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Data Analytics Online Course | Part-Time Course

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Online

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10 weeks, part-time

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$4,000

Course overview

Complement your existing professional expertise and boost your career with in-demand skills in Data Analytics and Data Science. No coding experience required!

The demand for Data Analytics talent is exponential, as this skillset is critical to improve business performance. With an average salary of AUD$114,600 and over 5,600 roles on offer, learning Data Analytics and Data Science is a sure way to future-proof your career.

Packed with industry-relevant content:

  • Receive an industry-recognised Certificate and digital credential
  • 50 hours of content and activities along 10 weeks
  • Premium content built in-house by experts 
  • Learn effective data communication techniques to secure stakeholders’ buy in

A highly supported experience: 

  • Live video sessions: your instructor takes you through the course material 
  • Sessions conveniently held twice a week after working hours
  • No need to go through a learning platform
  • Entire teams dedicated to your support 

The best way to learn is by doing:

  • Gain hands-on experience with Excel/ Google Sheets, SQL (SQLite, MySQL), Tableau, PowerBI, R and Python
  • Get practical experience that you can immediately use in your job 
  • Work on various activities throughout the course
  • Build your portfolio on a project of your choice, using predictive analytics

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 on projects, grow together, and make lifelong friends

Course timeframe

This course takes approximately 50 hours over 10 weeks. This timeframe includes getting through the learning materials, attending live video sessions, completing the course activities, developing your personal project, and communicating with your peers and instructor.

What you'll learn

  • Definitions and concepts, latest trends and hot topics
  • Discuss use cases and learn about the roles, marketplace and tools
  • Learn and discuss real world applications and pillars of successful capability
  • Latest aspects of data privacy and ethics
  • Introduce fictitious datasets
  • Work on simple hands-on example workflow
  • Types/structure of data
  • Ingesting and manipulating small datasets
  • Basics of spreadsheet functions and formulas
  • Data quality issues and potential solutions
  • Referencing and aggregating datasets
  • Basics of Pivot Tables
  • Advancing in Pivot Tables
  • Advancing in spreadsheet functions and formulas
  • Visualising numerical data in spreadsheets
  • Exploring nuances of different data types and variables
  • Intro to basic summary statistics and the power of sampling
  • Spurious correlation, and issues with cherry picking and bias
  • Discuss and introduce concepts in data infrastructure and architecture
  • Learn about databases and their role in supporting analytics
  • Database architecture flavours, tech providers and solutions available
  • Understand how relational databases work and best practices in designing them
  • Introduce the concepts of database schemas and SQL
  • Learn how to retrieve data from databases using SQL syntax
  • Contextualise and define SQL use cases in analytics
  • Advancing in SQL syntax, statements and operations
  • Explore and learn how to JOIN multiple tables in SQL
  • Hands-on experience setting up a cloud environment for database
  • Tackle analytics problems with large scale data using SQL
  • Guided exercise requiring participant’s work and solutions
  • Contextualise data visualisation in analytics
  • Overview and basic principles of visual representation of data
  • Analytics, Business Intelligence and other tools available for visual storytelling
  • Effective data communication
  • Intro to theory of graphs
  • Intro to theory of colours
  • Overview and applications of grammar of graphics
  • Learn and work through data storytelling best-practices and guidelines
  • Discuss and showcase examples of good data visualisations
  • Discuss and showcase examples of ‘not so good’ data visualisations
  • Advanced data visualisation in the context of predictive analytics
  • Hands-on exercises in Tableau and PowerBI
  • Define concepts and introduce machine learning
  • Available tools and techniques (R / Python)
  • Revisit some examples of popular applications
  • Discuss in depth different steps in predictive modelling
  • Topics in estimating function
  • Issues in prediction accuracy vs model interpretability
  • Basic hands-on experience with coding in R and Python
  • Intro to Supervised vs Unsupervised learning
  • Develop a linear regression model
  • Develop a binary classifier
  • Conclude with cutting edge techniques and other approaches
  • Final discussion on future learning opportunities and advanced topics
  • Revisit main topics discussed
  • Explore and deep dive into specifics as required
  • Tips in setting-up a professional profile
  • Ideas in taking your learning forward
  • Tips in starting an analytics portfolio
  • Industry learnings and collaboration

Who this course is for

There are no prerequisites for this course. This course is ideal for:

  • Anyone looking to enrich their career in a mentor-led course, with convenient live video sessions, held after working hours
  • Anyone looking for a practical course where they can develop their hands-on and theoretical expertise, in line with industry-standards

Earn a digital credential

We partner with Acclaim by 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 work with the best

Our students' success stories

Academy Xi has a 4.2 score out of 5, rated by 2386 students.

Meet your instructors

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