Course Curriculum

Welcome to the Python for Data Science & ML bootcamp! 00:01:00
Introduction to Python 00:01:00
Setting Up Python 00:02:00
What is Jupyter? 00:01:00
Anaconda Installation Windows Mac and Ubuntu 00:04:00
How to implement Python in Jupyter 00:01:00
Managing Directories in Jupyter Notebook 00:03:00
Input & Output 00:02:00
Working with different datatypes 00:01:00
Variables 00:02:00
Arithmetic Operators 00:02:00
Comparison Operators 00:01:00
Logical Operators 00:03:00
Conditional statements 00:02:00
Loops 00:04:00
Sequences Part 1: Lists 00:03:00
Sequences Part 2: Dictionaries 00:03:00
Sequences Part 3: Tuples 00:01:00
Functions Part 1: Built-in Functions 00:01:00
Functions Part 2: User-defined Functions 00:03:00
Course Materials 00:00:00
Installing Libraries 00:01:00
Importing Libraries 00:01:00
Pandas Library for Data Science 00:01:00
NumPy Library for Data Science 00:01:00
Pandas vs NumPy 00:01:00
Matplotlib Library for Data Science 00:01:00
Seaborn Library for Data Science 00:01:00
Introduction to NumPy arrays 00:01:00
Creating NumPy arrays 00:06:00
Indexing NumPy arrays 00:06:00
Array shape 00:01:00
Iterating Over NumPy Arrays 00:05:00
Basic NumPy arrays: zeros() 00:02:00
Basic NumPy arrays: ones() 00:01:00
Basic NumPy arrays: full() 00:01:00
Adding a scalar 00:02:00
Subtracting a scalar 00:01:00
Multiplying by a scalar 00:01:00
Dividing by a scalar 00:01:00
Raise to a power 00:01:00
Transpose 00:01:00
Element-wise addition 00:02:00
Element-wise subtraction 00:01:00
Element-wise multiplication 00:01:00
Element-wise division 00:01:00
Matrix multiplication 00:02:00
Statistics 00:03:00
What is a Python Pandas DataFrame? 00:01:00
What is a Python Pandas Series? 00:01:00
DataFrame vs Series 00:01:00
Creating a DataFrame using lists 00:03:00
Creating a DataFrame using a dictionary 00:01:00
Loading CSV data into python 00:02:00
Changing the Index Column 00:01:00
Inplace 00:01:00
Examining the DataFrame: Head & Tail 00:01:00
Statistical summary of the DataFrame 00:01:00
Slicing rows using bracket operators 00:01:00
Indexing columns using bracket operators 00:01:00
Boolean list 00:01:00
Filtering Rows 00:01:00
Filtering rows using AND OR operators 00:02:00
Filtering data using loc() 00:04:00
Filtering data using iloc() 00:02:00
Adding and deleting rows and columns 00:03:00
Sorting Values 00:02:00
Exporting and saving pandas DataFrames 00:02:00
Concatenating DataFrames 00:01:00
groupby() 00:03:00
Introduction to Data Cleaning 00:01:00
Quality of Data 00:01:00
Examples of Anomalies 00:01:00
Median-based Anomaly Detection 00:03:00
Mean-based anomaly detection 00:03:00
Z-score-based Anomaly Detection 00:03:00
Interquartile Range for Anomaly Detection 00:05:00
Dealing with missing values 00:06:00
Regular Expressions 00:07:00
Feature Scaling 00:03:00
Introduction (Exploratory Data Analysis in Python) 00:01:00
What is Exploratory Data Analysis? 00:01:00
Univariate Analysis 00:02:00
Univariate Analysis: Continuous Data 00:06:00
Univariate Analysis: Categorical Data 00:02:00
Bivariate analysis: Continuous & Continuous 00:05:00
Bivariate analysis: Categorical & Categorical 00:03:00
Bivariate analysis: Continuous & Categorical 00:02:00
Detecting Outliers 00:06:00
Categorical Variable Transformation 00:04:00
Introduction to Time Series 00:02:00
Getting stock data using yfinance 00:03:00
Converting a Dataset into Time Series 00:04:00
Working with Time Series 00:04:00
Visualising a Time Series 00:03:00
Data Visualisation using python 00:01:00
Setting Up Matplotlib 00:01:00
Plotting Line Plots using Matplotlib 00:02:00
Title, Labels & Legend 00:05:00
Plotting Histograms 00:01:00
Plotting Bar Charts 00:02:00
Plotting Pie Charts 00:03:00
Plotting Scatter Plots 00:06:00
Plotting Log Plots 00:01:00
Plotting Polar Plots 00:02:00
Handling Dates 00:01:00
Creating multiple subplots in one figure 00:03:00
What is Machine Learning? 00:02:00
Applications of machine learning 00:02:00
Machine Learning Methods 00:01:00
What is Supervised learning? 00:01:00
What is Unsupervised learning? 00:01:00
Supervised learning vs Unsupervised learning 00:04:00
Introduction to regression 00:02:00
How Does Linear Regression Work? 00:02:00
Line representation 00:01:00
Implementation in python: Importing libraries & datasets 00:02:00
Implementation in python: Distribution of the data 00:02:00
Implementation in python: Creating a linear regression object 00:03:00
Understanding Multiple linear regression 00:02:00
Exploring the dataset 00:04:00
Encoding Categorical Data 00:05:00
Splitting data into Train and Test Sets 00:02:00
Training the model on the Training set 00:01:00
Predicting the Test Set results 00:03:00
Evaluating the performance of the regression model 00:01:00
Root Mean Squared Error in Python 00:03:00
Introduction to classification 00:01:00
K-Nearest Neighbours algorithm 00:01:00
Example of KNN 00:01:00
K-Nearest Neighbours (KNN) using python 00:01:00
Importing required libraries 00:01:00
Importing the dataset 00:02:00
Splitting data into Train and Test Sets 00:03:00
Feature Scaling 00:01:00
Importing the KNN classifier 00:02:00
Results prediction & Confusion matrix 00:02:00
Introduction to decision trees 00:01:00
What is Entropy? 00:01:00
Exploring the dataset 00:01:00
Decision tree structure 00:01:00
Importing libraries & datasets 00:01:00
Encoding Categorical Data 00:03:00
Splitting data into Train and Test Sets 00:01:00
Results Prediction & Accuracy 00:03:00
Introduction (Classification Algorithms: Logistic regression) 00:01:00
Implementation steps 00:01:00
Importing libraries & datasets 00:02:00
Splitting data into Train and Test Sets 00:01:00
Pre-processing 00:02:00
Training the model 00:01:00
Results prediction & Confusion matrix 00:02:00
Logistic Regression vs Linear Regression 00:02:00
Introduction to clustering 00:01:00
Use cases 00:01:00
K-Means Clustering Algorithm 00:01:00
Elbow method 00:02:00
Steps of the Elbow method 00:01:00
Implementation in python 00:04:00
Hierarchical clustering 00:01:00
Density-based clustering 00:02:00
Implementation of k-means clustering in python 00:01:00
Importing the dataset 00:03:00
Visualising the dataset 00:02:00
Defining the classifier 00:02:00
3D Visualisation of the clusters 00:03:00
3D Visualisation of the predicted values 00:03:00
Number of predicted clusters 00:02:00
Introduction (Recommender System) 00:01:00
Collaborative Filtering in Recommender Systems 00:01:00
Content-based Recommender System 00:01:00
Importing libraries & datasets 00:03:00
Merging datasets into one dataframe 00:01:00
Sorting by title and rating 00:04:00
Histogram showing number of ratings 00:01:00
Frequency distribution 00:01:00
Jointplot of the ratings and number of ratings 00:01:00
Data pre-processing 00:02:00
Sorting the most-rated movies 00:01:00
Grabbing the ratings for two movies 00:01:00
Correlation between the most-rated movies 00:02:00
Correlation between the most-rated movies 00:02:00
Sorting the data by correlation 00:01:00
Filtering out movies 00:01:00
Sorting values 00:01:00
Repeating the process for another movie 00:02:00
Conclusion 00:01:00

