Data Analytics Immersive

Interested in pursuing an in-demand career as a data analyst? This 12-week full-time course covers SQL, Tableau, PowerBI, Python, and more to get you from a novice to a data expert.

Dig Deeper Into The Curriculum
Data Analytics Immersive

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About this course

With 16K+ hires, we’ve placed more grads in high-growth, high-pay tech careers than any other program in the world.

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500+ Students
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TRAINING THAT MAKES AN IMMEDIATE IMPACT

  • Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions.

 

BE IN DEMAND FOR YOUR SKILLS

  • Hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

 

BUILT FOR TRUE BEGINNERS

  • This is a beginner-friendly programme for those who are looking to transition into a career in data. Show up on day one with no data experience whatsoever, and leave on week 12 ready to start an entry-level data analyst position – with the portfolio to back it up.

 

TAKE THE DIRECT ROUTE TO A DATA CAREER

  • Train to become a data analyst, wherever you are and whatever you do now. Financially accessible and remotely available – designed to get you where you want to be.

 

 

 

What you'll learn

Complete hands-on exercises with access to real-world data sets to reinforce new skills
Create a portfolio project based off a real-world data problem
Explore the the process of gathering and presenting data to tell a story

Check out our elite team of instructors

Avinnaash Suresh

Lead Instructor

Avinnaash Suresh

Lead Instructor

Bharath Kumar P

Lead Instructor

Bharath Kumar P

Lead Instructor

Ng Shu Min

Data Analytics Instructor

Ng Shu Min

Data Analytics Instructor

These experts bring in-depth experience from the field to the classroom each day, providing invaluable insights into succeeding on the job.

GA instructors* are committed to providing personalised feedback and support to help you gain confidence with key concepts and tools.

*GA instructors are subject to their availability

19K+ Premier Hiring Partners From Around the World

Course Outline

Become an effective inspector, critically scrutinizing datasets for veracity and quality before deciding to use them. You’ll understand how to identify reliable data sources, data storytelling, algorithmic bias, and data ethics.

• Identify the possible limitations and quality issues associated with unfamiliar
datasets including missing data or unreliable sources.
• Understand the bias that is being introduced into predictive models through training
data, choice of model, or evaluation metric.
• Explain the ethical and regulatory issues associated with data acquisition methods.
• Apply a checklist to assess whether the data are likely to be accurate and reliable.
• Qualify issues in a data set and diagnose the type of missing data.
• Apply ethical scraping principles to a given scenario.

Jump into the fundamental statistical and mathematical techniques required for data analytics. Understand descriptive statistics, dependent and independent variables, types of missing data, linear regression, and model validation.

• Identify the difference between key descriptive statistics, including the median
and mean.
• Given a scenario, determine whether a simple or complex model should be used.
• Apply the principles of linear regression, including minimizing the sum of squared
residuals, and using RMSE as a performance metric.
• Compute appropriate statistics using a variety of tools, including SQL, Excel,
Tableau, or Python.
• Choose an appropriate model to solve a given problem; justify the choice
of the model used and identify the independent variables.

Develop your SQL skills. You’ll complete this unit with an understanding of the benefits of using specialized tools such as SQL for specific stages of the data analytics workflow, over multi-purpose tools such as Excel.

• Know the difference between relational and non-relational databases, and identify
the advantages and disadvantages of relational and non-relational databases.
• Apply knowledge of Boolean logic when filtering datasets and SQL syntax,
and debug queries that produce error messages.
• Apply algorithmic thinking skills to a series of SQL queries given an analytical question.
• Interpret the results and indicate the limitations of the data given a query.

Explore and analyze datasets using Excel. Learn to write formulas to perform more complex analyses, build visualizations using lookups to efficiently search datasets and pivot tables.

• Apply syntax and commonly used commands for cleaning, transforming,
and analyzing data.
• Justify the choice of analysis, given a scenario, and select the appropriate
calculations to perform for a given task.
• Select the most appropriate visualizations to effectively communicate results.
• Given a scenario, identify the correct order of steps of the analytics workflow
to plan and implement a full data analysis.
• Determine whether an unfamiliar dataset requires extensive cleaning or manipulation.

Analyze and visualize data using the dashboarding and business intelligence tools Tableau and PowerBI. Effective visualization and communication with storytelling will be the heart and soul of this unit.

