Course Duration:
  30 Hours
Mode of Study:
  Upon successful completion of this modular course, the student will receive an e-certificate from the London School of Business and Finance in Singapore.
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The Corporate Data Science course familiarize students with basic statistical concepts and applications for collection, analysis, and interpretation of data for collection, analysis and interpretation of data for decision making using data.

Students attending this course will receive a skills achievement modular certificate/Nano Degree awarded by LSBF Singapore. This is useful if they intend to pursue a career in the data science, business analytics industry or in occupations where data management is applied.

Students are fully supported throughout the duration of this course. Progress assessments are conducted frequently to check on the students’ development. Student performance and satisfaction is monitored to ensure that the course meets the students’ personal development needs; and industry contacts ensure that the programme is relevant and suitable for the demands of a career in the industry.

Students looking to pursue a career in business or data analytics; or just simply looking to advance their skills and knowledge can apply for this course.

Suitable profiles are under these categories, but not restricted to:

  1. Working professionals and PMETs in any industry sector,
  2. Senior Accounts Executives,
  3. Finance Managers,
  4. Internal Auditors and
  5. Tax Executives
  6. Variable workforce who are returning to the workforce.
  7. Full Time National Service (NSF).
  8. Anyone who is keen to improve their digital application skills.

Upon successfully completing the Corporate Data Science course, students will gain the ability to do the following:

  • Explain statistical methods and applications.
  • Appraise descriptive analysis to understand relationships.
  • Use Data Fitting into Statistical Distribution Functions.
  • Apply Kolmogorov-Smirnov Method for Non-Parametric Test.
  • Apply Linear Regression Analysis for a given problem statement and dataset.
  • Use Data Simulation functions for implementing Probability and Statistical Distribution Functions.
  • Apply Statistical Graphic functions for data illustration.
  • Analyse relationships using statistical modelling techniques.
  • Evaluate model effectiveness.


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