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Overview
Key facts
Organisations are turning to customer data in order to innovate and respond quickly to shifts in the market. Meanwhile, Governments are using to data to help guide policy decisions, making this a prime area for social scientists with an interest in quantitative methods.
London School of Economics and Political Science's Introduction to Data Science and Machine Learning course aims to provide an introduction to the quantitative analysis of data, blending classical statistical methods with recent advances in computational and machine learning. You will cover key topics such as the challenges of analysing big data using statistical methods, and how machine learning and data science can aid in knowledge generation and improve decision-making. You will also explore quantitative methods of text analysis, including mining social media and other online resources.Engaging with leading faculty, you will cover the main analytical methods from this field and the hands-on application of these methods using example datasets. As a result, the course allows you to gain experience and confidence in using the methods covered during the course in different contexts.Programme Structure
Courses include:
- The challenges of analysing big data using statistical methods
- Knowledge generation and decision-making
- Data acquisition, processing, conversion and storage
- Normalising data
- Data management using SQLite
- Statistical inference
Key information
Duration
- Full-time
- 19 days
Start dates & application deadlines
- StartingApply anytime.
More details
- Visit the website for full details.
Language
English
Credits
7 ECTS
Delivered
On Campus
Campus Location
- London, United Kingdom
Disciplines
Data Science & Big Data Machine Learning View 66 other Short Courses in Data Science & Big Data in United KingdomWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
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.
Student insurance
Make sure to cover your health, travel, and stay while studying abroad. Even global coverages can miss important items, so make sure your student insurance ticks all the following:
- Additional medical costs (i.e. dental)
- Repatriation, if something happens to you or your family
- Liability
- Home contents and baggage
- Accidents
- Legal aid
We partnered with Aon to provide you with the best affordable student insurance, for a carefree experience away from home.
Get your student insurance nowStarting from €0.53/day, free cancellation any time.
Remember, countries and universities may have specific insurance requirements. To learn more about how student insurance work at London School of Economics and Political Science and/or in United Kingdom, please visit Student Insurance Portal.
Tuition Fee
Every graduate student is charged a fee for their programme. Visit the website for more information about the fees.
Living costs for London
1137 - 2157 GBP /month
Living costs
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
The School recognizes that the cost of living in London may be higher than in your home town or country, and we provide generous scholarships each year to home and overseas students. Find out more about financial support.
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