Course Content
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Introduction to Data Science & Machine Learning
- Data science lifecycle
- Data science vs analytics vs engineering
- Machine learning vs traditional programming
- Types of machine learning (supervised, unsupervised)
- Regression vs classification
- Training vs testing data
- Overfitting vs underfitting
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Data Preparation Fundamentals
- Introduction to data preparation
- Features vs target variable
- Train/test split
- Handling missing values
- Introduction to data quality
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Data Cleaning & Encoding
- Handling categorical variables
- Encoding techniques (label encoding, one-hot encoding)
- Identifying categorical vs numerical data
- Data transformation basics
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Feature Engineering
- Creating new features
- Extracting date-based features
- Binning and grouping data
- Log transformation
- Feature importance (concept)
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Data Preparation Pipeline
- Feature scaling (StandardScaler, MinMaxScaler)
- Feature selection (correlation, basic techniques)
- Building a clean data pipeline
- Preparing data for modelling
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Supervised Learning: Regression
- Introduction to regression
- Linear regression (concept and Python)
- Multiple linear regression
- Making predictions
- Model evaluation (MAE, MSE, R²)
- Visualising regression results
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Supervised Learning: Classification
- Introduction to classification
- Binary vs multi-class classification
- Logistic regression (concept and Python)
- Model evaluation (accuracy, precision, recall, confusion matrix)
- Improving classification performance
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Decision Trees and Random Forest
- Concept and intuition
- Decision Tree in Python
- Controlling model complexity
- Random Forest in Python
- Comparing with Decision Trees
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Time Series & Forecasting
- Time-based data concepts
- Basic forecasting techniques
- Real-world business applications
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Unsupervised Learning: Clustering
- Introduction to clustering
- K-Means clustering (concept)
- K-Means in Python
- Choosing number of clusters (Elbow method)
- Business applications of clustering
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Model Evaluation & Deployment
- Why model evaluation matters
- Choosing the right evaluation metric
- Saving and loading models (pickle)
- Building simple prediction workflows
- Introduction to deployment concepts
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Final Capstone Project
- End-to-end machine learning project
- Data preparation → modelling → evaluation → deployment
- Real-world business dataset
Course Dates, Prices & Enrolment
Scroll right for more details
| Delivery Method | Dates & Times | Hours | Price | Enrolment |
Classroom Training
|
15 Jun 2026 - 19 Jun 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM
BT
| 12-hour over 3-day |
| Enrol Now |
Classroom Training
|
20 Jul 2026 - 21 Jul 2026
Monday to Tuesday
10:00 AM - 04:00 PM
BT
| 12-hour over 2-day |
| Enrol Now |
Classroom Training
|
17 Aug 2026 - 21 Aug 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM
BT
| 12-hour over 3-day |
| Enrol Now |
Classroom Training
|
21 Sep 2026 - 22 Sep 2026
Monday to Tuesday
10:00 AM - 04:00 PM
BT
| 12-hour over 2-day |
| Enrol Now |
Training venue: Unit 15, Boardman House, 64 Broadway, Stratford, London E15 1NT, United Kingdom
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Frequently Asked Questions
Do I need prior Python knowledge for this course?
Yes, basic knowledge of Python is recommended for this course. You should be familiar with fundamental concepts such as variables, loops, and functions. If you are new to Python, we recommend completing a beginner Python course first.
What will I learn in this Data Science with Python course?
You will learn how to work with data using Python, including data cleaning, analysis, visualisation, and introductory machine learning techniques. The course covers tools such as Pandas, NumPy, and Matplotlib, which are widely used in data science.
Will I get hands-on experience during the course?
Yes, this course is highly practical. You will work with real datasets and apply data science techniques step by step, helping you build confidence and practical skills.
What tools or software will I use?
You can use tools such as Jupyter Notebook (via Anaconda) or Google Colab, which are widely used in data science.
You may also use other environments such as Visual Studio Code or PyCharm, as long as required libraries like Pandas, NumPy, and Matplotlib are available.
What is data science and why is it important?
Data science involves analysing data to extract insights, identify patterns, and support decision-making. It combines programming, statistics, and domain knowledge, and is widely used across industries such as finance, healthcare, and technology.
Is Data Science with Python useful for my career?
Yes, data science is one of the most in-demand fields. Python is widely used for data analysis, machine learning, and automation, making it a valuable skill for many data-related roles.
What jobs can I apply for after completing this course?
After completing this course, you can pursue roles such as Data Analyst, Junior Data Scientist, Business Analyst, or Data Engineer (entry-level). It also provides a strong foundation for advanced data science and AI roles.
Does this course include machine learning?
Yes, the course introduces basic machine learning concepts, including how models are used to analyse data and make predictions. You will gain an understanding of how machine learning fits into the data science process.
Is this course enough to become a data scientist?
This course provides a strong foundation in data science using Python and can help you take the first step towards a Junior Data Scientist role. You can further enhance your skills by learning advanced topics such as deep learning and large language models (LLMs).
How is the course delivered?
The course is instructor-led and can be attended live online, in person at our London training centre, or delivered on-site at your organisation for corporate training.
Will I receive a certificate after completing the course?
Yes, you will receive a certificate of completion after successfully finishing the course.
What should I learn after completing this course?
After completing this course, you can progress to more advanced topics such as machine learning, artificial intelligence, deep learning, and generative AI. You may also explore areas like large language models (LLMs), automation, and intelligent systems to further develop your data science skills.
What is the difference between data analysis and data science?
Data analysis focuses on interpreting existing data to generate insights, while data science includes advanced techniques such as machine learning, predictive modelling, and building data-driven applications.