Course Overview 

The Python for Data Science & Machine Learning: Zero to Hero course takes you from complete beginner to confident practitioner. You will learn essential Python programming skills and discover how data professionals clean, analyse and visualise information to support smarter decisions.

Using practical exercises and real-world datasets, you will explore data analysis, predictive modelling and machine learning with Python. You will also learn how models are trained, evaluated and used to make predictions.

This beginner-friendly Python data science and machine learning course is ideal for students, career changers and professionals who want to strengthen their CV. By the end, you will be ready to build practical data projects and confidently understand core data science concepts.

Learning Outcomes

  • Write clean, efficient Python code to solve real-world data problems
  • Use popular Python libraries to organise, clean, and explore datasets
  • Create clear charts and visualisations that reveal hidden patterns in data
  • Apply core statistics and maths concepts used in data science every day
  • Build, train, and test machine learning models using Python
  • Evaluate model performance and improve accuracy using proven techniques
  • Apply your Python for Data Science & Machine Learning skills confidently to your own projects and job applications

Your Benefits by Learning with ‘Cambridge Open Academy’:

  • Accreditation: Showcase your ability with our accredited Python for Data Science & Machine Learning: Zero to Hero to potential employers.
  • Free Certificate: Get a Free Digital Certificate upon successful completion of the Python for Data Science & Machine Learning: Zero to Hero.
  • Flexibility: Learn virtually from anywhere, anytime at your own pace and convenience.
  • Advance Your Career: Upskill to impress your employers and land your dream job or long-awaited promotion.
  • Immediately Applicable Coursework: Keep up with the latest skill trend by putting your skillsets to work.
  • Affordability: Save big with our online Python for Data Science & Machine Learning: Zero to Hero as it not only suits your professional needs but also fits within your budget.
  • Tutor Support: Get tutor support on weekdays, 9-5 am, and our dedicated 24/7 customer support.
  • Lifetime Access: Achieve lifetime access to the top-notch expertly crafted course materials.

