Big data means big career opportunities

Program Overview

The online Data Analytics Certificate Program will teach you how to solve a broad range of problems using a variety of predictive analytics and statistical techniques. The program focuses on examining the vast amounts of data now generated by organizations and their customers and employees during online transactions, in order to develop informed strategies. It is designed to enhance the ability of professionals from any field to utilize data analytics and big data when making decisions. You will learn to apply statistical machine learning and other techniques to data sets, and you will discover how to collaborate with information technology and other specialists in collecting, maintaining and analyzing data to better inform decisions.

The program provides a 100% project-based, learn-by-doing experience, and while the program is online, our program participants are not alone. As a cohort-based online program, our participants often work on their projects in small groups, and all participants are matched to an expert data science mentor, who provides help, advice and feedback throughout the course of the program.

Are you still trying to figure out if the Data Analytics Certificate Program is right for you? 

Employment Outlook

Salary Outlook

  • Thailand Data Analyst on average THB 305,000 as much as THB 552,000. Payscale    expected ROI: >300%
  • Singapore Data Analyst on avaerage S$ 55,000 as much as S$ 88,000. Glassdoor     expected ROI: >900%
  • And best news of all, graduates of the program have the opportunity of getting hired as mentors as well!

Unique Program Approach

Story-centered curricula: Proven highly effective in skill-building programs, developed with industry pioneers.

  • Student work is framed in a realistic story of professional practice and the work situation to which they aspire.
  • Students receive feedback on projects from mentors.
  • Students reflect on lessons learned from their experiences, then move to the next “scene” in the story, which links to and builds on what they have done.

Faculty mentors enhance the online experience

  • One-to-one coaching nurtures excellence.

Strong emphasis on job skills beyond the technical

  • Principled decision making, teamwork, verbal and written communication, negotiation and self-directed learning.

Students build a portfolio of professional-quality work

  • Deliverables roll up into a portfolio that can be presented to employers.

Learning Outcomes

  • Learn Python, Rapid Miner, R, R Studio, SQL, Hadoop, Machine Learning, Numpy, Pandas, Elastic Map Reduce, Amazon Web Services and more
  • Translate business objectives into data mining opportunities
  • Install, run and apply statistical machine learning tools to different kinds of data
  • Apply data mining in e-commerce becoming broadly competent in the use of the statistical machine learning techniques of classification and regression
  • Acquire, process and analyze extremely large data sets using cloud-based data mining methods to do data exploration, discover patterns and answer business questions
  • Develop the confidence to succeed as a professional analyst by building a Professional Portfolio that includes end-to-end project experience on practical data analytics projects

Program Duration: 

Part-time: 40 weeks (15h/week)

 Full-time: 20 weeks (30h/week)

Prerequisites:

Applicants are required to have:

  • One year of work experience (no need to be technical).
  • Resonable level of computer literacy.
  • Familiarity with Windows, Mac or Linux operating systems, specifically:
    • Creating and managing folders within folders.
    • Creating and extracting files from zip archives.
    • Elementary administrative tasks (e.g., installing software requiring admin privileges).
  • Basic familiarity with Microsoft Office or equivalent.

Previous knowledge of statistics is helpful, but not required.

Additional Information:

This course is offered in collaboration with XTOL.

Recommended Pre-work

  • Take a tour of RapidMiner
  • Read Section 1 in Data Mining for the Masses
  • Work through designated exercises in the RapidMiner Learn tutorials