.png)
9 Reviews
3 Students
Overview: Data Science and Machine Learning using Python – A Bootcamp
Overview: Data Science and Machine Learning using Python – A Bootcamp
Data is transforming every industry, from finance and healthcare to retail, marketing, and artificial intelligence. The Data Science and Machine Learning using Python – A Bootcamp is designed for learners who want to build strong analytical and machine learning knowledge using Python programming. Organisations across the UK increasingly rely on data-driven systems to improve business decisions, automate processes, and predict future outcomes.
Key Points of This Course at a Glance:
Complete Python data science and machine learning training Learn NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn Study machine learning algorithms and predictive modelling Build data visualisation and analytical reporting knowledge Explore NLP and recommender systems
This data science course introduces learners to Python programming, data analysis, machine learning algorithms, data visualisation, and predictive modelling using industry-recognised Python libraries. The curriculum explains how raw datasets are transformed into meaningful insights through statistical analysis, visualisation tools, and machine learning techniques.
Learners will explore NumPy, Pandas, Matplotlib, Seaborn, Plotly, Scikit-Learn, NLP, recommender systems, clustering algorithms, regression models, and dimensionality reduction techniques. The course also includes multiple projects that simulate real data analysis and machine learning workflows.
Whether you are entering the data industry or strengthening technical knowledge, this training supports career progression in data science, analytics, machine learning, and artificial intelligence.
What Is Data Science Course?
The data science course focuses on analysing, processing, and interpreting large datasets using Python programming and machine learning frameworks. Data science combines programming, mathematics, statistics, and business intelligence to support decision-making and predictive analysis.
This course introduces learners to Python essentials before moving into data analysis libraries such as NumPy and Pandas. Learners study data manipulation, missing data handling, grouping operations, merging datasets, and analytical workflows.
The training then expands into data visualisation using Matplotlib, Seaborn, Plotly, and Pandas visualisation tools. Learners explore how charts, plots, and dashboards help organisations identify trends and patterns within datasets.
Advanced modules introduce machine learning models including:
 Linear Regression Logistic Regression K Nearest Neighbors Decision Trees Random Forests Support Vector Machines K Means Clustering Principal Component Analysis (PCA)
The course also introduces Natural Language Processing (NLP) and recommender systems using Python.
By completing this training, learners develop strong knowledge of data analysis workflows, machine learning techniques, and Python-based data science ecosystems.
Course Description
The Data Science and Machine Learning using Python – A Bootcamp begins with environment setup and course preparation. Learners configure Python development environments and explore tools required for data science workflows.
The Python essentials section introduces data types, loops, list comprehensions, functions, lambda expressions, map, filter operations, and foundational programming concepts required for data processing.
The course then moves into NumPy, where learners study arrays, indexing, slicing, broadcasting, arithmetic operations, boolean masking, and numerical computing methods.
Pandas training introduces data structures such as Series and DataFrames. Learners explore missing data handling, data wrangling, grouping operations, joins, merges, and analytical reporting. Real-world projects including customer purchases and payroll datasets help reinforce analytical workflows.
Data visualisation modules cover:
• Matplotlib plotting
• Object-oriented visualisation
• Seaborn distribution and regression plots
• Axis grids and matrix plots
• Interactive Plotly dashboards
• Geographical plotting using Plotly and Cufflinks
Capstone projects provide analytical exercises using financial and emergency call datasets.
The machine learning section introduces supervised and unsupervised learning techniques using Scikit-Learn. Learners study regression models, classification algorithms, clustering methods, dimensionality reduction, and predictive modelling workflows.
Additional modules cover recommender systems and Natural Language Processing using NLTK, including tokenisation, vectorisation, TF-IDF, feature engineering, and text preprocessing pipelines.
By completing this structured bootcamp, learners develop strong knowledge of Python-based data science, machine learning models, and analytical workflows used in modern data industries.
Learning Outcome
Learning Outcome- Understand Python programming essentials for data science
- Use NumPy for numerical computing and array operations
- Analyse datasets using Pandas DataFrames
- Perform data wrangling and missing data handling
- Create visualisations using Matplotlib and Seaborn
- Build interactive plots using Plotly and Cufflinks
- Apply machine learning algorithms using Scikit-Learn
Who Is This Course For?
Who Is This Course For?- Aspiring data analysts and data scientists seeking structured training.
- Python developers interested in machine learning and AI.
- Business analysts working with data-driven reporting.
- Students interested in analytics and artificial intelligence.
- Professionals seeking career progression in data science fields.
Why Enrol in This Data Science Course?
Modern organisations increasingly depend on data-driven strategies for forecasting, automation, customer analysis, and operational efficiency. Data science and machine learning play major roles in business intelligence, artificial intelligence, fintech, healthcare, retail, and cloud computing industries.
This course provides structured knowledge of data analysis, machine learning algorithms, data visualisation, and predictive modelling using Python.
