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5 Reviews
35 Students
Overview
If you want to build strong capability in data analysis, predictive modelling, and Python-based machine learning systems, the Data Science & Machine Learning with Python course is designed to develop structured technical knowledge aligned with modern industry needs. In today’s data-driven environment, organisations depend on skilled professionals who can transform raw data into actionable insights for decision-making and automation.
Data science and machine learning are widely used across sectors such as finance, healthcare, retail, cybersecurity, and digital marketing. Businesses increasingly rely on data modelling, statistical evaluation, and algorithmic systems to improve performance and forecast outcomes.
This training provides a structured pathway from Python fundamentals to advanced machine learning techniques. Learners progress through data handling, statistical analysis, visualization, feature engineering, and model evaluation using real datasets and industry tools.
- Learn Python programming foundations for data science applications
- Understand machine learning concepts, types, and real-world applications
- Work with NumPy, Pandas, and Matplotlib for data analysis and visualization
- Explore dataset structure, statistical patterns, and correlations
- Apply preprocessing, feature selection, and model evaluation techniques
Whether entering the data field or upgrading technical capability, this course supports structured progression into analytics and machine learning roles.
What Is Data Science & Machine Learning with Python Course?
The Data Science & Machine Learning with Python course focuses on how Python is used to analyse data, build predictive models, and support intelligent decision systems. It introduces learners to the complete data science workflow, from data collection to model deployment.
This training explains how datasets are processed, cleaned, structured, and analysed using Python libraries. It also covers how machine learning algorithms are applied to classification and regression problems to generate predictions from data patterns.
Learners explore statistical concepts, data visualisation methods, and preprocessing techniques such as scaling, normalization, and feature selection. These elements help prepare datasets for accurate model training and evaluation.
By the end of the course, learners develop a clear foundation in Python-based data science workflows, machine learning techniques, and structured analytical thinking used in modern digital industries.
Course Description
The Data Science & Machine Learning with Python course begins with an introduction to machine learning concepts, definitions, and classifications. Learners explore how machine learning is applied across industries for automation and prediction.
The training then moves into Python fundamentals, covering functions, data structures, and programming logic. It continues with key libraries such as NumPy, Pandas, and Matplotlib for numerical analysis, data manipulation, and visualization.
A major section focuses on dataset understanding, including structure analysis, statistical summaries, correlation, skewness, and distribution patterns. These topics help learners interpret data behaviour effectively.
The course includes data visualization techniques using histograms, density plots, box plots, scatter plots, and correlation matrices to identify relationships within datasets.
Further modules cover data preprocessing techniques such as rescaling, standardisation, normalization, and binarisation. Feature selection methods including chi-square testing, recursive feature elimination, PCA, and feature importance are also included.
Learners study algorithm evaluation techniques such as train-test splits, cross-validation methods, and performance testing strategies to measure model accuracy.
The programme also introduces evaluation metrics including accuracy, log loss, confusion matrix, ROC-AUC, mean absolute error, and R-squared.
Machine learning algorithms covered include logistic regression, K-nearest neighbours, decision trees, support vector machines, naïve Bayes, and regression models such as ridge, lasso, and elastic net.
Advanced topics include ensemble learning (bagging, boosting, voting), hyperparameter tuning using grid and random search, and pipeline construction for streamlined workflows.
The course concludes with model saving using Pickle and Joblib, real-time prediction examples, and final model evaluation using datasets such as diabetes prediction, iris classification, and housing price estimation.
Why Enrol in This Data Science & Machine Learning with Python Course?
Data-driven technologies are transforming how organisations operate and make decisions. Skilled professionals who can analyse data and build predictive systems are increasingly required across the UK job market.
This course provides structured training in Python programming, data science workflows, and machine learning techniques. It focuses on how data is processed, interpreted, and applied in real-world scenarios using industry tools and datasets.
Professionals with data science and machine learning capability contribute to better forecasting, improved decision-making, and automated solutions across industries.
Whether starting a new career or upgrading technical knowledge, this course supports progression into one of the fastest-growing technology fields.
Learning Outcome
Learning Outcome- Understand core principles of data science and machine learning
- Use Python for data processing and analytical tasks
- Work with NumPy, Pandas, and Matplotlib libraries
- Analyse datasets using statistical and visual methods
- Apply data preprocessing techniques for model readiness
- Perform feature selection and dimensionality reduction
- Build classification and regression models
Certificate of Achievement
Quality Licence Scheme Endorsed Certificate
Upon completing the final assessment, you can apply for the Quality Licence Scheme Endorsed Certificate of Achievement. Endorsed certificates can be ordered and delivered to your home by post for only £129.
