Course Features:

• 45 hours of Instructor led-Online Training
• Insightful presentations
• Hands-on training in R
• Thought provoking discussion
• Enable researchers to analyse their own data
• Anyone can look forward for completing the research projects using machine learning
  approach
• Participants can generate potential research papers for publications

Course Objectives:
Participants will learn Data Science approach to their Research Projects. Participants shall
discover trends & best practices in research methodology, biostatistics, learn R and apply the
machine learning concepts in life science projects to generate high quality publishable papers.
To Whom?

• Post graduates and Research Scholars in Medicine , Dental and Paramedical
• Post graduates and Research Scholars in Pharmacy Practice [IV PharmD, V PharmD,
  I M.Pharm, II M.Pharm , PhD Scholars]
• Post graduates and Research Scholars in Social Science
• This Program is suitable for the researchers who are in any stage of their project work
  [initial, middle or final]

Labs:
Hands-on session using R software
Course Outline:

• Essentials of Research Methodology
• Framing a research question, hypothesis, distilling objectives from research
  question, literature review
• Designing the Study (Case reports, case series, cross sectional, case-control, cohort ,
  RCT etc)
• Ethical aspects in Medical Research
• Sampling and Sampling techniques
• Randomization and Methods of Randomization
• Elements of Biostatistics Definition, Hypothesis, Sample Size, Errors, Level of significance, Statistical Significance, Power of the test, P value, Odds Ratio, Hazards Ratio, Incidence, Prevalence, Relative Risk, Attributable Risk, Attributable Risk %, Population Attributable Risk, Population Attributable Risk %, Number needed to treat Hypothesis Testing, Paramteric vs Non parametric tests

• Introduction to R programming
• Data Structure and Data Exploration
• Data Manipulation
• Data Visualization
• Discovering Biostatistics using R
• Descriptive Statistics
• Inferential Statistics – Parametric and Non parametric tests
• Discovering Advanced Statistics using R
• Linear Regression
• Logistic Regression
• Decision Tree and Random forest
• Unsupervised Learning
• Association Rule