Instead of focusing only on theory, the curriculum includes projects, exercises, analytical workflows, and machine learning implementation techniques widely used in modern data environments.
Professionals with Python data science knowledge support business intelligence, automation systems, and AI-driven decision-making processes.
Whether you want to transition into analytics, strengthen programming knowledge, or move into machine learning careers, this bootcamp supports long-term technical development.
Data Science Course Career Path
Knowledge of data science and machine learning supports several high-demand technical roles across the UK.
Typical career pathways include:
Data Analyst — £35,000–£55,000
Analyses datasets and business intelligence reports.
Junior Data Scientist — £40,000–£60,000
Builds predictive models and analytical systems.
Machine Learning Engineer — £55,000–£85,000
Develops AI and predictive automation systems.
Business Intelligence Analyst — £40,000–£65,000
Supports reporting and data-driven business decisions.
Python Developer — £45,000–£75,000
Builds applications and analytical systems using Python.
AI Engineer — £60,000–£95,000
Develops machine learning and artificial intelligence solutions.
Professionals with Python data science knowledge can progress into advanced AI, machine learning engineering, and cloud analytics roles.
Enrol Today – Build Python Data Science Skills
By enrolling in this data science course, you begin building analytical and machine learning knowledge required for modern data-driven industries.
Python remains one of the most widely used programming languages for analytics, automation, machine learning, and artificial intelligence development.
Start building the knowledge required for data analysis, predictive modelling, visualisation, and machine learning systems.
Certificate of Achievement
Upon successfully completing this data science course, learners will receive an Accredited Certificate confirming their knowledge of Python programming, data analysis, and machine learning workflows.
We provide two recognised certificate options:
CPD-QS Certificate
Learners can obtain a certificate accredited by CPD Quality Standards (CPD-QS), supporting professional development in data science and analytics.
SKILL UP Brand Recognised Certificate
Learners will also receive a SKILL UP Certificate of Completion verifying successful completion of the bootcamp.
Both certificates support career progression in analytics, machine learning, and data science roles.
Earn Your Accredited Certificate with Transcript
Save 20% with the coupon code SKILL20
Show Your Certified Identity with a CPD-QS Certificate
Perfect for employers, clients, or academic verification.
Frequently Asked Questions
 Yes. The course starts with Python fundamentals before progressing into advanced machine learning topics.
 Basic computer knowledge is helpful, but the Python essentials section supports beginners.
 The course includes NumPy, Pandas, Matplotlib, Seaborn, Plotly, Scikit-Learn, and NLTK.
 Yes. The training includes multiple projects and analytical exercises.
 Yes. Learners study Matplotlib, Seaborn, Plotly, and Pandas visualisation methods.
Data Science and Machine Learning using Python – A Bootcamp Reviews
A well-organised and highly valuable course with clear, easy-to-understand guidance throughout. I’ve gained knowledge that’s directly relevant to my day-to-day responsibilities. It’s given me greater confidence in applying these skills professionally.
Engaging content delivered in a straightforward and structured format. The examples were realistic and helped reinforce key concepts effectively. I would certainly recommend it to colleagues looking to upskill
Comprehensive, insightful and professionally presented from start to finish. The course materials were clear and well supported. A worthwhile investment for anyone serious about career development
Curriculum
-
Welcome & Course Overview
00:07:00
-
Set-up the Environment for the Course (lecture 1)
00:09:00
-
Set-up the Environment for the Course (lecture 2)
00:25:00
-
Two other options to setup environment
00:04:00
00:07:00 -
Python data types Part 1
00:21:00
-
Python Data Types Part 2
00:15:00
-
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)
00:16:00
-
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)
00:20:00
-
Python Essentials Exercises Overview
00:02:00
-
Python Essentials Exercises Solutions
00:22:00
-
What is Numpy? A brief introduction and installation instructions.
00:03:00
-
NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes.
00:28:00
-
NumPy Essentials – Indexing, slicing, broadcasting & boolean masking
00:26:00
-
NumPy Essentials – Arithmetic Operations & Universal Functions
00:07:00
-
NumPy Essentials Exercises Overview
00:02:00
-
NumPy Essentials Exercises Solutions
00:25:00
-
What is pandas? A brief introduction and installation instructions.
