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Data Science With R & Python

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining

Why this course ?

1.Data Scientist is one of the fastest-growing and highest paid jobs in tech.

2.Employers are waking up to the fact that employees with the ability to use data and analytics to solve business problems are increasingly valuable, whatever their background or position in an organization.

Course Features

  • Instructor Live Sessions

    30hrs of Online Live Instructor-led Classes. Weekend class:10 sessions of 3 hours each and Weekday class:15 sessions of 2 hours each.
  • Real-life Case Studies

    Live project based on any of the selected use cases on the above selected Domain.
  • Assignments

    Each class will be followed by practical assignments which can be completed before the next class.
  • Lifetime Access

    You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.
  • 24 x 7 Expert Support

    We have 24x7 online support team available to help you with any technical queries you may have during the course.
  • Certification

    Towards the end of the course, you will be working on a project. Covalent certifies you as an course Expert based on the project.

Course Curriculum

  • Data Science With R & Python Course Content

    Data Science course content                                      

    Introduction about Data Science:

    • What is data science?
    • Need of data science?
    • Use cases of Data science
    • How is data science different from business intelligence?
    • Who are data scientists?
    • What are the skills required and life cycle of data science?


    1. R & Python programming

    2. Model building 


    R & Python Programming:

    Section 1: Data science with R & Python

    • Application of machine learning
    • Understand Business Analytics and R, Python
    • Knowledge on the R & python language
    • Community and ecosystem
    • Understand the use of 'R & python' in the industry
    • Compare R, Python with other software in analytics
    • Install R, Python and the packages useful for the course
    • Perform basic operations in R, Python using command line
    • Learn the use of IDE R, Pyhton Studio and Various GUI
    • Use the ‘R, Python help’ feature in R, Python
    • Knowledge about the worldwide R, Pyhton community collaboration 

    Section 2: Introduction to R & Python Programming

    • The various kinds of data types in R, Pyhton and its appropriate uses
    • The built-in functions in R & Python like: seq(), cbind (), rbind(), merge()
    • Knowledge on the various Sub setting methods
    • Summarize data by using functions like: str(), class(), length(), nrow(), ncol()
    • Use of functions like head(), tail(), for inspecting data
    • Indulge in a class activity to summarize data
    • If Else
    • Nested If Else
    • For Loop
    • While Loop

    Section 3: Data Manipulation in R & Python

    • The various steps involved in Data Cleaning
    • Functions used in Data Inspection
    • Tackling the problems faced during Data Cleaning
    • Uses of the functions like grepl(), grep(), sub()
    • Coerce the data
    • Uses of the apply() functions

    Section 4: Data Import Techniques in R & Python

    • Import data from spreadsheets and text files into R & python
    • Import data from other statistical formats like sas7bdat and spss
    • Packages installation used for database import
    • Connect to RDBMS from R, pyhton using ODBC and basic SQL queries in R & Python
    • Basics of Web Scraping

    Section 5:  Exploratory Data Analysis

    • Understanding the Exploratory Data Analysis(EDA)
    • Implementation of EDA on various datasets
    • Boxplots
    • Understanding the cor() in R & Python
    • EDA functions like summarize(), llist()
    • Multiple packages in R & Python for data analysis
    • The Fancy plots like Segment plot
    • HC plot in R & Python

    Section 6: Data Visualization in R & Python

    • Understanding on Data Visualization
    • Graphical functions present in R & Python
    • Plot various graphs like tableplot, histogram, boxplot
    • Customizing Graphical Parameters to improvise the plots
    • ggplot2 


    Model building:

    Section 7:  Data Pre-processing

    • Get the dataset
    • Importing the Libraries
    • Missing Data
    • Categorical Data
    • Splitting the Dataset into the Training set and Test set
    • Feature Scaling
    • Data Pre-processing Template! 


