Data Analysis Experience

With Tristan Frizza, Oscar Lukersmith, Nabeel Siddiqui

Data is an integral part of every successful business, as companies increasingly make data-driven decisions. According to IBM, there were more than 2.7 million data jobs in 2020.

In the Data Analyst Accelerator, you will learn to use data analysis tools, make data-driven decisions to improve business performance, and learn to create data visualizations. No prerequisites or previous experience required to sign up!

Experience Designed by

Oscar Lukersmith

Senior Analytics Engineer at Safety Culture

Nabeel Siddiqui

Senior Product Analyst at Wise (Previously Transferwise)

Tristan Frizza

Ex-Data Scientist at Atlassian

We have a few spots left, save yours now!

Curriculum

1. Introduction to Data Analysis

1.1 What is a Data Analyst

Lesson: What is a Data Analyst, how do they use data, and what does a day in the life of a Data Analyst look like?
Task: Reflection on current skills and skills to develop to be a Data Analyst.

1.2 Tools of the Trade

Lesson: What tools to Data Analysts use? How do each of these help Data Analysts?
Task: 1) Quiz on which tool is most useful for each task. 2) Get excel and tableau

2. Framing the problem and communicating the plan

2.1 Framing the problem

Lesson: How to ask questions - Framing the problem, and using the 5 Whys approach to Root Cause Analysis.
Task: Case study given to students with a business problem. Frame the problem and begin with the 5 whys approach

2.2 Choosing the right data

Lesson: Determining clients needs and the right data for the right problem.
Task: More information on the business given. Determine what data should be collected and how it should be presented.

2.3 Communicating the plan of action

Lesson: Creating and communicating a plan of action
Task: Write a project brief outlining the next steps for your client.

3. Collecting and Cleaning Data

3.1 Data Sources

Lesson: Data comes from multiple sources. How to aggregate data
Task: Gather data from different sources and extract to Excel

3.2 Data Cleaning

Lesson: Importance of cleaning data, removing unnecessary data, and common techniques
Task: Normalise and standardise the data compiled on excel.

4. Visualisations and Reports

4.1 Excel: Pivot Tables and Charts

Lesson: Pivot Tables and Chart usage in Excel, how to create
Task: Create a Pivot Table and Chart for your case study

4.2 Tableau: Visualising Data

Lesson: Using Tableau to create dashboards and visualise data
Task: Connect excel to Tableau, and build a dashboard for your case study.

4.3 Using Data to tell a story

Lesson: Identifying your narrative and creating a story that is easy to follow with your data
Task: Create a report with your visualisations from Excel and Tableau to tell a story with your data.

5. Self-Guided Project

5.1 Pick a dataset

Lesson: Finding a dataset that interests you and framing the problem when it is not clearly defined.
Task: Define the question and need for analysis

5.2 Collecting and cleaning data

Lesson: TBA
Task: Collecting data from sources, cleaning through unnecessary data

5.3 Visualization

Lesson: TBA
Task: Visualise data with the tool

5.4 Reporting

Lesson: How to create a compelling presentation with your findings
Task: Create a final presentation with your findings.