Python for Data Science and Machine Learning

Learn Python for data science and machine learning through practical, instructor-led training in the UK. Use NumPy, pandas, Matplotlib, Seaborn, and scikit-learn to clean data, analyse datasets, build models, and communicate insights.

about this course

Python for Data Science and Machine Learning is a practical, instructor-led course designed to help learners use Python to work with real datasets, explore patterns, build machine learning models, and communicate results clearly. The course introduces the core Python data stack, including Jupyter Notebook, NumPy, pandas, Matplotlib, Seaborn, and scikit-learn. Learners practise importing data, cleaning messy files, analysing trends, creating visualisations, preparing features, training supervised learning models, checking model performance, and presenting findings in a simple project workflow. It is suitable for learners who want to move beyond basic Python into data analysis, data science, AI, and machine learning work without jumping straight into advanced mathematics or deep learning.

UK-Based
IT Provider

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No pre-recorded lessons

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Python for Data Science and Machine Learning Course Overview and Modules

Want to turn raw data into useful insights and start building machine learning models with Python? This hands-on course takes you from practical Python data handling to model building using the tools commonly used in data science projects. You will work with datasets, clean and transform data, create charts, train simple models, evaluate results, and understand how a typical data science workflow fits together. This course can help you:

Use Python and Jupyter Notebook for practical data science work
Work with NumPy arrays and pandas DataFrames to organise and analyse data
Clean missing, duplicated, inconsistent, and poorly formatted data
Create clear visualisations using Matplotlib and Seaborn
Build basic regression, classification, and clustering models with scikit-learn
Evaluate model performance and explain results in a simple, business-friendly way

Prerequisites

Learners should be comfortable using a computer, web browser, files, folders, and spreadsheets. Basic Python knowledge is helpful but not essential, as the course includes a practical Python refresher. A willingness to practise with datasets, code examples, and guided exercises is more important than advanced maths experience.

Certification Alignment

This is a practical skills-based Codionix course focused on Python data science and machine learning practice. It does not include a formal vendor certification exam. The course content is aligned with common Python data analysis workflows and can support further learning toward Python data analytics certifications such as Python Institute PCAD, as well as broader data science and machine learning study.

Practical Application

Includes hands-on labs using Jupyter Notebook and real-style datasets. Learners practise reading CSV files, cleaning and transforming data, exploring trends, creating charts, preparing features, splitting data for training and testing, building models with scikit-learn, comparing results, and presenting a short data science project.

Skills You Will Gain

Use Python for data analysis, manage notebooks, work with NumPy and pandas, clean and prepare datasets, create exploratory visualisations, build simple machine learning models with scikit-learn, evaluate model performance, and communicate insights from a data project.

What you will learn in Python for Data Science and Machine Learning Certification

A carefully structured overview of the core concepts, technical domains, and practical competencies you will master to build confidence and certification readiness.

Use Python and Jupyter for Data Science Work

Set up a practical Python data science workflow using Jupyter Notebook, write and run code cells, organise notebooks, and use Python basics for data tasks.

Work with NumPy and pandas

Learn how NumPy arrays and pandas DataFrames help you store, filter, join, group, summarise, and reshape data for analysis.

Clean, Prepare, and Explore Datasets

Handle missing values, duplicates, wrong data types, inconsistent text, outliers, and feature preparation so datasets are ready for analysis and modelling.

Visualise Data and Communicate Insights

Create charts for trends, distributions, relationships, and comparisons using Matplotlib and Seaborn, then explain the story behind the data.

Build Machine Learning Models with scikit-learn

Use scikit-learn to train practical regression, classification, and clustering models, including common steps such as train-test splits and preprocessing.

Evaluate Models and Improve Results

Check model performance using suitable metrics, compare results, avoid common beginner mistakes, and understand what to improve before using a model in real work.

Python for Data Science and Machine Learning Course Statistics

Everything you need to know about this certification, including exam details, delivery modes, duration, and pricing.

