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Data Analytics: Elevate

online

Online

12-weeks-part-time

12 weeks, part-time

Course overview

Data Analytics: Elevate is a practical, mentor-led course designed to equip you with the tools and techniques needed to harness the power of data.  

Specifically built for professionals (like marketers, consultants, etc.) who want to be more data-driven, this course is designed and taught by practising industry experts. 

With companies expected to analyse 150 trillion gigabytes of data by 2025*, learning to source, clean and analyse data to make business decisions is a sure way to add value in your career.  

Packed with industry-approved content:

  • Premium content built in-house with input from industry experts 
  • 80 hours of content and practical activities across 12 weeks
  • Get comfortable with the full process of data analysis, from retrieving raw data sets and running SQL queries, to cleaning, manipulating and visualising data
  • Learn to present data persuasively to secure stakeholder buy-in

The best way to learn is by doing:

  • Pick up a range of popular data analysis tools, including Excel, SQL, Tableau, BigQuery, and Datawrapper (Optional: Python, Google Colab, Plotly.py)
  • Put your new skills to the test with a series of exciting real-world data challenges
  • Beginner friendly, with a ‘low code’ route or the option to develop more advanced coding skills
  • Work on a unique project on a real-world dataset of your choice from Our World in Data
  • Gain practical experience with Data Analytics that you can immediately use in your job

A highly supported experience: 

  • Mentor-led live video sessions to facilitate classroom discussions, unpick projects and bring the content to life
  • Weekly sessions conveniently held after working hours
  • Two 1:1 on-demand sessions with your mentor for tailored support
  • Stay on track with fixed deadlines while enjoying flexibility in how you pace your weekly activities
  • Entire teams dedicated to your support and progression 

A social and collaborative learning environment:

  • Start the course as a group and learn at the same pace as all other students  
  • Collaborate with your classmates, grow together, and make lifelong friends

Earn trusted and recognised credentials:

  • On completion of this course, you can add an industry trusted digital credential to your Linkedin profile
*Forbes, 2021

Course timeframe

This course takes approximately 80 hours over 12 weeks. This timeframe includes getting through the learning materials, attending live video sessions, completing the course activities, developing your projects, and communicating with your peers and mentor.

What you'll learn

Understand the data analytics pipeline, develop a problem-solving mindset and understand the uses of different data analysis tools.  

TOPICS

  • Understand the value of data analytics
  • Recognise types of problems answerable with data
  • Become familiar with the processes of the data analytics pipeline 
  • Understand the best mindset with which to approach data analytics problems
  • Gain awareness of key data analytics tools and their use cases
    • Excel, SQL/Google BigQuery, Datawrapper, Tableau
    • Python/Google Colab (optional)

LEARNING ACTIVITIES

  • Reproduce the data analytics pipeline and create a report for a smart toilet start-up

Understand how to define problems, scope data projects and explore Excel and Python.  

TOPICS

  • Gain awareness of online data sources & search engines
  • Understand why defining the problem is a key step in data analytics 
    • Articulate good problem definitions
    • Identify good v bad problem definitions
    • Translate a business problem definition to a data analysis problem definition
  • Introduction to Excel and Python/Python libraries
  • Statistics: Sample vs population, sample size, statistical significance

LEARNING ACTIVITIES

  • Produce a data project ‘scope of work’ for an online wine store

Explore different ways to source data, learn the basics of SQL and understand data ethics and privacy.  

TOPICS

  • Discover various ways to source data
    • Consider build/collect decisions
    • Learn simple SQL syntax and basics of databases
  • Identify potential issues re: data ownership
    • Copyright, licensing issues and scraping legality
  • Statistics: basic probabilities

LEARNING ACTIVITIES

  • Determine the best major markets and developing countries to launch a new surgical device in 

Get to grips with manipulation techniques by filtering, sorting and converting different types of data.

