DataScience with Python and R

12000 Learners

Course Objective:

Prerequisite: No expertise is required; however basic knowledge High School Mathematics and Statistics will be required.

Course Content:


Introduction to Data Science
  • What is data science
  • Different fields of data science
  • The ven diagram for data science
  • Tools of data science
  • Introduction to R for data science
Introduction to Python programming
  • Introduction to Python language
  • Environmental setup for Python, Anaconda, Jupyter, Spider etc
  • Data types of Python, numbers, string
  • If, elif, Loops in python
  • Functions in python
  • Lambda function
  • Modules of Python
  • Import a module, call a function of a module
Object oriented Python
  • Introduction to Object oriented language
  • Class, object
  • Create class and object in python
  • Data hiding, inheritance
String manipulation and collections in python
  • Creating and accessing strings
  • Basic functions on strings
  • Indexing and slicing on string
  • Strings methods
  • List and its methods
  • Accessing lists
  • Tuple, set, dictionary and their methods
  • Indexing, slicing on list, tuple, set , dictionary
  • List comprehension and its uses
Introduction Numeric and Data Analysis Modules of Python
  • Introduction to Numpy
  • Creating 1D and 2D Numpy array
  • Slicing and indexing of Numpy Array
  • Introduction to Pandas
  • Series and DataFrame
  • Different functions on dataframe
  • Read external dataset using Pandas
Handling Data – Data Wrangling
  • What is Data Wrangling
  • Prepare data for use
  • Introduction to sklearn module of python
  • Find for missing value and impute
  • Fillna() and Imputer() of sklearn
Visualization library of Python
  • Using pandas plotting functions
  • Introduction to Matplotlib and Seaborn
  • Difference between the two
  • Scatter plot using matplotlib
  • Draw histogram, barchart, pie chart to any data
  • Create pairplot using seaborn
  • Concept of Box plot in seaborn
  • Drawing of 3D graphs
Introduction to R programming
  • Introduction to R and its features
  • Install R and RStudio
  • Different data types and functions in R
  • Control statements and loops in R
  • Vectors, lists, matrix, factors in R
  • R data frames
  • Data reshaping and merging
  • Reading data from external file (csv/excel)
R packages for Data manipulation
  • What is data exploration and data wrangling
  • R packages for data exploration
  • Installing and using the packges
  • Dplyr package and its functions
  • Chaining of different functions
  • Data.table package for fastest manipulation in data
Generating plots in R
  • Install ggplot2 visualization package
  • Concepts of geom() function
  • Different charts like, bar plot, histogram, pie chart etc
  • Box plot and its uses
  • Draw density plot and frequency polygon plot
  • Set axis labels and title of R chart
  • Draw plot with png in background
  • Save plots as an image in disc
Statistical inference
  • Why we need statistics
  • Terminology used in statistics
  • Measure of central tendency
  • Measure of spread
  • Correlation and covariance
  • Standard deviation
  • Standardization and normalization
  • Normal and binary distribution
  • Probability and types of probability
  • Hypothesis testing
  • ANOVA (Analysis of Variance)
Introduction to Machine learning (using R and Python)
  • What is machine learning
  • Types of machine learning
  • Concepts of supervised and unsupervised learning
  • Predictive modeling using Linear Regression
  • Simple and multiple linear regression in R and Python
  • Assumptions of linear regression
  • Accuracy test for the model
  • R2 score and RMSE score
Classification Technique
  • What is classification and its uses
  • Logistic regression to solve classification problem in R and Python
  • Difference with linear regression
  • Accuracy test for classification
  • Confusion matrix and classification report
  • True positive, false positive rate
  • Decision tree and random forest for classification in R and Python
  • Pruning of tress
  • Entropy and information gain
Unsupervised learning
  • Understanding unsupervised learning
  • Clustering and its uses
  • Different types of clustering
  • K means, canopy clustering and hierarchical clustering
  • Mathematics behind K means
  • K means process flow
  • Implement K means in R and Python
Dimensionality reduction (PCA)
  • What is dimensionality reduction
  • Feature extraction and feature elimination
  • Principal Component Analysis (PCA)
  • Apply PCA in R and Python
  • Advantages of PCA using example
  • Augmenting retail sales with Data Science
  • Prediction on Pokeman dataset
  • Analyzing pre-paid model of stock broking
  • Email spam filtering
  • Bigmart sale analysis
  • House price prediction
  • Analysis of stock market
  • Youtube data analysis

Key Features

  • Gain skills and competencies required in Industry by Experts.
  • Work on Real-time Projects depending upon the course you select.
  • Students work in a professional corporate environment.
  • Get a globally recognized Certificate form WebTek with our partner logos.
  • Global Brand recognition for Placements.


  • Course Duration: 3 MONTHS
  • Regular Batches: Online / Offline

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