Data Science: introduction
Bridge data analysis and machine learning with Python. Master data handling, create insights with modern visualization, and build predictive models using state-of-the-art AutoML techniques. Gain practical, industry-relevant skills.
Choose a starting date
Learning modes and locations may be different depending on the course start date. Please check the location of your chosen course and read our guide to learning modes and locations to help you choose the right course for you.
- Start Date: 02 Nov 2025End Date: 23 Nov 2025Sun (Daytime): 10:00 - 13:00Choose either online or in-personLocation: Hybrid (choose either online or in-person)Duration: 4 sessions (over -4 weeks)Course Code: CPPY49Tutors: Muhammad KhanFull fee £259.00 Senior fee £207.00 Concession £168.00
Please note: We offer a wide variety of financial support to make courses affordable. Just visit our online Help Centre for more information on a range of topics including fees, online learning and FAQs.
What is the course about?
This course explores the intersection between data analytics and machine learning, guiding you through the entire data science workflow—from problem understanding and data preparation to predictive modelling and result interpretation. Through practical, real-world scenarios and Python programming, you'll build both technical skills and the confidence to transform data into actionable insights relevant across various industries.
What will we cover?
The course covers each essential stage of the data science pipeline: data collection, preparation, exploratory data analysis (EDA), advanced visualization techniques, feature engineering, and statistical analysis. Participants will explore machine learning fundamentals, predictive modelling (including regression and classification), and ethical considerations in data use.
Key Python libraries such as pandas, NumPy, matplotlib, seaborn, scikit-learn, and modern AutoML tools like H2O will be utilized throughout.
What will I achieve?
By the end of this course you should be able to...
- Prepare and clean datasets effectively for analysis.
- Conduct exploratory analyses and craft insightful visualizations.
- Apply fundamental statistical methods and concepts.
- Build and evaluate foundational machine learning models (e.g., linear regression, decision trees).
- Utilize modern AutoML tools such as H2O for enhanced predictive modelling.
- Communicate analytical findings clearly through data storytelling techniques.
- Understand the impact and ethical implications of data science in decision-making.
What level is the course and do I need any particular skills?
This course is suitable for learners with basic familiarity with Python and data handling or those highly motivated to acquire these foundational skills quickly. Although beginner-friendly, prior programming experience and data manipulation knowledge will be advantageous. The initial sessions provide foundational programming and data analysis basics, ensuring all participants have a strong starting point. An enthusiasm for problem-solving and data-driven insights is essential.
How will I be taught, and will there be any work outside the class?
Teaching methods include concise lectures, live coding demonstrations, interactive discussions, and practical exercises using real-world datasets. Most learning activities occur during class; however, optional exercises and mini projects are available for those seeking further exploration and skill enhancement outside class hours.
Are there any other costs? Is there anything I need to bring?
There are no additional costs. All software used on the course is free to download and use and your tutor will recommend where to find this software. Unfortunately due to the range of hardware and software used by students at home, the College is unable to provide advice on installation issues.
When I've finished, what course can I do next?
Please click here to view our Programming and Maths courses.
Disclaimer: Use of Third-Party Software
This course might require you to either use your own personal account or create an account for the purposes of this course. City Lit cannot accept any responsibility for any failings of the third party or provide technical support. Whilst using the software you will be responsible for abiding by the providers terms and conditions and maintaining your own work.
Muhammad is a passionate and experienced tutor currently studying for his PhD in Artificial Intelligence. With a strong background as a former Software Engineer and programming tutor, Muhammad combines his deep academic knowledge with practical industry experience to deliver exceptional educational experiences. Notably, he is the first to create a UAV navigation algorithm using Dispersive Flies Optimization (DFO), which outperformed conventional benchmarks typically employed by major corporations. Dedicated to making advanced technology concepts accessible for all, Muhammad is the creator of the 2-step method to mastering any technological skill from conception to completion, where each lesson is related to individually tailored experiences whilst still adhering to a consistent group-based approach. His goal is to democratize AI and technology, ensuring that these powerful tools are available to and usable by every segment of society through digital literacy and empowerment.
Please note: We reserve the right to change our tutors from those advertised. This happens rarely, but if it does, we are unable to refund fees due to this. Our tutors may have different teaching styles; however we guarantee a consistent quality of teaching in all our courses.