TOPICS

  • Understand different data file formats and how to open them (CSV, TSV, JSON, XML)
  • Learn how to filter/sort data
    • Conditional formatting
  • Understand different data types, their uses and how to convert between them
    • Strings, numbers (floats/integers) and dates
    • Qualitative vs quantitative, continuous & discrete, categorical and boolean
  • Statistics: distributions, boolean

LEARNING ACTIVITIES

  • Manipulate a given dataset to answer questions for a women’s clothing boutique launching a range of shoes

Understand a range of chart types and how to apply them to different statistics for exploratory analysis. 

TOPICS

  • Recognise common chart types, and know when to use each
    • Bar & column (stacked & grouped)
    • Histogram
    • Line
    • Scatter & bubble
    • Box plots
    • Pie & donut (and why they’re bad)
  • Understand when to plot relative vs absolute values
  • Basics of colour usage in charts
    • Qualitative scales and quantitative scales
  • Statistics: Mean, median, standard deviation/variance, outlier & correlation

LEARNING ACTIVITIES

  • Help an Australian craft beer company to understand the US craft beer market by creating exploratory charts

Learn how to clean data, validate it and produce summary stats and charts.  

TOPICS

  • Clean data
    • Plot data to identify potential problems
    • Deal with missing data, outliers and erroneous data
  • When to use SQL vs local data manipulation.
  • How to perform data validation
  • Statistics: Extrapolation/interpolation

LEARNING ACTIVITIES

  • Clean a provided dataset
  • Produce summary stats and charts with cleansed data 

Perform exploratory data analysis using stats and data visuals and learn how to combine datasets using joins.

TOPICS  

  • Learn exploratory data analysis methods.
    • Using stats
    • Visualisations
  • Combine data tables.
    • Excel: Vlookup / Hlookup
    • Python: Dataframe joins
    • SQL: Joins
    • Concatenating tables
  • Statistics: Bayesian statistics

LEARNING ACTIVITIES

  • Combine multiple tables with joins
  • Characterise the combined dataset with EDA techniques

Learn how to group, reshape and tidy data for analysis, then use validation and verification techniques to test conclusions.   

TOPICS

  • Grouping data
  • Reshaping data
    • Tidy vs messy data 
  • Understanding data conclusions
  • Validation and verification
  • Statistics: Regression analysis, statistical significance and p-value, correlation v causation

LEARNING ACTIVITIES

  • Analyse given dataset

Learn how to present data visually using different charts, colours, values and interactive elements.  

TOPICS

  • How to build charts for presentation (vs analysis)
    • Effective use of colours
    • Whether to use colours at all
  • Learn when to use interactive charts
    • Allowing users’ data discovery vs directing users concretely
  • When to map data

LEARNING ACTIVITIES

  • Work on your final project

Use Tableau to create dashboards and learn how to present actionable data reports to secure stakeholder buy-in. 

TOPICS

  • Dashboard basics
    • Familiarise with Tableau
  • Understand use cases for dashboards vs reports
  • How to present actionable conclusions
    • Tailoring the presentation to suit the audience

LEARNING ACTIVITIES

  • Work on your final project

Weeks 11 and 12 are all about completing your final project and presentation.  

  • Select a dataset and define your challenge
  • Perform exploratory analysis and prepare a visual presentation of your analysis 
  • Present project to cohort and mentor for review

Who this course is for

If you’re keen to explore the real-world uses of data and develop the skills to implement your own data-driven initiatives, our Data Analytics: Elevate course is ideal for you. This course has no prerequisites and is perfect for:

  • Working professionals who want to gain a competitive edge with key data analysis skills
  • Marketers, product managers, business analysts, sales and finance professionals who want to improve their decision-making and performance by learning data essentials
  • Anyone who wants to gather, analyse and present data to drive decisions without relying on their data team
  • Those who work with data teams and want to better understand the data analysis process

Earn a digital credential

We partner with Credly to deliver digital credentials for our graduates. Digital credentials are a graphical representation of your skills, combined with a description of the knowledge and activities it took to earn them. 

Digital badges can be used in email signatures or digital resumes, and on social media sites such as LinkedIn, Facebook and Twitter. 

For more info, click here.

Our students' success stories

Academy Xi scored 5 out of 5, as rated by our students.

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