Getting Started with Data Science

Step into the world of data science now and open the doors to opportunity.



Crack Your Next Interview

Want to make it through the next interview you will appear for? Hone your skills with our five-part series of interview questions widely asked in the industry. With basic to advanced questions, this is a great way to expand your repertoire and boost your confidence.

Data Science Interview Questions
Python Interview Questions
SAS Interview Questions
R Interview Questions
Data Mining Interview Questions
AI Interview Questions
Test Your Skills

Think you have it in you? Test your skills with our series of Data Science quizzes and measure yourself to your expectations. Improvise in the process with questions carefully curated for different levels of difficulty.

R Quiz
SAS Quiz
R Quiz
SAS Quiz


Things to Learn

Choose where to begin, learn at your own pace:

Data science with R

Try R for Data Science

Data Analytics Tutorial
R vs SAS vs SPSS- Data Analytics Tools
Exporting Data from R to CSV, Text, SAS, SPSS, Stata
Importing Data in R
Manipulating and Processing Data in R
R Descriptive Statistics
Contingency Tables in R
R Data Types – Vectors, Matrices, Lists, Data Frames
Graphical Data Analysis with R
Hypothesis Testing In R
R Linear Regression Tutorial
R Nonlinear Regression Analysis
R Decision Trees
Cluster Analysis with R
Graphical Models in R
Top Real-World Graphical Models Applications
SVM Training and Testing Models in R with e1071
Bayesian Networks with R
Bayesian Methods with R
Probabilistic Inference in Bayesian Networks
Top 10 Real-World Bayesian Network Applications
R- Predictive and Descriptive Analytics
R Data Frames & Operations
Logistic Regression in R
Binomial and Poisson Distributions in R
Numeric and Character Functions in R
Random Forest in R
Lattice Package in R with Lattice Graphs
R Statistics for Statistical Programming in R
Principal Components and Factor Analysis in R
Bootstrapping in R & Bootstrap Resampling
OLS Regression in R
T-Tests in R- Welch t-test and its uses
ANOVA Model in R – Common Statistical Models
Survival Analysis in R Programming
Normal Distribution in R – Basic Probability distribution
R Cluster Analysis
Data Visualization in R
Classification in R
R Data Reshaping – Reshape Function and Reshape Package
Exploratory Data Analysis With R
Data Analytics with R, Tableau, and Excel
Chi-Square Test in R
Bar Charts in R | Histogram in R
Top 10 Data Analytic Tools
Best Data Analysis Software Systems
R Vs Python for Data Science and Statistics

Check out more cool technologies

Exploring the Concept

Let’s take a look at some facts about Data Science and its philosophies.

William S. Cleveland introduced data science as an independent discipline in 2001. This came with his article “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics”. According to him, six technical areas aggregate into the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory. In a 2012 article, DJ Patil claimed the coinage of the term along with Jeff Hammerbatch as a way to define their jobs at LinkedIn and Facebook, respectively.

Today, data science puts to use scientific methods, processes, algorithms, and systems hoping to extract knowledge and insights from data in forms structured and unstructured.

Data Science William S. Cleveland

William S. Cleveland