Data Analytics: Transform




15 weeks, part-time


Career support

Course overview

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Go from beginner to job-ready data analyst with Academy Xi’s Data Analytics Transform course.

The demand for Data Analytics talent is growing exponentially. With an average salary of AUD$90,000 and over 6300* roles on offer, learning Data Analytics and Data Science is a sure way to future-proof your career.

Get ready to make sense of data and unleash its true potential.

*source:, as at Nov 2021

97% job placement success

This report includes data for 136 students who actively participated in our Career Support Program this financial year. 132 of the 136 active participants successfully landed roles, giving the program a success rate of 97%.

Why study this course?

Career Support Program:

  • Enter the course a beginner and be job-ready by graduation
  • 24 weeks of personalised career support and guidance
  • Expand your connections in the data world and join an active community

Packed with comprehensive, industry-relevant content:

  • Premium curriculum built by experts from the world-renowned Flatiron School
  • 225+ hours of learning and activities along 15 weeks
  • Learn Python, SQL, Regression Data Modelling, APIs and Web Scraping, and Statistical Data Analysis
  • Visualise and present data persuasively to secure stakeholders’ buy in

Get the practical experience employers are looking for:

  • Spend 50% of your time coding, with three course projects and weekly labs
  • Build an active GitHub profile as you work through the course, to showcase your analytics skills to employers
  • Develop and present your projects using Jupyter Notebooks, a powerful collaborative tool used by Data Scientists
  • Build your personal brand and prove you can talk tech by writing a technical blog
  • Showcase your final project to a panel of industry experts and gain invaluable feedback, plus the opportunity to network
  • Get hands-on experience that you can immediately use in your job or a new role

A highly supported experience: 

  • 2 weekly live video sessions with your Mentor and cohort to cement learning and ask questions
  • Personalised, unlimited 1:1 support from your Mentor
  • Entire teams dedicated to your support and progression 

A social and collaborative learning environment:

  • Powered by Canvas, the world’s largest learning management system
  • Start the course as a group and learn at the same pace as other students  
  • Collaborate with your classmates on projects, grow together, expand your network 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

What you'll learn

Python Fundamentals & Descriptive Statistics

Python is a powerful part of the data scientist’s toolbox as it can handle massive amounts of data, automate reporting, and connect to databases seamlessly. Learn and use basic Python programming to manipulate and clean data, and create impactful visualisations.

  • Variables and data types
    • Strings
    • Numbers
    • Booleans
    • Lists
    • Dictionaries
  • Conditionals and control flow
  • Using built-in operators and functions
  • Loops
    • For loops
    • While loops
    • Nested loops
    • Looping over collections
  • Writing functions
    • With and without arguments
  • NumPy library
  • Measures of central tendency (mean, median, mode, quantiles)
  • Measures of dispersion (standard deviation, variance)
  • Covariance and correlation
  • Creating visualisations using Matplotlib
  • Customising visualisations
  • Set up a local environment to run Jupyter notebooks
  • Clone, create, and push to GitHub repositories
  • File input and output in Python
  • Read and extract data from JSON files
  • Read and extract data from CSV files

Loading, Cleaning, and Visualising Data for Exploration

Learn how to collect data from the web using web scraping and APIs. Load and clean data using Pandas, a popular python library. Use SQL, the top language for data work to organise data and perform operations on it. 

  • Load, access, and describe data using pandas
  • Use descriptive statistics to perform Exploratory Data Analysis
  • Clean, transform, and aggregate data using pandas
  • Understand the basic HTTP Request/Response Cycle
  • Understand and use Auth to call 3rd party APIs
  • Read, store, and parse JSON responses
  • Understand and use best practices for working with 3rd party APIs safely
  • Explain what HTML is and why it’s important in the context of web development
  • Describe the DOM and its relationship to HTML
  • Navigate HTML documents using Beautiful Soup
  • Identify and scrape images from a webpage
  • Perform basic SQL queries on a single table, including the use of WHERE and ORDER BY
  • Demonstrate understanding of the parts of a database (row, column, table, relationship, etc)
  • Perform basic CRUD operations in SQL
  • Perform joins
  • Use aggregate functions in SQL with GROUP BY
  • Perform advanced queries in SQL including nested queries

Work in groups to analyse the pizza market of NYC and make business recommendations.

