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DATA Science Specialization with R

Course Info


This course covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. A practical understanding of the fundamental methods used by data scientists including; statistical thinking and conditional probability, machine learning and algorithms, and effective approaches for data visualization will be covered in this course. We have designed this program by working closely with expert data analysts and scientists at leading technology companies, and in partnership with their hiring managers to ensure you emerge from the course with the skills and talents these companies are seeking.

Learn to:

  • Wrangle, extract, transform, and load data from various databases, formats, and data sources
  • Use exploratory data analysis techniques to identify meaningful relationships, patterns, or trends from complex data sets
  • Classify unlabeled data or predict into the future with applied statistics and machine learning algorithms
  • Communicate data analysis and findings through effective data visualizations

calendar_logo Approx 4 months.
members_logo 55 Enrollments (and counting)

Course Syllabus

Descriptive Statistics
  • Intro to Research Methods
  • Visualizing Data
  • Central Tendency
  • Variability
  • Standardizing
  • Normal Distribution
  • Sampling Distributions
Inferential Statistics
  • Estimation
  • Hypothesis Testing
  • t-tests
  • ANOVA
  • Correlation
  • Regression
  • Chi-square Test
Data Analysis using R
  • Introduction to R
  • R Packages
  • Data structures & data types
  • Functions
  • Control Structures
  • Importing Data from various sources
  • Cleaning Data with tidyr
  • Manipulating data with dplyr
Data Visualization with Tableau
  • Tableau Introduction
  • Data preparation under Tableau
  • Vizualizing Data – Maps and Images
  • Data analysis with Tableau
  • Calculation with Tableau
  • Authoring Dashboards with Tableau
Machine Learning with R
  • Introduction to Supervised and Unsupervised Learning
  • Linear Regression with Multiple Variables
  • Logistic Regression
  • Decision Trees [CART]
  • k-Fold Cross Validation
  • Bagging and Bootstrapping
  • Random Forest
  • Gradient Boosting (XGBoost)
  • Principal component Analysis
  • K-means clustering
  • Hierarchical Clustering
  • Market Basket Analysis

About Faculty





Kushagra Singhania
B.Tech and MBA(IIT, Delhi) and 7 years of experience in IT and consulting domains. He is passionate about Digital technologies and possess deep insights about the domain.

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