Order Your QLS Certificate
An extra £10 postage charge will be required for students leaving overseas.
Skill Up Recognised Certificate
Upon successful completion of the Data Science & Machine Learning with Python course, you have the opportunity to request a Skill Up Recognised Certificate. This certificate holds significant value, and its validation will endure throughout your lifetime.
- 1. PDF Certificate + PDF Transcript: £14.99
- 2. Hardcopy Certificate + Hardcopy Transcript: £19.99
- 3. Delivery Charge: £10.00 (Applicable for International Students)
Earn Your Accredited Certificate with Transcript
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Show Your Certified Identity with a CPD-QS Certificate
Perfect for employers, clients, or academic verification.
Get a QLS Certificate to Showcase Your Skills
Detailed record of your completed modules and qualifications.
Who Is This Course For?
Who Is This Course For?- Aspiring data analysts and data scientists
- Beginners interested in Python programming and analytics
- IT professionals moving into machine learning roles
- Students exploring artificial intelligence and data science careers
- Business professionals working with data-driven decisions
Career Path
Understanding data science workflows, machine learning systems, and Python-based analytics supports a wide range of technical and analytical career roles across modern industries such as technology, finance, healthcare, and digital services.
Typical career pathways include:
Data Analyst — £30,000–£45,000
Works with datasets to identify trends, create reports, and support business decision-making using data tools and visualisation techniques.
Junior Data Scientist — £35,000–£55,000
Applies statistical methods and machine learning models to analyse data and generate predictive insights for business use.
Machine Learning Engineer — £45,000–£75,000
Develops, trains, and optimises machine learning models for automation, prediction, and intelligent system applications.
Business Intelligence Analyst — £32,000–£50,000
Builds dashboards and reporting systems to transform raw data into structured business intelligence for strategic planning.
Data Engineer — £40,000–£65,000
Designs and maintains data pipelines, ensuring efficient data flow, storage, and processing for analytics and machine learning systems.
AI Analyst — £38,000–£60,000
Works on data modelling and algorithm-based systems to support artificial intelligence applications and decision support tools.
Professionals with strong capability in Python, data analysis, and machine learning can progress into senior roles such as Data Scientist, AI Engineer, Analytics Lead, and Machine Learning Architect, where responsibility expands into system design, model optimisation, and strategic data solutions.
Enrol Today – Build Data Science Capability
By enrolling in this Data Science & Machine Learning with Python course, you begin structured learning in one of the most in-demand technical fields.
Data science skills support automation, prediction, and advanced decision-making across industries such as finance, healthcare, and technology.
Start building capability in Python, machine learning, and data analysis for long-term career growth.
Frequently Asked Questions
The course is fully online and self-paced, typically completed within 6–10 weeks.
No prior experience is required. The course starts from beginner level.
Python is introduced within the course modules.
Yes, an accredited certificate is provided upon completion.
Yes, it supports data science, analytics, and machine learning career roles in the UK.
Data Science & Machine Learning with Python 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
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Course Overview & Table of Contents
00:09:00
00:09:00 -
Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types
00:05:00
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Introduction to Machine Learning – Part 2 – Classifications and Applications
00:06:00
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System and Environment preparation – Part 1
00:08:00
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System and Environment preparation – Part 2
00:06:00
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Learn Basics of python – Assignment 2
00:09:00
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Learn Basics of python – Functions
00:04:00
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Learn Basics of python – Data Structures
00:12:00
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Learn Basics of NumPy – NumPy Array
00:06:00
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Learn Basics of NumPy – NumPy Data
00:08:00
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Learn Basics of NumPy – NumPy Arithmetic
00:04:00
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Learn Basics of Matplotlib
00:07:00
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Learn Basics of Pandas – Part 1
00:06:00
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Learn Basics of Pandas – Part 2
00:07:00
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Understanding the CSV data file
00:09:00
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Load and Read CSV data file using Python Standard Library
00:09:00
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Load and Read CSV data file using NumPy
00:04:00
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Load and Read CSV data file using Pandas
00:05:00
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Dataset Summary – Peek, Dimensions and Data Types
00:09:00
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Dataset Summary – Class Distribution and Data Summary
00:09:00
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Dataset Summary – Explaining Correlation
00:11:00
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Dataset Summary – Explaining Skewness – Gaussian and Normal Curve
00:07:00
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Dataset Visualization – Using Histograms
00:07:00
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Dataset Visualization – Using Density Plots
00:06:00
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Dataset Visualization – Box and Whisker Plots
00:05:00
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Multivariate Dataset Visualization – Correlation Plots
00:08:00
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Multivariate Dataset Visualization – Scatter Plots
00:05:00
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Data Preparation (Pre-Processing) – Introduction
00:09:00
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Data Preparation – Re-scaling Data – Part 1
00:09:00
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Data Preparation – Re-scaling Data – Part 2
00:09:00
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Data Preparation – Standardizing Data – Part 1
00:07:00
