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Data Science and Machine Learning using Python : A Bootcamp
29 Students (7 Reviews)
Master Python data science and machine learning skills through this practical bootcamp for modern technology careers.
GET THIS COURSE AND 3000+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE
Overview
Data Science and Machine Learning using Python: A Bootcamp provides learners with an introduction to the powerful combination of programming, data analysis, and intelligent technologies. This course helps participants understand how data is collected, organised, interpreted, and used to create meaningful solutions. Learners will explore Python programming techniques and develop essential skills for working with modern data-driven applications.
The course covers important areas including Python fundamentals, data processing, data visualisation, statistical concepts, machine learning algorithms, and predictive modelling. Participants will gain practical knowledge of industry-relevant tools and techniques while learning how to analyse datasets, build models, and evaluate outcomes. The training encourages logical thinking and effective problem-solving approaches.
This bootcamp is ideal for individuals looking to develop technology skills and prepare for opportunities in data science and artificial intelligence. The knowledge gained can support career progression in analytics, machine learning, software development, and business intelligence environments.
Learning Outcomes
- Develop machine learning models using practical methods
- Analyse datasets with essential Python libraries effectively
- Apply statistical techniques for accurate data insights
- Create data visualisations for meaningful communication
- Build intelligent solutions using machine learning techniques
- Understand data science concepts through Python programming
Why choose this course
- Affordable, Accessible, informative and well-structured study materials
- Earn a globally recognised accredited qualification
- Study in your own time, at your own pace, through any device
- Benefit from feedback through mock exams and multiple-choice assessments
- Excellent career advancement/career switch opportunities
- Get 24/7 help or advice from our email and live chat teams
Certification
Certificate of Achievement
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Entry Requirements
Basic computer knowledge and an interest in programming, technology, or data analysis are recommended for this course. No previous advanced coding experience is required, as learners will develop Python fundamentals before exploring data science, analytics, and machine learning concepts.
Who is this course for?
- Aspiring data science career professionals
- Beginners exploring Python programming skills
- Analysts improving their data abilities
- Professionals advancing technology knowledge
- Learners interested in artificial intelligence
Career Paths
This course can support progression into roles such as:
- Data Science Analyst Roles
- Machine Learning Engineer Careers
- Python Developer Professional Roles
- Data Analyst Industry Positions
- Artificial Intelligence Specialist Careers
- Business Intelligence Analyst Roles
Course Curriculum
| Welcome, Course Introduction & overview, and Environment set-up | |||
| 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 | ||
| Python Essentials | |||
| 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 | ||
| Python for Data Analysis using NumPy | |||
| 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 | ||
| Python for Data Analysis using Pandas | |||
| 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 | ||
| Python for Data Visualization using matplotlib | |||
| 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 | ||
| Python for Data Visualization using Seaborn | |||
| 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 | ||
| Python for Data Visualization using pandas | |||
| Pandas Built-in Data Visualization | 00:34:00 | ||
| Pandas Data Visualization Exercises Overview | 00:03:00 | ||
| Panda Data Visualization Exercises Solutions | 00:13:00 | ||
| Python for interactive & geographical plotting using Plotly and Cufflinks | |||
| 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 | ||
| Capstone Project - Python for Data Analysis & Visualization | |||
| 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 | ||
| Python for Machine Learning (ML) - scikit-learn - Linear Regression Model | |||
| 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 | ||
| Python for Machine Learning - scikit-learn - Logistic Regression Model | |||
| 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 | ||
| Python for Machine Learning - scikit-learn - K Nearest Neighbors | |||
| 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 | ||
| Python for Machine Learning - scikit-learn - Decision Tree and Random Forests | |||
| 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 | ||
| Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) | |||
| 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 | ||
| Python for Machine Learning - scikit-learn - K Means Clustering | |||
| 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 | ||
| Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) | |||
| 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 | ||
| Recommender Systems with Python - (Additional Topic) | |||
| 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 | ||
| Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) | |||
| 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 | |||
| Resources – Data Science and Machine Learning using Python : A Bootcamp | 00:00:00 | ||
| Order your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
Can’t find the anwser you’re looking for ? Reach out to customer support team.
Data Science and Machine Learning using Python is a comprehensive training program that teaches learners how to work with data, develop algorithms, and create intelligent solutions using Python-based technologies.
Yes, this course is suitable for beginners because it introduces essential concepts step-by-step and helps learners build confidence in programming, data analysis, and machine learning techniques.
You will gain skills in Python programming, data handling, data visualisation, statistical analysis, machine learning models, and interpreting information to support better decisions.
Python is widely used in data science because it provides simple programming methods, powerful libraries, and flexible frameworks for analysing data and developing intelligent applications.
No, previous coding experience is not required. The course begins with programming fundamentals and gradually introduces more advanced data science and machine learning concepts.
The course includes Python programming, data preparation, data analysis, visualisation techniques, machine learning algorithms, predictive modelling, and practical applications of data science.
Yes, completing this bootcamp can help learners develop valuable technical skills needed for entry-level positions in data science, analytics, programming, and artificial intelligence fields.
Machine learning is used to identify patterns, make predictions, automate processes, and create smart systems that improve efficiency across different industries.
Yes, learners will complete practical activities that allow them to apply Python techniques, analyse datasets, and understand how real-world machine learning projects are developed.
Data science skills are used in healthcare, finance, technology, retail, marketing, and business sectors to improve decision-making and create innovative solutions.
The course develops problem-solving skills by teaching learners how to examine data, identify patterns, create models, and use analytical approaches for challenges.
After completing this bootcamp, learners can explore careers such as data analyst, machine learning engineer, Python developer, AI specialist, and business intelligence professional.