Data Analytics
Learn the most in-demand skill of our time, and complete a real-world project.
“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades.”
Hal Varian, Chief Economist, Google
“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades.”
Hal Varian, Chief Economist, Google
In this course you will learn the key aspects to data analysis. You will begin to explore the fundamentals of gathering data, and learning how to identify your data sources. You will then learn how to clean, analyze, and share your data to help make informed decisions on operations, risk management, finance, marketing, etc.
Taught by industry experts.
Our courses are taught by experts who have both an academic and industry background. That means what you'll learn will be relevant, practical, and up-to-date.
A real-world project to add to your resume.
All Flex courses teach you to build a real-world project that you can show potential employers as evidence of your new skill set.
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chapter 1.
Intro to Data Analysis & Visualization with Python
Introduction
What we are going to learn and why learn data science
Why use python?
Setting up python, Anaconda and Jupyter notebooks
Intro to Jupyter notebook
Intro to Python Notebook + variables
Strings
Numbers
Boolean
Data Structures
Wrapping up and finding help
Homework
chapter 2.
Problem formulation & obtaining data
Introduction
Framing the problem
Data sources
Data Quality and Ethics
Reading data in
Intro to Pandas and dataframes
Reading and Writing dataframes
Inspecting data frames
Indexing
Golden thread project
Homework
chapter 3.
Data Preparation
Introduction
Lightning quick intro to data preparation
Selecting columns
Renaming columns
Filtering rows
Dropping columns and rows
Mutating columns
Summarising columns
Homework
chapter 4.
chapter 1.
Intro to Data Analysis & Visualization with Python
Introduction
What we are going to learn and why learn data science
Why use python?
Setting up python, Anaconda and Jupyter notebooks
Intro to Jupyter notebook
Intro to Python Notebook + variables
Strings
Numbers
Boolean
Data Structures
Wrapping up and finding help
Homework
chapter 2.
Problem formulation & obtaining data
Introduction
Framing the problem
Data sources
Data Quality and Ethics
Reading data in
Intro to Pandas and dataframes
Reading and Writing dataframes
Inspecting data frames
Indexing
Golden thread project
Homework
chapter 3.
Data Preparation
Introduction
Lightning quick intro to data preparation
Selecting columns
Renaming columns
Filtering rows
Dropping columns and rows
Mutating columns
Summarising columns
Homework