We offer introductory, applied and advanced courses for students who are interested in learning and making a career in data science. The courses have been designed based on the job requirements in the industry. These courses have an equal balance of theory and practical knowledge which will pave a way for the students into the corporate world.

Workshop in Data Analytics using R

Duration: 16 hours

Objectives:
  • To introduce participants to the fundamentals of data analytics using R
  • A case study methodology is adopted to reinforce basics and teach first-level data science algorithms.
Learning outcomes:
  • Use the R
  • Perform exploratory data analysis
  • Visualize data
  • Develop an appreciation for basic machine learning algorithms

Workshop on Predictive Analytics Using Python

Duration: 16 hours

Objectives:
  • To introduce participants to the fundamentals of data analytics using Python.
  • A case study methodology is adopted to reinforce basics and teach first-level data science algorithms.
Learning outcomes:
  • Use Python – Spyder tool
  • Perform all basic operations in the dataset using the libraries
  • Visualize data
  • Perform exploratory data analysis
  • Use Python for predictive analytics
  • Execute two machine learning algorithms

An applied course in data science using R / Python

Duration: 40 hours

☆ People who are interested in making a career in data science*
  • Bachelors/Masters in Engineering/Mathematics/Statistics/ Economics/Management.
  • A study of how to pose meaningful data science problems will be covered.

The course has been designed based on requirements for careers in the corporate world. A deeper study of how to pose meaningful data science problems will be covered.

Advanced course in Machine Learning using R / Python

Duration: 40 hours

Objectives:
  • Introduce advanced machine learning algorithms
  • Code advanced and cutting-edge machine learning algorithms using R / Python
  • Solve practical case studies
Learning outcomes:
  • Describe a flow process for data science problems (Remembering)
  • Classify data science problems into standard typology (Comprehension)
  • Develop Python codes for data science solutions (Application)
  • Correlate results to the solution approach followed (Analysis)
  • Assess the solution approach (Evaluation)