part 1 00:04:00
    • AdaBoost Part 2 00:15:00
    • SVM Outline 00:05:00
    • SVM intuition 00:11:00
    • Hard vs Soft Margins 00:13:00
    • C hyper-parameter 00:04:00
    • Kernel Trick 00:12:00
    • SVM – Kernel Types 00:18:00
    • SVM with Linear Dataset (Iris) 00:13:00
    • SVM with Non-linear Dataset 00:13:00
    • SVM with Regression 00:06:00
    • [Project] Voice Gender Recognition using SVM 00:04:00
    • Unsupervised Machine Learning Intro 00:20:00
    • Representation of Clusters 00:20:00
    • Data Standardization 00:19:00
    • PCA Section Overview 00:05:00
    • What is PCA? 00:09:00
    • PCA Drawbacks 00:03:00
    • PCA Algorithm Steps (Mathematics) 00:13:00
    • Covariance Matrix vs SVD 00:04:00
    • PCA – Main Applications 00:02:00
    • PCA – Image Compression 00:27:00
    • PCA Data Preprocessing 00:14:00
    • PCA – Biplot and the Screen Plot 00:17:00
    • PCA – Feature Scaling and Screen Plot 00:09:00
    • PCA – Supervised vs Unsupervised 00:05:00
    • PCA – Visualization 00:08:00
    • Importing Data 00:00:00
    • Jupyter Notebook 00:00:00
    • Matplotlib 00:00:00
    • NumPy Basics 00:00:00
    • Pandas 00:00:00
    • Python Basics 00:00:00
    • Scikit-Learn 00:00:00
    • Seaborn 00:00:00
    • Supervised Learning 00:00:00
    • Unsupervised Learning 00:00:00
    • Python For Data Science Cheat Sheet 00:00:00
    • 600 Data Science Questions & Answers 00:00:00
    • Creating A Data Science Resume 00:07:00
    • Data Science Cover Letter 00:04:00
    • How to Contact Recruiters 00:04:00
    • Getting Started with Freelancing 00:04:00
    • Top Freelance Websites 00:05:00
    • Personal Branding 00:04:00
    • Networking Do’s and Don’ts 00:03:00
    • Importance of a Website 00:03:00
    • Cover Letter Sample 00:00:00
    • Recruiter Email Template 00:00:00
    • Resume Template 00:00:00
    • Cold Email Template & Examples 00:00:00
    • Cover Letter Template & Samples 00:00:00
    • Networking Guide 00:00:00
    • Recruiter Reach Out Template and Examples 00:00:00
    • Resume Checklist 00:00:00
    • Get Your Certificate & Transcript 00:00:00

Course Reviews


4 ratings
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  1. Wondeful course


    Great course still studying it

  2. Narges Khadem HosseiniJuly 3, 2021 at 8:22 am

    Machine learning and data science


    The information was very beneficial, better off having English subtitles.

  3. Your comment is awaiting moderation.

    Data Science & Machine Learning with Python


    While the course covers a great deal of this field, it does not include any marked or unmarked exercises to perform . This does not embed knowledge very well., nor test it. The lessons jump about a bit as well. If you want a proper certification I would look elsewhere, perhaps Data-camp / Data-quest/ Coursera etc . or even Kaggle, which have marked exercises. As I only paid about £9 for the course I cannot complain too much.

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