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Course Overview
Learn Python for data analysis online — from scratch to confident understanding, with CPD accreditation.
Curious how Python can transform raw data into actionable insights? The Python for Data Analysis Course is a CPD-accredited online programme designed for learners who want to explore data manipulation, visualisation, and foundational machine learning concepts using Python. This course equips you with the knowledge to understand trends, make data-informed decisions, and advance your career in analytics, AI, or business intelligence.
With demand for Python skills in the UK growing by over 30% across finance, healthcare, tech, and business analytics, mastering Python has never been more valuable. Learners will explore NumPy, Pandas, Matplotlib, Seaborn, Plotly, and scikit-learn, gaining confidence in handling datasets, producing meaningful visualisations, and understanding machine learning concepts. This course provides theoretical and applied knowledge aligned with professional standards and CPD accreditation.
Course Overview
Structured into focused modules, this CPD-accredited Python for Data Analysis Course covers:
- Python fundamentals: variables, data types, control flow, and functions
- Data manipulation using NumPy and Pandas for structured datasets
- Visualising data with Matplotlib, Seaborn, and Plotly
- Creating interactive and dashboard-ready visualisations
- Machine learning fundamentals with scikit-learn: regression, classification, clustering
- Advanced techniques including PCA, recommender systems, and NLP basics
- Integrating SQL and Python for data handling
- Final capstone project analysing real-world datasets
Each module is designed to help learners grasp Python concepts, analyse data effectively, and understand the theoretical frameworks used in modern data roles. Enrol today!
Learning Outcomes
By completing this Python for Data Analysis course, you will be able to:
- Set up Python environments using Jupyter, Anaconda, or Google Colab
- Write clear, structured Python code for data analysis
- Perform data wrangling and transformation using NumPy and Pandas
- Build data visualisations with Matplotlib, Seaborn, and Plotly
- Understand foundational machine learning principles
- Analyse unstructured data using NLP concepts
- Integrate SQL with Python for querying and managing datasets
- Apply Python knowledge to business, finance, healthcare, and tech contex
Why Enrol in This Course?
Python is one of the most in-demand technical skills in the UK. Enrolling in this CPD-accredited Python for Data Analysis course allows you to:
- Gain theoretical knowledge and applied understanding of Python for analytics
- Study flexibly at your own pace with 24/7 online access
- Learn with examples relevant to business, healthcare, finance, and technology
- Build confidence in data analysis, visualisation, and predictive modelling
- Enhance employability in data science, AI, and analytics careers
Who is This Course for?
This Python for Data Analysis course is designed for anyone looking to gain confidence in working with data and Python. Whether you’re starting your career, transitioning into analytics, or seeking to enhance your technical skills, this training equips you with knowledge that you can apply directly in the workplace.
- Aspiring data analysts and Python developers
- Students and graduates seeking technical skills for modern careers
- Professionals in finance, business, healthcare, and tech wanting data literacy
- Career switchers moving into analytics or AI
- Self-learners aiming for CPD-accredited Python knowledge
Prerequisites for This Course
There are no formal prerequisites to enrol in the Python for Data Analysis course. It is suitable for:
- Beginners with zero programming background
- Professionals transitioning into data-related roles
- University students looking to boost employability
- Anyone with a laptop and internet connection
Certification
Upon completing the Python for Data Analysis course and passing the final exam, you will receive a CPD QS Accredited Certificate from Apex Learning. This certification demonstrates your knowledge and skills as a nursing assistant in the UK. The Digital version of the certificate is available for £9.99 only, and you can order a hard copy of the certificate for £14.99 only.
Career Path
Python for Data Analysis
Learn Python for data analysis online — from scratch to confident understanding, with CPD accreditation.
Curious how Python can transform raw data into actionable insights? The Python for Data Analysis Course is a CPD-accredited online programme designed for learners who want to explore data manipulation, visualisation, and foundational machine learning concepts using Python. This course equips you with the knowledge to understand trends, make data-informed decisions, and advance your career in analytics, AI, or business intelligence.
