Data Analytics: Elevate

Define the problem
Understand the importance of connecting the high-level business objective (the ‘why’) with the objective for your data analysis. You’ll learn how to research the ‘problem domain’, write a problem statement and set success metrics.

• Define the problem, the problem domain and context 
• Establish which insights are obtainable via data 
• Translate business goals into data analysis goals 
• Create a data analysis SMART goal 
• Set expectations with stakeholders regarding data projects 
• Apply basic Excel skills to a data problem Introduction to Python libraries 
• Create a data analysis project statement of work

Get the data
Learn how to identify data for your analysis goals and the best data sources. You’ll uncover potential issues related to data collection, including data quality and ownership. You’ll also learn how to make queries to retrieve data from databases.

• Data licensing, ethics and privacy 
• Obtain data from internal and external sources 
• Relational databases 
• Apply database skills by using Google BigQuery 
• Write SQL queries 
• Extract data from real databases 
• Combine Python and SQL

Manipulate the data
Explore common data manipulation techniques, such as converting data types, sorting, filtering and managing duplicates. You’ll also learn how to work with various file formats, such as CSV and JSON.

• Understand statistical and computer science data types 
• Identify different data file types such as CSV and JSON 
• Apply common data manipulation techniques, such as converting data types, sorting, filtering and managing duplicates in Excel 
• Apply these same data manipulation techniques in Python

Intro to charts
Learn how to use charts to explore data, discover new insights or form your own hypotheses. You’ll learn about different types of charts, how to build them, and how to use them to discover patterns and insights.

• Introduction to charts and data visualisations 
• Identify charts for exploration and charts for communication 
• Analyse types of charts and when each type is appropriate to use 
• Understand the anatomy of a chart and chart elements 
• Create charts using Excel and Python 
• Use chart-creating tools such as Datawrapper and Tableau

Clean the data
Learn how to ensure your data is the best version of itself it can be. ‘Dirty’ data can lead to poor or misleading analysis results. By the end of the module, you’ll know how to identify dirty data and the techniques needed to clean it.

• Understand what ‘dirty’ data is Identify dirty data using Excel, with techniques such as filtering and summary statistics 
• Clean dirty data using Excel and Python

Analyse the data 1
This module emphasises exploratory data analysis (EDA), which is a great approach for identifying patterns in data. You’ll learn how to combine data from multiple tables, use pivot tables to aggregate data, and answer specific questions using a combination of Excel and SQL.

• Understand the concept of EDA and the EDA mindset 
• Visualise distributions, relationships and categorical variables 
• Group and aggregate data using pivot tables and binning data 
• Combine multiple data tables in Excel and SQL 
• Apply EDA knowledge to the Python environment (optional)

Analyse the data 2
Get introduced to the practice of data modelling. You’ll learn why models exist, how they can add value to data analysis tasks, and how to identify different types of models. You’ll even take your first steps into modelling by building a few models of your own.

• Data modelling and regression basics 
• Analyse trendlines and R-squared values 
• Create a simple linear regression model in 
• Excel Create a simple multiple regression and non-linear model in Excel (optional) Analyse regression fit and errors 
• Managing the performance/complexity tradeoff 
• Introduction to machine learning

Communicate the data (data visualisation)
Learn how to effectively communicate your findings. This brings the data analysis process full circle, as the outputs are tied back to the problem definition and used to effect change.

• Introduction to data storytelling and data dashboards 
• Analyse data storytelling techniques 
• Create data stories using Tableau and/or Datawrapper 
• Apply techniques to communicate geographical data

Communicate the data (effecting change)
Focus on dashboard design and construction, covering use cases and the guiding principles for different dashboard builds. You’ll also review a number of sample dashboards and understand the suitability of various dashboarding tools.

• Analyse what makes an effective dashboard 
• Explore different dashboard tools 
• Create a simple dashboard in Excel or Tableau (optional) 
• Examine best practice presentation skills, including understanding the audience and storytelling 
• Create a presentation