Python for Data Science

DescriptionQuantityAmountTotalTax
Course Fee1R 6,300.00R 6,300.00R 0.00 (0%)
Total amountR 6,300.00
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Overview

In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?

This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyse data and to conduct data science.

Specifically, you’ll learn how to use:
•    python
•    jupyter notebooks
•    pandas
•    numpy
•    matplotlib
•    git
•    and many other tools.

You will learn these tools all within the context of solving compelling data science problems.

After completing this course, you’ll be able to find answers within large datasets by using python tools to import data, explore it, analyse it, learn from it, visualise it, and ultimately generate easily sharable reports.

By learning these skills, you’ll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.

Prerequisites
Previous experience with any programming language (Java, C, Pascal, Fortran, C++, Python, PHP, etc.) is expected. This includes a high school, or undergraduate equivalent, to an introduction to computer science course. Learners should be comfortable with loops, if/else, and variables.

  • Start date
    9 April 2019
  • Programme duration
    10 weeks
  • Estimated effort
    8 to 10 hours per week
  • Fee
    R6300
  • Institution
    MITx
  • Language
    English
Start Dates

What you will learn:

  • Basic process of data science
  • Python and Jupyter notebooks
  • An applied understanding of how to manipulate and analyse uncurated datasets
  • Basic statistical analysis and machine learning methods
  • How to effectively visualise results