With demand for Python skills in the UK growing by over 30% across finance, healthcare, tech, and business analytics, mastering Python has never been more valuable. Learners will explore NumPy, Pandas, Matplotlib, Seaborn, Plotly, and scikit-learn, gaining confidence in handling datasets, producing meaningful visualisations, and understanding machine learning concepts. This course provides theoretical and applied knowledge aligned with professional standards and CPD accreditation.
Course Overview
Structured into focused modules, this CPD-accredited Python for Data Analysis Course covers:
- Python fundamentals: variables, data types, control flow, and functions
- Data manipulation using NumPy and Pandas for structured datasets
- Visualising data with Matplotlib, Seaborn, and Plotly
- Creating interactive and dashboard-ready visualisations
- Machine learning fundamentals with scikit-learn: regression, classification, clustering
- Advanced techniques including PCA, recommender systems, and NLP basics
- Integrating SQL and Python for data handling
- Final capstone project analysing real-world datasets
Each module is designed to help learners grasp Python concepts, analyse data effectively, and understand the theoretical frameworks used in modern data roles. Enrol today!
Learning Outcomes
By completing this Python for Data Analysis course, you will be able to:
- Set up Python environments using Jupyter, Anaconda, or Google Colab
- Write clear, structured Python code for data analysis
- Perform data wrangling and transformation using NumPy and Pandas
- Build data visualisations with Matplotlib, Seaborn, and Plotly
- Understand foundational machine learning principles
- Analyse unstructured data using NLP concepts
- Integrate SQL with Python for querying and managing datasets
- Apply Python knowledge to business, finance, healthcare, and tech contex
Following Courses are Included With This Course (For Free) - Limited Time!
Why Enrol in This Python for Data Analysis Course?
Python is one of the most in-demand technical skills in the UK. Enrolling in this CPD-accredited Python for Data Analysis course allows you to:
- Gain theoretical knowledge and applied understanding of Python for analytics
- Study flexibly at your own pace with 24/7 online access
- Learn with examples relevant to business, healthcare, finance, and technology
- Build confidence in data analysis, visualisation, and predictive modelling
- Enhance employability in data science, AI, and analytics careers
Who is This Python for Data Analysis Course for?
This Python for Data Analysis course is designed for anyone looking to gain confidence in working with data and Python. Whether you’re starting your career, transitioning into analytics, or seeking to enhance your technical skills, this training equips you with knowledge that you can apply directly in the workplace.
- Aspiring data analysts and Python developers
- Students and graduates seeking technical skills for modern careers
- Professionals in finance, business, healthcare, and tech wanting data literacy
- Career switchers moving into analytics or AI
- Self-learners aiming for CPD-accredited Python knowledge
Prerequisites
There are no formal prerequisites to enrol in the Python for Data Analysis course. It is suitable for:
- Beginners with zero programming background
- Professionals transitioning into data-related roles
- University students looking to boost employability
- Anyone with a laptop and internet connection
Assessment Method
Assessment for the course is conducted through an automated multiple-choice exam. A score of 60% is required to pass and earn the CPD Accredited Certificate. Reflective assignments are provided to enhance your learning, with expert tutor feedback available.
Certification
Upon completing the Python for Data Analysis course and passing the final exam, you will receive a CPD QS Accredited Certificate from Apex Learning. This certification demonstrates your knowledge and skills as a nursing assistant in the UK. The Digital version of the certificate is available for £9.99 only, and you can order a hard copy of the certificate for £14.99 only.
Career Path
FAQs: Python for Data Analysis
Is Python good for data analysis?
Yes. Python is widely used in the UK for data analysis due to its versatility, ease of learning, and extensive library support. It allows professionals to clean, manipulate, visualise, and analyse datasets efficiently, making it a preferred language for business intelligence, analytics, AI, and research applications.