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Data Preparation – Standardizing Data – Part 2
00:04:00
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Data Preparation – Normalizing Data
00:08:00
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Data Preparation – Binarizing Data
00:06:00
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Feature Selection – Introduction
00:07:00
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Feature Selection – Uni-variate Part 1 – Chi-Squared Test
00:09:00
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Feature Selection – Uni-variate Part 2 – Chi-Squared Test
00:10:00
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Feature Selection – Recursive Feature Elimination
00:11:00
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Feature Selection – Principal Component Analysis (PCA)
00:09:00
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Feature Selection – Feature Importance
00:07:00
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Refresher Session – The Mechanism of Re-sampling, Training and Testing
00:12:00
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Algorithm Evaluation Techniques – Introduction
00:07:00
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Algorithm Evaluation Techniques – Train and Test Set
00:11:00
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Algorithm Evaluation Techniques – K-Fold Cross Validation
00:09:00
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Algorithm Evaluation Techniques – Leave One Out Cross Validation
00:05:00
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Algorithm Evaluation Techniques – Repeated Random Test-Train Splits
00:07:00
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Algorithm Evaluation Metrics – Introduction
00:09:00
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Algorithm Evaluation Metrics – Classification Accuracy
00:08:00
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Algorithm Evaluation Metrics – Log Loss
00:03:00
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Algorithm Evaluation Metrics – Area Under ROC Curve
00:06:00
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Algorithm Evaluation Metrics – Classification Report
00:04:00
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Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction
00:06:00
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Algorithm Evaluation Metrics – Mean Absolute Error
00:07:00
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Algorithm Evaluation Metrics – Mean Square Error
00:03:00
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Algorithm Evaluation Metrics – R Squared
00:04:00
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Classification Algorithm Spot Check – Logistic Regression
00:12:00
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Classification Algorithm Spot Check – Linear Discriminant Analysis
00:04:00
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Classification Algorithm Spot Check – K-Nearest Neighbors
00:05:00
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Classification Algorithm Spot Check – Naive Bayes
00:04:00
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Classification Algorithm Spot Check – CART
00:04:00
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Classification Algorithm Spot Check – Support Vector Machines
00:05:00
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Regression Algorithm Spot Check – Linear Regression
00:08:00
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Regression Algorithm Spot Check – Ridge Regression
00:03:00
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Regression Algorithm Spot Check – Lasso Linear Regression
00:03:00
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Regression Algorithm Spot Check – Elastic Net Regression
00:02:00
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Regression Algorithm Spot Check – K-Nearest Neighbors
00:06:00
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Regression Algorithm Spot Check – CART
00:04:00
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Regression Algorithm Spot Check – Support Vector Machines (SVM)
00:04:00
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Compare Algorithms – Part 1 : Choosing the best Machine Learning Model
00:09:00
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Compare Algorithms – Part 2 : Choosing the best Machine Learning Model
00:05:00
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Pipelines : Data Preparation and Data Modelling
00:11:00
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Pipelines : Feature Selection and Data Modelling
00:10:00
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Performance Improvement: Ensembles – Voting
00:07:00
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Performance Improvement: Ensembles – Bagging
00:08:00
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Performance Improvement: Ensembles – Boosting
00:05:00
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Performance Improvement: Parameter Tuning using Grid Search
00:08:00
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Performance Improvement: Parameter Tuning using Random Search
00:06:00
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Export, Save and Load Machine Learning Models : Pickle
00:10:00
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Export, Save and Load Machine Learning Models : Joblib
00:06:00
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Finalizing a Model – Introduction and Steps
00:07:00
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Finalizing a Classification Model – The Pima Indian Diabetes Dataset
00:07:00
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Quick Session: Imbalanced Data Set – Issue Overview and Steps
00:09:00
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Iris Dataset : Finalizing Multi-Class Dataset
00:09:00
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Finalizing a Regression Model – The Boston Housing Price Dataset
00:08:00
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Real-time Predictions: Using the Pima Indian Diabetes Classification Model
00:07:00
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Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
00:03:00
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Real-time Predictions: Using the Boston Housing Regression Model
00:08:00
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Resources – Data Science & Machine Learning with Python
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Submit Your Assignment & Order QLS Certificate
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Skill Up Recognised Certificate
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Order CPDQS Certificate
Offer Ends in
TAKE ALL COURSES FOR £99-
Duration:10 hours, 24 minutes
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Access:1 Year
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Units:93
