Course Content
1. Fundamentals
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1.1 Statistics
1.2 Probability and Probability Distributions
2. Data Science using R Programming
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2.1 Introduction: What is Data Science?
2.2 Introduction to R
2.3 Basic Operations in R Programming
2.4 Data Handling in R Programming
2.5 R data structure
2.6 Using functions in R
2.7 Introduction To Machine Learning
2.8 Machine Learning Concepts & Terminologies
2.9 Linear Regression
2.10 Hypothesis Testing
2.11 Decision Trees And Supervised Learning
2.12 Unsupervised Classification Algorithms
2.13 Introduction to Deep Learning
Data Science based on R programming
About Lesson
  • Hypothesis Testing
  • Null Hypothesis, P-Value
  • Need for Hypothesis Testing in Business
  • Two tailed, Left tailed & Right tailed test
  • Hypothesis Testing Outcomes : Type I & II erros
  • Parametric vs Non-Parametric Testing
  • Parametric Tests , T – Tests : One sample, two sample, Paired
  • One Way ANOVA
  • Importance of Parametric Tests
  • Non Parametric Tests : Chi-Square, Mann-Whitney, Kruskal-Wallis etc.,
  • Which Test to Choose?
  • Ascerting accuracy of Data
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