Which Python is best for data analysis?
For data analysis in the UK, Python 3 is the recommended version, as it is fully supported by libraries such as NumPy, Pandas, Matplotlib, Seaborn, Plotly, and scikit-learn. Python 3 ensures compatibility with modern data tools and frameworks commonly used by UK businesses, finance, healthcare, and tech industries.Â
What is the Python for Data Analysis course?
This is a CPD-accredited online course that teaches Python programming, data analysis, and data visualisation. It is designed for beginners and professionals seeking to develop theoretical knowledge for data-driven roles.
Do I need any prior programming experience to enrol?
No. The Python for Data Analysis course starts with Python fundamentals and gradually introduces data analysis concepts, making it suitable for complete beginners or career switchers.
What tools will I learn in this Python course?
You’ll work with Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Plotly, Scikit-learn, and NLTK.
Will I learn machine learning in this course?
Yes. The course covers supervised and unsupervised learning algorithms using real datasets.
Is this Python certificate recognised?
Yes. The certificate is CPD QS Accredited and respected by employers across the UK and internationally.
Is this course suitable for career changers?
Yes. This Python for Data Analysis course is ideal for beginners or professionals looking to gain theoretical knowledge of Python for data analysis. It provides a strong foundation in data concepts, visualisation, and machine learning principles, helping learners understand how Python is applied in analytics, AI, and data-focused roles in the UK.
Ready to explore data with Python? Enrol today and get instant access.
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✔ CPD accreditedÂ
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Course Curriculum
- 21 sections
- 42 lectures
- 00:00:00 total length
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Welcome & Course Overview 00:07:00 -
Set-up the Environment for the Course (lecture 1) 00:09:00
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Set-up the Environment for the Course (lecture 2) 00:25:00
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Two other options to setup environment 00:04:00
Welcome & Course Overview -
Python data types Part 1 00:21:00
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Python Data Types Part 2 00:15:00
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Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00
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Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00
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Python Essentials Exercises Overview 00:02:00
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Python Essentials Exercises Solutions 00:22:00
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What is Numpy? A brief introduction and installation instructions. 00:03:00
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NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. 00:28:00
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NumPy Essentials – Indexing, slicing, broadcasting & boolean masking 00:26:00
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NumPy Essentials – Arithmetic Operations & Universal Functions 00:07:00
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NumPy Essentials Exercises Overview 00:02:00
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NumPy Essentials Exercises Solutions 00:25:00
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What is pandas? A brief introduction and installation instructions. 00:02:00
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Pandas Introduction 00:02:00
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Pandas Essentials – Pandas Data Structures – Series 00:20:00
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Pandas Essentials – Pandas Data Structures – DataFrame 00:30:00
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Pandas Essentials – Handling Missing Data 00:12:00
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Pandas Essentials – Data Wrangling – Combining, merging, joining 00:20:00
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Pandas Essentials – Groupby 00:10:00
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Pandas Essentials – Useful Methods and Operations 00:26:00
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Pandas Essentials – Project 1 (Overview) Customer Purchases Data 00:08:00
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Pandas Essentials – Project 1 (Solutions) Customer Purchases Data 00:31:00
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Pandas Essentials – Project 2 (Overview) Chicago Payroll Data 00:04:00
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Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00
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Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach 00:13:00
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Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach 00:22:00
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Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach 00:22:00
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Matplotlib Essentials – Exercises Overview 00:06:00
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Matplotlib Essentials – Exercises Solutions 00:21:00
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Seaborn – Introduction & Installation 00:04:00
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Seaborn – Distribution Plots 00:25:00
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Seaborn – Categorical Plots (Part 1) 00:21:00
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Seaborn – Categorical Plots (Part 2) 00:16:00
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Seborn-Axis Grids 00:25:00
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Seaborn – Matrix Plots 00:13:00
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Seaborn – Regression Plots 00:11:00
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Seaborn – Controlling Figure Aesthetics 00:10:00
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Seaborn – Exercises Overview 00:04:00
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Seaborn – Exercise Solutions 00:19:00
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Pandas Built-in Data Visualization 00:34:00
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Pandas Data Visualization Exercises Overview 00:03:00