Inferential Statistics and A/B Testing

Use inferential statistics to make inferences about the population based on sample data, by testing hypotheses and deriving estimates.

  • Understand mathematical set notation, and connect set logic to probability concepts
  • Clean, transform, and aggregate data using pandas
  • Understand sampling error and the Central Limit Theorem
  • Construct confidence intervals
  • Set up experiments
  • Perform hypothesis tests (z-tests and t-tests)
  • Interpret the results of statistical tests, including p-values and Type I and Type II errors
  • Understanding what statistical significance means
  • Use ANOVA test to find out whether the differences between groups of data are statistically significant
  • Conduct an A/B test to compare the two versions of a variable to find out which performs better in a controlled environment

 Regression Modelling

Regression modelling is all about making predictions about the future, based on past data and behaviour. Learn how to exploit patterns found in historical and transactional data to identify risks and opportunities.

  • Perform linear regression on a dataset with one predictor using statsmodels
  • Interpret regression output
  • Identify and test the assumptions of simple linear regression models
  • Encode categorical variables
  • Address multicollinearity
  • Scaling and normalization
  • Perform multiple regression using high dimensional datasets in statsmodels and scikit-learn
  • Interpret regression output
  • Validate model using holdout data
  • Understand sampling error and the Central Limit Theorem
  • Construct confidence intervals
  • Set up experiments
  • Perform hypothesis tests (z-tests and t-tests)
  • Interpret the results of statistical tests, including p-values and Type I and Type II errors

Work in pairs to source your own data, using APIs and/or externally sourced CSVs. Once you’ve collected your data set, you’ll run hypothesis tests, AB tests, and/or regression analysis on it. 


Note: A default dataset can be provided upon request but is not recommended

Go solo on your final capstone project, which brings together everything you have learned in the course. You’ll source data, prepare it for modelling and perform a regression analysis. 

Present your completed project to your course Mentor, peers and a panel of Industry Experts, who will review your work and provide feedback. Use your industry showcase to build your network in the data community. 

Career support program

After you graduate, it’s all about landing you that dream Data Analyst role! At this point of your learning journey, you’ll have the skills and practical experience – all that’s left is to craft your career narrative. 

With the Career Support Program, you’ll receive comprehensive career guidance and support. You’ll undertake activities including but not limited to the following:

  • Finesse: Add polish to your Portfolio, CV and LinkedIn creating a coherent career narrative
  • Practice: Hone your soft skills, such as interview and presentation techniques
  • Search: Build your network within the industry and apply for your dream roles
*Note: This 24-week career support program is optional, but highly recommended.

Career outcomes

Graduates of this course and our Career Support Program can expect to land junior to mid-level analytics roles, including but not limited to:

  • Data Analyst
  • Data Administrator
  • Data Engineer
  • Business Analyst

Graduate success

We know that signing up for a rigorous course online can be daunting, but trust us, you’re in safe hands!

Who this course is for

This course is suitable for anyone looking to break into the data/tech industry and has no prerequisites

If you are detail-oriented, analytical and have an interest in transforming data to insights, this is the career change for you.  Those interested in this course may include: 

  • Anyone who wants to work with data daily in their next or current role
  • Career changers who are currently working in an unrelated field and want to future proof their careers with in-demand skills
  • People re-entering the workforce after a break and looking to retrain
  • Junior analysts or others working with large amounts of data in spreadsheets, who want to level up and learn powerful tools that make the job easier and secure you a promotion

Course timeframe

You’ll dedicate 15-20 hours to study per week, over 15 weeks. 

This time frame includes getting through the learning materials, attending live video sessions, completing the course activities, developing your projects, and communicating with your peers and instructor.

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

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