Course ID
Key schedule and booking details
Length 5 Days
Location online
Fees £ 1850
Date 2026-05-18
OVERVIEW
The world is made up of data, the sheer amount of information that happens every second is staggering. Social media interactions, business transactions and everything in between, data flows endlessly around us. The Introduction to Data Science course was crafted by endless experience with data science professionals for one particular goal! To help you make sense of complex data and apply it to real-world problems.
Over five intensive days, you’ll dive into the fundamentals of data science, from the basics of Python programming to more advanced topics like machine learning, deep learning, and natural language processing (NLP). By the end of this course, you’ll not only have a strong foundation in data science but also practical experience in solving real-world challenges through data-driven insights.
With a focus on real-world applications, you’ll explore how data science transforms industries, learn how to clean and manage data, build predictive models, and gain insight into the ethical considerations of working with data. Whether you're aiming to upskill, change careers, or simply explore the vast possibilities of data science, this course is your gateway to becoming a confident data-driven problem solver.
You’ll Learn How To:
Understand the core concepts of data science and its importance across various industries.
• Set up a Python environment and use it for data science tasks, including data collection, storage, and processing.
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• Perform exploratory data analysis (EDA) to uncover insights from datasets and improve data quality.
If you’ve ever thought, “I wish I could make sense of all this data and use it to solve real problems,” this course is your chance to turn that thought into action
OBJECTIVES
By the end of The Introduction to Data Science course, you’ll have a well-rounded toolkit that empowers you to manipulate data, build predictive models, and make informed decisions with ease. Here’s what you’ll gain:
Proficiency in Python for data science, including data manipulation and analysis.
• Hands-on experience with popular data science libraries like Pandas, NumPy, Matplotlib, and Scikit-learn.
• The ability to build and train machine learning models, and evaluate their performance.
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• Knowledge of deep learning and NLP techniques, and their practical applications.
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• Familiarity with cutting-edge trends in AI, Big Data, and IoT.
An understanding of the ethical challenges in data science and how to approach them responsibly.
IDEAL PARTICIPANTS
The Introduction to Data Science course is perfect for:
Aspiring data scientists or analysts looking to gain foundational skills in data science.
• Business professionals who want to better understand data and its role in decision-making.
• Software developers aiming to expand their knowledge into data science and machine learning.
• Students or graduates interested in launching a career in the growing field of data science.
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• Anyone curious about how data can be used to drive innovation and solve real-world problems.
OUTLINE
DAY 1
Data science is about using data to make decisions, not just about doing math. We'll establish the foundation for comprehending data science concepts and using Python in practice today. Describe data science. Why it matters and how industries are being shaped by it.
• Important jobs in a data science team: what each role does and why it matters.
• The foundation of data analysis is an understanding of data kinds and structures.
• How data is handled, including gathering, storing, and processing.
• Data science with Python Configuring your environment, writing your first piece of code, and comprehending the fundamentals of syntax.
• DAY 2
Before you can make data work for you, you need to understand it. Today’s focus is on exploratory data analysis (EDA) and feature engineering—two crucial steps in preparing high-quality data for machine learning. Introduction to statistics in data science – Using descriptive and inferential statistics in Python.
• How to conduct EDA – Identifying patterns, spotting anomalies, and forming hypotheses.
• Data cleaning techniques – Handling missing values, outliers, and inconsistencies.
• Feature engineering – Creating new variables, encoding categorical data, and scaling features for better model performance.
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• Hands-on with Python libraries – Using Pandas, NumPy, and Matplotlib for EDA and feature engineering.
DAY 3
Now that we have clean data, it’s time to make predictions. Today’s session is all about understanding machine learning techniques and building your first models. What is machine learning? – Its role in data science and how it powers business decisions.
• Types of machine learning – Supervised, unsupervised, and reinforcement learning explained.
• Building basic machine learning models – Linear and logistic regression, decision trees, clustering techniques, and XGBoost.
• Model training and validation – Understanding overfitting, cross-validation, and key performance metrics.
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• Hands-on implementation – Training, testing, and optimizing models with Scikit-learn in Python.
DAY 4
AI isn’t just about structured data—deep learning and NLP help make sense of complex, unstructured information. Today, we dive into neural networks and language-based AI.
• Introduction to deep learning frameworks – TensorFlow and PyTorch basics.
What is deep learning? – How neural networks process data and solve complex problems.
• Natural Language Processing (NLP) – Text processing, sentiment analysis, and language modeling.
• Hands-on practice – Building simple deep learning models and NLP applications in Python.
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• Real-world AI applications – Exploring deep learning in image recognition, chatbots, and voice assistants.
DAY 5
The future of AI is here, but how do we use it responsibly? In our final session, we explore Generative AI, future trends, and the ethical implications of working with data. Understanding Generative AI – How models like GANs (Generative Adversarial Networks) create new data.
• Hands-on with Generative AI – Experimenting with generative models in Python.
• The ethics of data science – Addressing bias, privacy concerns, and fairness in AI.
• What’s next in data science? – The impact of AI, Big Data, and IoT on the industry.
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• Building a future-proof skill set – How to stay ahead in an evolving data-driven world.
Will I receive course materials?
Yes, high-quality documentation is provided to all delegates.
Do you issue certificates?
An accredited Certificate of Completion is awarded upon successful completion.
What are the course timings?
09:00–12:45 or 13:00–17:00.
How do I register and pay?
Complete the registration form on the course page and select your preferred payment method.
What is your cancellation policy?
14 days from booking for a full refund or free transfer; exceptions apply on medical grounds.
Do you offer airport transfers?
Yes, airport pick-up and drop-off to/from the hotel can be arranged.
CONSULTING SERVICES
Tailored solutions for sustainable growth
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