Summer Programme – AI Business

Python is an important tool in our quest to describe and analyse data. In order to be able to present findings in a way that non-specialists can understand, we need to be able to import, create and visualise data. There are numerous business applications where data are a necessary and vital part of the process. This course aims to introduce students to the basic tools and statistics for business using python. The course is designed for students who do not necessarily have a strong programming or/and statistical background. The concepts introduced in this course can be applied to various topics in business, such as business analytics, finance and financial markets, corporate concepts, business networks and more.

TOPICS COVERED

This course includes the following main topics:

• Variables and lists in python
• Important python packages for business data such as pandas
• Importing different types of datasets from different sources.
• Understanding your data and dealing with data issues such as missing values
• Data cleaning and data reshaping
• Describing the data and their important characteristics
• Data visualisation
• Basic statistics using python (different variables and distributions, basic descriptive statistics, frequencies, etc.)

This course will provide you with knowledge of statistics and methods needed for data analysis that can be applied to business problems and to large datasets.

You will learn how to model real-world applications using statistical methods through a mix of theory, exercises, and case studies. By leveraging python libraries for data analysis and visualisation, you will gain exposure to tools used in data analysis and visualisation that constitute the backbone of the statistical analysis you may need in developing your future projects.

TOPICS COVERED:

• Data types and models for analysis
• Data visualisation and hypothesis testing
• Regression analysis
• Models for discrete variables
• Cluster Analysis

Textual data has grown dramatically in recent years, including news articles, scientific literature, emails, corporate documents, and social media such as blog posts, forum posts, product reviews, and tweets. People need tools to analyse and manage large amounts of textual data effectively and efficiently. Textual data is often directly generated by humans and accompanied by semantically rich content. Current natural language processing techniques have not yet reached the point where computers can precisely understand natural language text, but over the past few decades, a wide range of statistical and heuristic methods have been developed to mine and analyse textual data. They are generally very robust and can be used to analyse and manage textual data in any natural language and on any topic.

This course will introduce learners to the basics of text mining and text manipulation. Includes applications for mining word associations, mining and analyzing topics in text, clustering and classifying textual data, opinion mining and sentiment analysis, topic mining & analysis and application for text mining in business. You will learn the most useful basic concepts, principles and techniques in text mining and analysis, which can be used to build a wide range of text mining and analysis applications.

TOPICS COVERED:

• Data preparation for Text Mining
• Word association mining & analysis
• Opinion mining & sentiment analysis
• Topic mining & analysis
• Application of Text mining in business

Networks represent the pattern of complex interactions and interdependence among entities and have a wide range of applications including business. To illustrate, the diffusion of goods and services does not only depend on consumers’ own attributes, but also on how consumers interact with and influence each other. As another example, one may consider how financial or input-output relations among organisations can lead to the spread and propagation of shocks in the economy.

Therefore, the aim of this module is to help students understand how networks can inform business decisions and gain basic skills to analyse and interpret network data. To this aim, the module brings together statistical, mathematical, and conceptual foundations of networks, business problems, and applied data analysis.

TOPICS COVERED:

• Introduction to Networks and Business Applications of Networks
• Network Representations
• Micro and Macro Network Measures
• Sub-network
• Diffusion in Networks
• Time Aware Network analysis