    Supervised Techniques:

    Section 8: Regression

    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Support Vector Regression (SVR)
    • Decision Tree Regression
    • Random Forest Regression
    • Evaluating Regression Models Performance 

                   R-Squared Intuition

                  Adjusted R-Squared Intuition

                  Interpreting Linear Regression Coefficients


    Supervised Techniques Classification:

    Section 9:  Classification

    • Logistic Regression
    • K-Nearest Neighbours (K-NN)
    • Support Vector Machine (SVM)
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • Evaluating Classification Models Performance

                  False Positives & False Negatives

                  Confusion Matrix

                  Accuracy Paradox

                  CAP Curve

                  CAP Curve Analysis


    Unsupervised Techniques:

    Section 10:  Clustering

    • K -Means Clustering

    Section 11:  Association Rule Learning

    • Apriori (Market basket Analysis) 

    Section 12: Text mining

    • Sentiment analysis (Twitter)
    • Natural Language processing (NLP)

    Section 13: Deep Learning

    What is Deep Learning?

    Artificial Neural networks (ANN)

    • The Neuron
    • The Activation Function
    • How do Neural Networks work?
    • How do Neural Networks learn?
    • Gradient Descent
    • Stochastic Gradient Descent
    • Back propagation

    Convolutional Neural Networks (CNN, Image recognition)

    • What are convolutional neural networks?
    • Step 1 - Convolution Operation
    • Step 1(b) - ReLU Layer
    • Step 2 - Pooling
    • Step 3 - Flattening
    • Step 4 - Full Connection
    • SoftMax & Cross-Entropy

    Section 14: Model Selection & Boosting

    Model Selection

    k-Fold Cross Validation

    Grid Search


    Section 15: Projects

    2 Real time projects

    Section 16: Statistics

    Statistics will be covered during the course where ever it’s required.



      Section 1: Introduction about Tableau

      Section 2: Tableau Basics: Your First Bar chart

      Section 3: Timeseries, Aggregation, and Filters

      Section 4: Maps, Scatterplots, and Dashboards

      Section 5: Joining and Blending Data, PLUS: Dual Axis Charts

      Section 6: Table Calculations, Advanced Dashboards, Storytelling

      Section 7: Advanced Data Preparation



    1.Introduction about sql server

    2.Introduction TSQL (transact structured query language) 


    4.DML Commands 




    8. Stored procedures & views


  • Data Science Project

    We will provide 2 Real time projects.


  • Can I attend a demo session before enrolment?


  • What if I miss a class ?

    If you miss a class we can provide recording video for particular session and same session you have to attend another batch also

  • Will I get placement Assistance ?


  • Do I receive a certificate for training ?

    • Once you are successfully through the course you will be awarded with Covalent's Training certificate.
    • Covalent certification has industry recognition and we are the preferred training partner for many MNCs.
  • what support is available after the training?

    Doubts clarification up to getting a job

    Resume preparation

    Malk interviews

    Placement Assistance

  • What Features do you provide?

    • Led sessions with corporate trainers
    • Course Material
    • Real time projects with industry experts
    • Day wise Assignments (Tasks)
    • Lab facilities
    • Placement assistance
    • Resume preparation
    • Software installation
    • Doubts  clarifications


  • Course Completion Certificate

    • Once you are successfully through the course you will be awarded with Covalent's Training certificate.
    • Covalent certification has industry recognition and we are the preferred training partner for many MNCs.


Data Science Videos are Under construction


Prasad Reddy

Associate data scientist

I have taken the Data Science program from Covalent. I had a very good learning experience there. The course content, lectures are very effective to understand the concept and described in an organized way. The trainers are also very good. Lab access is very useful. Their Management support is helpful and provides a quick solution to all our queries.



Data scientist Engineer

Attending Covalent Data Science Academy is one of the most important and accurate decisions I have made in my life. The academy provides strong training on statistical and machine learning.

Covalent has given me a right platform to learn analytic's and grow my career. The Course Content was extraordinary and the professors are incomparable in terms of the teaching methodology ,this unique feature enabled me to a structured way of approaching my goal.


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