Fundamentals of Data Science (Technical) - an online course

Southampton Data Science Academy

Program Description

Fundamentals of Data Science (Technical) - an online course

Southampton Data Science Academy

Developed to provide the foundations of a career in Data Science, the six-week course equips beginners with technical skills and the theoretical knowledge needed to apply the powerful insights of data science to their work.

Overview

The course, exclusively designed by the world-leading Data Science team at the University of Southampton, provides a hands-on approach to learning data skills. Through a number of interactive, online exercises, you will be able to try out many of the concepts and techniques studied in the taught material.

We use Python to implement technical aspects of the course and we strongly advise all applicants have some experience with Python or similar languages.

Structure

The course is taught over six weeks.

Week 1: the chance to meet your tutor and the other participants, familiarise yourself with the course content for the next six weeks, and find out what support we provide. You will be given ‘hands-on’ experience of Jupyter, the web-based environment used for all your course exercises and assignments. For those of you who are unfamiliar with a programming language, this week also contains a Python Primer activity.

Week 2: time to learn the fundamental terminology and processes in data science. You will be introduced to the technology landscape that has helped fuel the data explosion, as well as the tools used by data scientists to unlock the value hidden amongst vast amounts of data. We also take a closer look at Python and its use in data science.

Week 3: brings a hands-on experience of data science. With a focus on the collection, storage and management of data, you will learn how different sources of data can be combined, in order to increase potential insights.

Week 4: helps you understand how data is analysed. We will cover a range of techniques typically used by a data science team, from machine learning to statistics. Using Python, you will apply these analytical techniques to a real-world dataset.

Week 5: teaches how to use different data visualisation techniques to report the findings from data science work. You will discover various ways to display particular types of data, in order to improve the impact of your reports by highlighting a key finding.

Week 6: looks at the future of data science, with a focus on supporting you in the completion of your assignments.



Prerequisites: You will need a decent understanding of programming, and we would also recommend you have experience with either Python or a similar language.

Target group: Anyone interested in a career in Data Science, or in learning how to apply the transformative techniques of Data Science to their current business challenges.

Learning materials: Video tutorials, online exercises, presentations, further reading.

Assessment and feedback: You will be expected to complete three pieces of coursework. Guidance and formative feedback will be provided by expert tutors throughout the course.

Hands-on experience and assignments: Each week contains a mixture of taught and self-study material, with practical online exercises and activities.

In Week 1 there is an optional, ungraded introduction/refresher on Python, including online exercises for you to work through in your own time.

There are further ungraded Python practice exercises in Week 2. These are designed to help you with your assignments and we encourage everyone to complete them.

Weeks 3, 4 and 5 each feature ungraded online exercises, and a related graded coursework assignment.

Aims and Learning Outcomes

The course provides the knowledge and expertise to become a proficient data scientist.

Upon successful completion, you will be awarded a Certificate of Completion and a graded transcript. You will be able to:

  • Understand the key concepts in data science, including the toolkit used by data scientists, and real-world applications.
  • Explain how data science collects, manages and stores data.
  • Use MongoDB to implement data collection and management scripts.
  • Demonstrate an understanding of the machine learning concepts and statistics vital for data science.
  • Produce Python code to statistically analyse datasets.
  • Use data visualisations to communicate stories from data, and critically evaluate their design.
  • Use Python and Bokeh to plan and generate visualisations from data.

Technology Stack

  • Visualising: Bokeh (Python)
  • Management/Querying: MongoDB (using Python)
  • Base: Python
  • Stats/Analysis: NumPy/ScyPy/Pandas

Syllabus

Week 1: Welcome and course information

Topics

  • Welcome and introduction
  • Learning outcomes of the week
  • What data science is and why it's important
  • Course syllabus and learning outcomes
  • Using discussion forums
  • Introduce yourself
  • Help and tutoring support
  • Course assignment details
  • 'Hands-on' Jupyter familiarisation activity
  • Python Primer
  • Glossary of terminology

Week 2: Introduction to core concepts and technologies

Topics

  • Introduction
  • Learning outcomes of the week
  • Data science in a nutshell
  • Terminology
  • The data science process
  • A data science toolkit
  • Types of data
  • Example applications
  • Further reading
  • Summary

Week 3: Data collection and management

Topics

  • Introduction
  • Learning outcomes of the week
  • Sources of data
  • Data collection and APIs
  • Exploring and fixing data
  • Data storage and management
  • Using multiple data sources
  • Further reading
  • Summary

Week 4: Data analysis

Topics

  • Introduction
  • Learning outcomes of the week
  • Terminology and concepts
  • Introduction to statistics
  • Nature of statistics and introduction
  • Central tendencies and distributions
  • Variance
  • Distribution properties and arithmetic
  • Samples/CLT
  • Basic machine learning algorithms
  • Linear regression
  • SVM
  • Naive Bayes
  • Further reading
  • Summary

Week 5: Data Visualisation

Topics

  • Introduction
  • Learning outcomes of the week
  • Types of data visualisation
  • Exploratory
  • Explanatory
  • Data for visualisation
  • Data types
  • Data encodings
  • Retinal variables
  • Mapping variables to encodings
  • Visual encodings
  • Technologies for visualisation
  • Bokeh (Python)
  • Further reading
  • Summary

Week 6: Future of data science

Topics

  • Introduction
  • Learning outcomes for the week
  • The future of data science
This school offers programs in:
  • English


Last updated November 14, 2018
Duration & Price
This course is Online
Start Date
Start date
Mar. 2019
Apr. 2019
Duration
Duration
60 hours
Part time
Price
Price
1,500 GBP
Information
Deadline
Locations
United Kingdom - Cambridge, England
Start date : Mar. 2019
Application deadline Request Info
End date Request Info
Start date : Apr. 2019
Application deadline Request Info
End date Request Info
Start date : May 2019
Application deadline Request Info
End date Request Info
Start date : June 2019
Application deadline Request Info
End date Request Info
Start date : July 2019
Application deadline Request Info
End date Request Info
Start date : Aug. 2019
Application deadline Request Info
End date Request Info
Dates
Mar. 2019
United Kingdom - Cambridge, England
Application deadline Request Info
End date Request Info
Apr. 2019
United Kingdom - Cambridge, England
Application deadline Request Info
End date Request Info
May 2019
United Kingdom - Cambridge, England
Application deadline Request Info
End date Request Info
June 2019
United Kingdom - Cambridge, England
Application deadline Request Info
End date Request Info
July 2019
United Kingdom - Cambridge, England
Application deadline Request Info
End date Request Info
Aug. 2019
United Kingdom - Cambridge, England
Application deadline Request Info
End date Request Info