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Intro


Introduction to Data Science with Python

Introduction to Data Science with Python
399.00
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What you'll learn


What you'll learn


What you'll learn

Gain a foundation in data science methods, ethics and tools before moving on to learning how to code in Python.

This course is taught in two parts and incorporates our introductory course, Introduction to Data Science for Social Scientists.  

Part one:  Introduction to Data Science for Social Scientists will teach you the theory of computational social science and provide a foundation in data science before you start to develop your programming skills. (Please note, you can also take this as a standalone course).

Part two: The second part of the course will see you specialize in the Python programming language. You’ll master the fundamentals of Python and learn practical skills that are directly applicable to social science research.  

This course is ideal for anyone who wants to develop a solid foundation in data science before going on to learn the basics of Python. The two stage approach provides a comprehensive overview,  teaching you theory and practical skills that can be applied to your research straight away.

If you would prefer to specialize in R we also offer Introduction to Data Science with R

English
25 hours to learn
3 months access

Instructor
[[Instructor]]

Price
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Course modules


Course modules


PART ONE - Introduction to Data Science for Social Scientists

This part of the course includes:

  • Introduction and overview to data science
  • Ethics in data science
  • Surveys and crowdsourcing data
  • Data science tools

For further information about part one of the course, you can read the full syllabus on the Introduction to Data Science for Social Scientists course page.

PART TWO - Learning Python

  • Introduction & Overview of Python
  • Jupyter Notebooks
  • Variable Assignment
  • Data Types & Coercion
  • Strings
  • Built-Ins
  • Lists
  • Loops
  • Conditionals
  • Functions
  • Functions and Variable Scope
  • Dictionaries
  • Files
  • Libraries
  • Errors
  • List Comprehensions
  • Programming Style

Testimonials


Testimonials


 
 

Prerequisites


Prerequisites


Prerequisites

 

None, this course is suitable for all. A basic foundation in statistics would be helpful, but it is not essential.

FAQs


FAQs


Do I need to install any software?

No. All you need to complete this course is a web browser and an internet connection. We do all our programming using JupyterHub, which means that you can code in your browser window.

Jupyter notebooks offer a seamless integration of code with explanatory markdown text. It will allow you to read the narrative of the programming task, and write code of your own to fit into the larger narrative.

If you’d like to find out more about Jupyter notebooks, see our blog post “What is a Jupyter Notebook”.

How long will I have access to the course for?

You will have access to the course for 3 months.

What support will I receive and when?

For the first 4 weeks of the course you will be able to reach your instructor on the class forum if you get stuck. We recommend that you complete part one of the course during this time.

During weeks 4-8 of your course, live Q&A video sessions will be held weekly with your instructor to ensure that you have support when you move onto part two and start learning how to code. They will be recorded and posted on the learning platform so you can watch the videos later if you can’t attend.

Will I get a certificate?

All of our courses offer a certificate of completion signed by your instructor. You will be able to download this certificate, from the Learning Platform, when you complete the course.

How long is the course?

Part one is approximately 10 hours of learning. Part two is approximately 15 hours of learning. We recommend one hour per day on four days a week for four consecutive weeks. Research shows that regular short practice is more effective than inconsistent long sessions of study.

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