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Panda Data Visualization Exercises Solutions 00:13:00
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Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) 00:19:00
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Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) 00:14:00
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Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) 00:11:00
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Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) 00:37:00
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Project 1 – Oil vs Banks Stock Price during recession (Overview) 00:15:00
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00
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Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) 00:03:00
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Introduction to ML – What, Why and Types….. 00:15:00
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Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00
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scikit-learn – Linear Regression Model – Hands-on (Part 1) 00:17:00
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scikit-learn – Linear Regression Model Hands-on (Part 2) 00:19:00
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Good to know! How to save and load your trained Machine Learning Model! 00:01:00
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scikit-learn – Linear Regression Model (Insurance Data Project Overview) 00:08:00
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scikit-learn – Linear Regression Model (Insurance Data Project Solutions) 00:30:00
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Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. 00:10:00
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scikit-learn – Logistic Regression Model – Hands-on (Part 1) 00:17:00
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scikit-learn – Logistic Regression Model – Hands-on (Part 2) 00:20:00
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scikit-learn – Logistic Regression Model – Hands-on (Part 3) 00:11:00
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scikit-learn – Logistic Regression Model – Hands-on (Project Overview) 00:05:00
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scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) 00:15:00
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Theory: K Nearest Neighbors, Curse of dimensionality …. 00:08:00
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scikit-learn – K Nearest Neighbors – Hands-on 00:25:00
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scikt-learn – K Nearest Neighbors (Project Overview) 00:04:00
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scikit-learn – K Nearest Neighbors (Project Solutions) 00:14:00
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Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. 00:18:00
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scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) 00:19:00
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scikit-learn – Decision Tree and Random Forests (Project Overview) 00:05:00
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scikit-learn – Decision Tree and Random Forests (Project Solutions) 00:15:00
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Support Vector Machines (SVMs) – (Theory Lecture) 00:07:00
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scikit-learn – Support Vector Machines – Hands-on (SVMs) 00:30:00
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scikit-learn – Support Vector Machines (Project 1 Overview) 00:07:00
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scikit-learn – Support Vector Machines (Project 1 Solutions) 00:20:00
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scikit-learn – Support Vector Machines (Optional Project 2 – Overview) 00:02:00
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Theory: K Means Clustering, Elbow method ….. 00:11:00
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scikit-learn – K Means Clustering – Hands-on 00:23:00
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scikit-learn – K Means Clustering (Project Overview) 00:07:00
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scikit-learn – K Means Clustering (Project Solutions) 00:22:00
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Theory: Principal Component Analysis (PCA) 00:09:00
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scikit-learn – Principal Component Analysis (PCA) – Hands-on 00:22:00
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scikit-learn – Principal Component Analysis (PCA) – (Project Overview) 00:02:00
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scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) 00:17:00
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Theory: Recommender Systems their Types and Importance 00:06:00
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Python for Recommender Systems – Hands-on (Part 1) 00:18:00
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Python for Recommender Systems – – Hands-on (Part 2) 00:19:00
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Natural Language Processing (NLP) – (Theory Lecture) 00:13:00
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NLTK – NLP-Challenges, Data Sources, Data Processing ….. 00:13:00
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NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00
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NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. 00:19:00
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NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … 00:13:00
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NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… 00:09:00
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Resources- Python for Data Analysis 00:00:00
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Claim Your Certificate 00:00:00
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Thank you for completing your course with us! We’d appreciate your feedback! 00:00:00
£21.99
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Duration:1 day
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Access:1 year access
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Level:
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CPD Points:10