Certification

Python for Data Science and Machine Learning

Exam Code

—

Starting from

£375

Not sure which format works best for you? Contact us for free advice and we’ll help design a tailored training plan to match your goals. Whether you need flexible course duration, online or on-site delivery, or a customised schedule, our advisors will guide you every step of the way.

Who This Course Is For

Python for Data Science and Machine Learning is designed for aspiring data analysts, junior data scientists, Python learners, Excel or BI users, developers moving into data work, students, graduates, and career changers who want practical experience with Python data tools. It is also useful for business professionals and teams who want to understand how Python can support reporting, analysis, forecasting, and machine learning projects.

Certification & Exam Information

Course Information

AI Literacy for Productivity & Office Work

Training Format

Type: Practical instructor-led AI productivity course
Duration: 1 Day intensive or 3 Days standard
Daily Teaching Time: 4 hours per day
Delivery: Live online or private team delivery
Assessment: Guided practical exercises and workplace AI activities
Certificate Awarded: Certificate of Completion from Codionix IT Training UK
Notes: This is a skills-based course and does not include a formal certification exam

Skills Covered

AI literacy fundamentals for workplace productivity
Prompt writing and prompt improvement across common AI tools
Emails, reports, summaries, brainstorming, and document support
Practical use of ChatGPT, Claude, Gemini, and Microsoft Copilot
Responsible AI use, review, privacy awareness, and human oversight
Office productivity workflows for individuals and teams

Recommended Next Steps

Advanced AI prompting and workflow design
AI for business communication and reporting
AI automation and office process improvement

Python for Data Science and Machine Learning Careers & Salaries in the UK

Data Analyst (£42,000 avg)

Data Scientist (£55,000 avg)

Machine Learning Engineer (£76,000 avg)

Python Developer - Data/ML (£66,000 avg)

Book a Free Consultation With a Course Advisor

Frequently Asked Questions – Python for Data Science and Machine Learning

We understand that choosing the right course is important. Below are answers to common questions about training, certification, exams, and career outcomes.

What is this Python data science and machine learning course about?

This course teaches you how to use Python for practical data science and introductory machine learning. You will learn how to clean data, analyse datasets, create visualisations, build basic models, evaluate results, and explain findings using common Python tools.

Who should take this Python data science course in the UK?

It is ideal for aspiring data analysts, junior data scientists, Python learners, Excel or BI users, students, graduates, developers, and career changers who want hands-on experience with Python data tools and machine learning workflows.

Are there any prerequisites for this course?

No formal prerequisites are required. Basic Python knowledge is helpful, but the course includes a practical refresher. Learners should be comfortable using a computer, working with files, and following guided coding exercises.

Is this course suitable for beginners?

Yes. The course is beginner-friendly for data science, but it is best suited to learners who are ready to practise with code and datasets. It starts with practical foundations before moving into pandas, visualisation, and machine learning.

What Python libraries are covered in this course?

The course covers Python, Jupyter Notebook, NumPy, pandas, Matplotlib, Seaborn, and scikit-learn. Depending on the class pace, learners may also discuss notebooks, CSV files, basic statistics, feature preparation, and project workflow.

Will I build machine learning models during the course?

Yes. You will build practical introductory models such as regression, classification, and clustering models using scikit-learn. The focus is on understanding the workflow, not just copying code.

Does this course cover deep learning or neural networks?

This course focuses on core data science and traditional machine learning. Deep learning and neural networks may be introduced at a high level, but they are not the main focus of this course.

Is this course delivered live online?

Yes. The course can be delivered as live instructor-led online training, and private team delivery options can also be discussed depending on your requirements.

Does this course include a certification exam?

No. This is a practical skills-based course and does not include a formal vendor certification exam. Learners receive a Certificate of Completion from Codionix IT Training UK after successfully completing the course.

What should I do after completing this course?

After this course, you can continue with advanced machine learning, statistics, SQL, data visualisation, portfolio projects, or certification preparation such as Python Institute PCAD if you want to validate your data analytics skills.

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