ME314: Introduction to Data Science and Machine Learning

Data Science combines scientific inquiry, statistical knowledge and computer programming with a focus on learning powerful insights from big data.

Businesses use data to plan, evaluate, innovate, and respond quickly to shifts in the market. Likewise, governments use to data to guide decisions and evaluate public policies. Other organisations, from NGOs to universities are increasingly turning to data for actionable insights.

This course provides a first step into this exciting world, blending classical statistical methods with recent advances in causal inference, machine learning, and computational data science. You will cover key topics such as the computational landscape of modern data science, the fundamentals of "data," probability and regression, methods for establishing cause and effect, and tools for predicting, classifying, and clustering outcomes. Moreover, you will learn how data science contributes to knowledge generation and can improve decision-making. You will also explore some of the most exciting frontiers of data science, including large language models, generative AI and "unstructured" data like images and maps.

Engaging with leading faculty, you will not only learn many of the core analytical methods for modern data science, but will engage in hands-on application of these methods using both real-world and simulated data. This course offers you the experience necessary to build confidence in using these methods in the real world.

Prerequisites: Students should be familiar with quantitative methods at an introductory level, up to linear regression analysis, and should be generally comfortable engaging quantitative notation and concepts. The course will use the R and python programming languages, so some exposure to computer programming with either language is encouraged although not formally required.

Students will be expected to have a laptop that they can bring to class, which can run the required statistical software and do the required data analysis—for example, a recent MacBook Pro/Air or Windows/Linux laptop with at least 8 GB RAM, and not a Chromebook or a tablet. Please contact the instructors if you have any questions or concerns.

Level: 300 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: One take-home assessment and one in-person exam

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

This course is suitable if you already have prior training in quantitative methods and coding, and want to enhance this training with the fundamental concepts and techniques of Data Science and Data Analytics. It is also suitable for practitioners from industry, government, or research organisations with some basic training in quantitative analysis or computer programming.

The course surveys diverse techniques and methods, making it an ideal foundation for more advanced or specific training. If you are targeting a role in government, politics, data science, research, law, business management, consulting or marketing you should consider this course.

• Gain knowledge of and practical experience working with data using statistical methods, R, Python and modern data science workflows

• Learn how to draw credible conclusions from data and connect empirical results to real-world questions, theories and decisions

• Learn to acquire, clean, transform, store and analyse both structured and unstructured data from diverse sources, including APIs and the web

• Apply core techniques in data wrangling and visualisation

• Understand the fundamentals of statistical inference including probability and linear regression

• Gain an overview of causal inference for data science, including both experimentation ("A/B testing") and observational causal inference

• Gain an overview of machine learning methods and related techniques for assessing model fit and cross-validating predictive models

• Learn techniques for text-as-data analysis, including working with large language models and data from social media

• Build the ability to use generative AI effectively in applied data science workflows, including critically evaluating outputs

The design of this course is guided by LSE faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.