• Tackle every stage of the data analytics workflow, including handling very
large datasets.
• Identify and apply data visualization tools that can interface with a range of data
sources.
• Determine whether exploratory data analysis is required as a precursor to predictive
modeling tasks, given a scenario.
• Demonstrate the principles of good data dashboard design.

Now is the point to enhance your growing skills in data acquisition, analysis, and visualization using Python programming fundamentals, data acquisition with APIs, exploratory data analysis, and simple linear regression.

• Identify the algorithmic thinking skills that are required to break complex questions
into smaller steps.
• Indicate how Jupyter Notebook is a useful development environment for
data analytics.
• Identify the information that can be gained from Matplotlib, seaborn, or Plotly.
• Determine the significance of certain metrics when performing exploratory data
analyses in Pandas.

Learn the skills you need to work in an organization, as part of a team of data professionals and nontechnical colleagues. The importance of adhering to regulations, data privacy, and security, will also be emphasized.

• Identify landmark legislation around data governance and privacy, including GDPR
and how it applies to roles in an organization.
• Identify the differences between commonly used agile working frameworks,
including scrum and kanban.
• Give a technical presentation in which information is presented concisely for a non-
technical audience.
• Deploy a Python script on a cloud service to demonstrate that continuous monitoring
and analysis is possible.

To round out your education, you will apply rigorous data analysis techniques to solve a problem in two projects: a group project and an individual project. Both will require you to collect, clean, and analyze a data set and create a compelling presentation to share your — or your team’s — insights.

At the end of the course, you will have personalized job support to help you transition into a Data Analyst role. In sessions held throughout the course, you’ll work with dedicated career coaches to help you confidently build a personal brand, apply for jobs, prep for interviews, and tackle technical assessments.

Pricing & Payment Plans

Installments

from as low as

RM -/month

excluding admin fees and 6% SST

*Discounts available for first-time self-paying individuals.

Full Tuition

RM 25,000

excluding admin fees and 6% SST

*Discounts available for first-time self-paying individuals.

Employer Sponsorship

Our courses are HRD Corp-claimable, so you can receive reimbursement of your tuition fees from your employer.

Need a more flexible payment option?

Instalment plans available for self-paying individuals.

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Frequently Asked Questions

Data analyst is one of the most in-demand job roles today. In fact, the World Economic Forum cited it as the second-fastest growing field in the United States. As companies in virtually every industry - from information technology to higher education - harness the power of big data to make decisions, demand for data analysts is skyrocketing.

Yes! Upon passing this course, you will receive a signed certificate of completion. Thousands of GA alumni use their course certificate to demonstrate skills to potential employers — including our 19K+ hiring partners — along with their LinkedIn networks. Our programs are well-regarded by many top employers, who contribute to our curriculum and partner with us to train their own teams.

This is a beginner-friendly programme with no prerequisites, although many students have engaged in self-learning previously or have worked at tech startups or in tech-adjacent roles. Whether you’re new to the field or you’re looking to formalise your practice, our curriculum helps you gain fluency in the data analytic tools & concepts that modern employers demand and put them to work on the path to a new career in the field.

Our Admissions team can discuss your background and learning goals to advise if this coding course is a good fit for you.

DAI students come from all walks of life but share one common mission: They are passionate about launching a career in data analytics. We see career-changers from diverse professional backgrounds, including engineers and recent STEM graduates, mid-career marketing and financial analysts, and business strategists, as well as more those from more far-flung fields like sales and the law.

Here are just some of the benefits Immersive students can expect at GA:

  • Expert instruction in the skills you need to enter the workforce as a junior data analyst.

  • Self-paced pre-work to explore data analytics fundamentals help you hit the ground running on day one of class.

  • Robust coursework, including expert-vetted lesson decks, project toolkits, and more. Refresh and refine your knowledge throughout your professional journey as needed.

  • A professional-grade portfolio of projects taken from concept to completion — each mirroring real problems that data analysts face — that allows you to showcase the breadth of your technical skills to employers.

  • Individual feedback and guidance from instructors and TAs during office hours. Stay motivated and make the most of your experience with the help of GA’s dedicated team.

  • Exclusive access to alumni discounts, networking events, and career workshops.

  • A GA course certificate to showcase your new skill set on LinkedIn.

  • Connections with a professional network of instructors and peers that lasts well beyond the course. The global GA community can help you navigate and succeed in the field.

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