00:02:00
-
Pandas Introduction
00:02:00
-
Pandas Essentials – Pandas Data Structures – Series
00:20:00
-
Pandas Essentials – Pandas Data Structures – DataFrame
00:30:00
-
Pandas Essentials – Handling Missing Data
00:12:00
-
Pandas Essentials – Data Wrangling – Combining, merging, joining
00:20:00
-
Pandas Essentials – Groupby
00:10:00
-
Pandas Essentials – Useful Methods and Operations
00:26:00
-
Pandas Essentials – Project 1 (Overview) Customer Purchases Data
00:08:00
-
Pandas Essentials – Project 1 (Solutions) Customer Purchases Data
00:31:00
-
Pandas Essentials – Project 2 (Overview) Chicago Payroll Data
00:04:00
-
Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data
00:18:00
-
Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach
00:13:00
-
Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach
00:22:00
-
Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach
00:22:00
-
Matplotlib Essentials – Exercises Overview
00:06:00
-
Matplotlib Essentials – Exercises Solutions
00:21:00
-
Seaborn – Introduction & Installation
00:04:00
-
Seaborn – Distribution Plots
00:25:00
-
Seaborn – Categorical Plots (Part 1)
00:21:00
-
Seaborn – Categorical Plots (Part 2)
00:16:00
-
Seborn-Axis Grids
00:25:00
-
Seaborn – Matrix Plots
00:13:00
-
Seaborn – Regression Plots
00:11:00
-
Seaborn – Controlling Figure Aesthetics
00:10:00
-
Seaborn – Exercises Overview
00:04:00
-
Seaborn – Exercise Solutions
00:19:00
-
Pandas Built-in Data Visualization
00:34:00
-
Pandas Data Visualization Exercises Overview
00:03:00
-
Panda Data Visualization Exercises Solutions
00:13:00
-
Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1)
00:19:00
-
Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2)
00:14:00
-
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview)
00:11:00
-
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions)
00:37:00
-
Project 1 – Oil vs Banks Stock Price during recession (Overview)
00:15:00
-
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1)
00:18:00
-
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2)
00:18:00
-
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3)
00:17:00
-
Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview)
00:03:00
-
Introduction to ML – What, Why and Types…..
00:15:00
-
Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
00:15:00
-
scikit-learn – Linear Regression Model – Hands-on (Part 1)
00:17:00
-
scikit-learn – Linear Regression Model Hands-on (Part 2)
00:19:00
-
Good to know! How to save and load your trained Machine Learning Model!
00:01:00
-
scikit-learn – Linear Regression Model (Insurance Data Project Overview)
00:08:00
-
scikit-learn – Linear Regression Model (Insurance Data Project Solutions)
00:30:00
-
Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc.
00:10:00
-
scikit-learn – Logistic Regression Model – Hands-on (Part 1)
00:17:00
-
scikit-learn – Logistic Regression Model – Hands-on (Part 2)
00:20:00
-
scikit-learn – Logistic Regression Model – Hands-on (Part 3)
00:11:00
-
scikit-learn – Logistic Regression Model – Hands-on (Project Overview)
00:05:00
-
scikit-learn – Logistic Regression Model – Hands-on (Project Solutions)
00:15:00
-
Theory: K Nearest Neighbors, Curse of dimensionality ….
00:08:00
-
scikit-learn – K Nearest Neighbors – Hands-on
00:25:00
-
scikt-learn – K Nearest Neighbors (Project Overview)
00:04:00
-
scikit-learn – K Nearest Neighbors (Project Solutions)
00:14:00
-
Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging….
00:18:00
-
scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1)
00:19:00
-
scikit-learn – Decision Tree and Random Forests (Project Overview)
00:05:00
-
scikit-learn – Decision Tree and Random Forests (Project Solutions)
00:15:00
-
Support Vector Machines (SVMs) – (Theory Lecture)
00:07:00
-
scikit-learn – Support Vector Machines – Hands-on (SVMs)
00:30:00
-
scikit-learn – Support Vector Machines (Project 1 Overview)
00:07:00
-
scikit-learn – Support Vector Machines (Project 1 Solutions)
00:20:00
-
scikit-learn – Support Vector Machines (Optional Project 2 – Overview)
00:02:00
-
Theory: K Means Clustering, Elbow method …..
00:11:00
-
scikit-learn – K Means Clustering – Hands-on
00:23:00
-
scikit-learn – K Means Clustering (Project Overview)
00:07:00
-
scikit-learn – K Means Clustering (Project Solutions)
00:22:00
-
Theory: Principal Component Analysis (PCA)
00:09:00
-
scikit-learn – Principal Component Analysis (PCA) – Hands-on
00:22:00
-
scikit-learn – Principal Component Analysis (PCA) – (Project Overview)
00:02:00
-
scikit-learn – Principal Component Analysis (PCA) – (Project Solutions)
00:17:00
-
Theory: Recommender Systems their Types and Importance
00:06:00
-
Python for Recommender Systems – Hands-on (Part 1)
00:18:00
-
Python for Recommender Systems – – Hands-on (Part 2)
00:19:00
-
Natural Language Processing (NLP) – (Theory Lecture)
00:13:00
-
NLTK – NLP-Challenges, Data Sources, Data Processing …..
00:13:00
-
NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing
00:19:00
-
NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW….
00:19:00
-
NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes …
00:13:00
-
NLTK – NLP – Pipeline feature to assemble several steps for cross-validation…
00:09:00
-
Resources – Data Science and Machine Learning using Python – A Bootcamp
-
Order Your Certificate
Offer Ends in
TAKE ALL COURSES FOR £99
-
Duration:1 day
-
Access:1 Year
-
Units:100
