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Data Science

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.

Scheduled Batches

30 Dec
Weekends (8 Weekends)
Weekend Class Room |     8.00 AM - 11.00 AM
30 Dec
Weekends (8 Weekends)
Weekend Online |     8.00 AM - 11.00 AM

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.
  • 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 Course Content

    1. 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?


    2. Statistics: 

    Section 1: Descriptive Statistics 

    • Intro to Research Methods
    • Central tendency & Variability and Central limit theorem
    • Variance and Range parameters
    • Normal & Sampling distributions

     Section 2: Inferential Statistics 

    • Intro to Predictive modeling
    • Hypothesis testing: t-tests/z-test(1-sample,paired sample)
    • Correlation
    • Regression
    • Chi-square test
    • Analysis of Variance (ANOVA)

    Section 3: practice lab


    3. R & Python Programming: 

    Section 1: Environment setup & R/Python language basics: 

    • 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, Python and Various GUI 

    Section 2: Exploratory Data Analysis (EDA) and Data Preprocessing techniques in R/Python language: 

    • Data structures & data types(Vectors, Matrices, Lists, Factors & Data frames/Pandas)
    • Importing Data(from different
    • File formats, Databases, Stats software and web)
    • Exporting Data
    • Viewing Data
    • Handling Missing Values
    • Date & Time
    • Understanding the cor() in R & Python
    • EDA functions like summarize(), list()
    • Multiple packages in R & Python for data analysis
    • The Fancy plots like Segment plot
    • HC plot in R & Python
    • Understanding on Data Visualization
    • Graphical functions present in R & Python
    • Plot various graphs like table plot, histogram, boxplot
    • Customizing Graphical Parameters to improvise the plots
    • How to use ggplot2/matplot libraries

     Section 3: practice lab


    4. Machine Learning: 

    Section 1: Classification 

    • Logistic Regression
    • K-Nearest Neighbors (K-NN)
    • Support Vector Machine (SVM)
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • XG Boost Classification
    • Evaluating Classification Models Performance like Confusion matrix, ROC curve, F-Score…

    Section 2: Regression 

    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Support Vector Regression (SVR)
    • Decision Tree Regression
    • Random Forest Regression
    • Evaluating Regression Models Performance like R-Squared and Adjusted R-Squared Intuitions 

    Section 3: Clustering

    • What is Clustering
    • K- Means Clustering
    • Hierarchical Clustering
    • LDA (Latent Dirichlet allocation) 

    Section 4: Recommendation

    • What is Recommendation
    • Association rules using generator
    • Content and Collaborative filtering recommendation techniques
    • Recommendation engine building using R/Python libraries


    5. Real Time Projects: 

    Section 1: Real time Projects

    We will teach 2 Real time end to end projects in Health Care and Retail domain industries.

    Section 2: Tableau

    • Introduction about Tableau and building First Bar chart
    • Time series, Aggregation, and Filters
    • Maps, Scatterplots, and Dashboards
    • Joining and Blending Data, PLUS: Dual Axis Charts 

    Section 3: Resume Preparation


  • 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.




The trainer here for Data Science is very skilled. Project explanation is also quite good. Thanks to our trainer for all the guidance & support. Having understood the demand for Big Data & Data Science.



Data Science Trainer

The best thing about this Data Science training program here is that a lot of real-time concepts will be discussed. Topics like Data Mining, Data Cleaning & Data Modeling have been explained very well.


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