What You’ll Gain:

  • Crack the code of Python programming from scratch, covering variables, loops, and functions with no prior experience needed.
  • Master essential libraries like NumPy and Pandas, the backbone of every data science project.
  • Unravel messy data using proven data cleaning techniques, so your results stay accurate and reliable.
  • Explore your data visually, building charts and graphs with Matplotlib and Seaborn to spot patterns fast.
  • Demystify Machine Learning, learning the difference between supervised and unsupervised methods.
  • Build predictive models using regression and classification algorithms, including KNN, Decision Trees, and Logistic Regression.
  • Group and cluster data with K-Means and hierarchical clustering, uncovering hidden patterns in real datasets.
  • Create your own recommender system, the same technology behind Netflix and Amazon’s suggestions.

Our Specialised Delivery Method:

  • Interactive Learning Materials: The course modules were created using an EdTech industry-recognised tool to keep you engaged at all times. With this tool, you get interactive, engaging and top-notch course content. In our courses, you can take advantage of features like—
    • Drop Down Menu
    • Drag and Drop
    • Flash Card
    • Label Graphic
    • Timeline View
  • Responsiveness: In light of contemporary mobile and point-of-need learning trends, our courses are designed to be intrinsically dynamic and provide you the ultimate eLearning solution. These courses will adapt to any gadget without any extra software.
  • Learner-Friendly Navigation: Our courses are also quite simple to navigate for any learner. These courses are designed with simplicity and a modern flow that appeals to a wide range of learning audiences, regardless of their technical background and gadgets.
  • Elegant Outline: Our courses are visually appealing, where you can —
    • Track your progress on the left-side navigation toolbar.
    • Engage in drag-and-drop sorting activities and use the multiple response question to test your understanding of course topics.

Certification:

Once you have successfully completed the Python for Data Science & Machine Learning: Zero to Hero course, you will receive a PDF certificate completely free of cost as a proof of your accomplishment. The hardcopy certificate is also available for the cost of £9.99. UK students are required to pay a £10 as a delivery fee, while international students have to pay £19.99 for the shipment of a hardcopy certificate to their designated address.

Who is this course for?

This Python for Data Science & Machine Learning: Zero to Hero course is developed for people who wish to excel in their professional and personal life. Learn from industry leaders and interact with a global network of experts by enrolling in this Python for Data Science & Machine Learning: Zero to Hero course.

This course is ideal for:

  • Beginners looking to learn Python programming.
  • Professionals seeking to expand their programming knowledge.
  • Students aiming to enter the data science field.
  • Anyone interested in building a foundation in Python.

Requirements

Enrolling in our Python for Data Science & Machine Learning: Zero to Hero course does not require any prior knowledge or experience. All that is required is an internet-connected gadget and a passion to learn.

Career Path

  • Senior Machine Learning Engineer (Python, AWS, MLOps)
    Builds machine learning pipelines and implements MLOps practices within autonomous DevOps teams.
    Average salary: Up to £105,000 per year
  • AI Engineer (Agentic Systems)
    Designs and develops intelligent AI systems capable of reasoning, planning and completing complex tasks.
    Average salary: £60,000–£95,000 per year
  • Machine Learning Engineer / Researcher
    Develops machine learning and generative AI solutions using Python, Databricks, Kafka and AWS technologies.
    Average salary: £45,000–£70,000 per year
  • Machine Learning Engineer
    Creates reliable machine learning models to solve complex business problems using Python and specialist ML libraries.
    Average salary: £50,000–£80,000 per year
  • Data Scientist
    Builds production-ready data science solutions using Python, R, SQL, Dash and Streamlit.
    Average salary: £55,000–£85,000 per year
  • Manufacturing Data Analyst
    Creates data extraction solutions and develops analytical models using Python, SQL and Power BI.
    Average salary: £35,000–£55,000 per year
  • Associate Data Analyst
    Analyses and organises data to provide insights that support senior stakeholders and business decisions.
    Average salary: £30,000–£45,000 per year

Certification:​

Once you have successfully completed this course, you can order a certificate as proof of your achievement.

A PDF certificate is available for £5.99. You can also request a hardcopy certificate for £9.99.

Delivery of the hardcopy certificate is free within the United Kingdom. For international learners, an additional delivery charge will apply based on the destination country.

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