Cartes-fiches

•  Almost every aspect of a business is open to data collection, be it web server logs, tweet streams, IoT sensors, online transactions, or some other source. Data science provides techniques to extract useful information from this data and thus generate added value
• The question facing every company, startup, and non-profit that wants to attract a community in the modern era is how to use data effectively. Using data effectively requires something different from traditional statistics; namely an interdisciplinary approach that involves elements of statistics, computer science, and domain expertise. Data science is the methodology that synthesises these three domains.
• In essence, data science is about the extraction of useful information and knowledge from large volumes of data, in order to improve business decision-making (Provost & Fawcett, 2013). With improved decision making comes improved productivity, market value, and competitive edge. Thus the main goal of data science is to enable data-driven decision making across the whole company, where decisions are based on the analysis of data rather than pure intuition.

This module provides students with a hands-on introduction to the methods of data science, with an emphasis on applying these methods to solve business problems. By the end of this course, it is expected that students will:

• Know how to approach business problems from a data science perspective;
• Understand the fundamental principles behind extracting useful knowledge from data;
• Understand the core concepts and terminology of machine learning;
• Gain hands-on experience with mining data for insights.

Throughout the course, students will also have the opportunity to learn several technical skills:

• Python programming and experience with the core libaries for data analysis, visualisation, and modelling.
• Working with data: collecting, cleaning, and transforming.
• Creating and interpreting descriptive statistics.
• Creating and interpreting data visualisations.
• Practical experience with machine learning.

By the end of this course you will:

• Know how to approach business problems from a data science perspective

• Understand the fundamental principles behind extracting useful knowledge from data

• Gain hands-on experience with mining data for insights

• Python programming and core libraries for data analysis, visualisation, and modelling

• Working with data: collecting, cleaning, transforming

• Creating and interpreting descriptive statistics

• Creating and interpreting data visualisations

• Creating statistical models for inference

• Practical machine learning

Data science is about the extraction of useful information and knowledge from large volumes of data, in order to improve business decision-making

Is an interdisciplinary subject with 3 key areas:

• Statistics

• Computer science 

•  Domain expertise

In the past, data analysis was typically slow: needed teams of statisticians, analysts etc to explore data manually

Today: volume, velocity, and variety make manual analysis impossible …

… but fast computers and good algorithms allow much deeper analyses than before )

--> data-driven decision making

-->  base decisions on analysis of data, not intuition

• Iterative process • Non-sequential • Early termination • Established processes, e.g. CRISP-DM (https://bit.ly/1tX6508) 

Raw data, little value --> Data exploration --> Model building and analysis --> Reporting, Automation