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Data Science Certification Training using R

Time to scale up and learn the much trending Data Science skills. Register for a Data Science Training that can boost your skill set and get you a very high paying job. This modern-day online Data Science Training is like one of the necessities in this technology era that you need to learn in order to excel in the present-day job market.

Next Class Begins in -1 days - 15 Dec 2019

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Instructor-led Live Online Classes

Upcoming Data Science Classes

15 Dec

15 Dec ( 6 Weeks)

09:00 PM - 10:30 PM EST

USD 549
Discount on Call
25 Dec

25 Dec ( 6 Weeks)

09:00 PM - 10:30 PM EST

USD 549
04 Jan

04 Jan ( 6 Weeks)

09:00 PM - 10:30 PM EST

USD 549
14 Jan

14 Jan ( 6 Weeks)

09:00 PM - 10:30 PM EST

USD 549

15 Dec ( 6 Weeks)

09.00 - 10.00 PM EST

USD 549
Discount on Call

25 Dec ( 6 Weeks)

09.00 - 10.00 PM EST

USD 549

04 Jan ( 6 Weeks)

09.00 - 10.00 PM EST

USD 549

14 Jan ( 6 Weeks)

09.00 - 10.00 PM EST

USD 549

Earn your Data Science Certificate

Best-in-class content by leading faculty & industry leaders in the form of videos and projects, assignments & live sessions.

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Data Science Certification Course Roadmap

With JanBask Training, you can be assured of one thing for sure, and that is The Contemporary Syllabus

Data Science with R Programming

    • Learn about getting to Know R: Download and Install R
    • Get a good knowledge about Functions and Data Structures
    • Learn about Loops and Flow Control
    • Get a good knowledge about working with Vector and Matrix
    • Learn about Reading and Writing Data in R
    • Get a good knowledge about working with Data
    • Learn about Manipulating Data
    • Get a good knowledge of Data Modeling
    • Learn about Graphics in R
    • Get a good knowledge about Case Study

Statistical Analysis

    • Get a good knowledge about an introduction to Statistical Analysis
    • Learn about- why is statistical analysis required
    • Get a good knowledge about Random Variables, Probability Distribution
    • Learn about Properties of Normal Distribution
    • Get a good knowledge about Sampling Variation & Central Limit Theorem
    • Learn about Confidence Interval
    • Get a good knowledge about Hypothesis Testing: Single and 2 tailed Tests
    • Learn about Comparison of two population
    • Get a good knowledge about ANOVA
    • Learn about Data Processing

Supervised & Unsupervised

    • Learn about Linear Regression
    • Get a good knowledge about Logistic regression
    • Learn about K nearest neighbor
    • Get a good knowledge about Decision Trees
    • Get a good knowledge about Random Forest
    • Learn about Adaboost
    • Get a good knowledge about Naïve Bayes
    • Get a good knowledge about SVM
    • Learn about Introduction
    • Get a good knowledge of Clustering Algorithms
    • Get a good knowledge about Market Basket Analysis 1
    • Learn about Principal Component Analysis

Time Series Forecasting & Data Visualization

    • Get an introduction of Time Series Forecasting
    • Learn about Performance Evaluation
    • Learn about Forecasting Methods
    • Learn about Smoothing Methods
    • Learn about Regression-Based Models
    • Data Visualization
    • Get an Introduction to Tableau
    • Learn about Establishing Connections
    • Learn about Joints and Union
    • Get to learn about Data Blending
    • Learn about Creating Dashboards
    • Get to learn about Creating Stories

AI & Deep Learning

    • You will get an introduction to Neural Network
    • Learn about Functioning & Usage
    • Learn About the Advantages and Disadvantages
    • Get a good knowledge of Convolutional Networks
    • Learn about Recurrent Neural Networks
    • Learn about AutoEncoders
    • Get a good knowledge of Long Short Term Memory
    • Learn about Deep Learning with Keras
    • Learn about Deep learning with TensorFlow
    • Get a working knowledge of Practical implementation of all these

Data Science with Python

    • Get an Introduction to Python
    • Learn about Installing Python
    • Learn about the Key features of Python
    • Learn about Python variables
    • Learn about Key Data Structures
    • Get a working knowledge of the Data Structures & key libraries in Python
    • Learn about Key Data Structures - Contd.
    • Get a good knowledge of Control Flows
    • Learn about Numpy 1 & Numpy 2
    • Learn about Pandas basics
    • Learn about Pandas 2
    • Get knowledge of Matplotlib

Data Science Certification Course Roadmap

  • With JanBask Training, you can be assured of one thing for sure, and that is The Contemporary Syllabus. Our team does extensive market research before penning down every topic that they choose to teach in this Data Science Course. We know what the industry demands. Our Data Science Learning Path is something that will easily ready you for a thriving data science career.

    You will also learn about the following supporting concepts

    • Need for Data Scientists
    • Foundation of Data Science
    • What is Business Intelligence
    • How is data science is different from BI and Reporting?
    • What is Data Analysis, Data Mining, and Machine Learning
    • Analytics vs Data Science
    • What is the day to day job of Data Scientist
    • Value Chain
    • Types of Analytics
    • Lifecycle Probability
    • Analytics Project Lifecycle
    • A Primer to R programming
    • What is R? Similarities to OOP and SQL
    • Types of objects in R – lists, matrices, arrays, data.frames etc.
    • Creating new variables or updating existing variables
    • If statements and conditional loops - For, while, etc.
    • String manipulations
    • Subsetting data from matrices and data.frames
    • Casting and melting data to a long and wide format.
    • Merging datasets
    • The data types in R and its uses
    • Built-in functions in R
    • Subsetting methods
    • Summarize data using functions
    • Use of functions like head(), tail(), for inspecting data
    • Use-cases for problem-solving using R
    • Various phases of Data Cleaning
    • Functions used in Inspection
    • Data Cleaning Techniques
    • Uses of functions involved
    • Use-cases for Data Cleaning using R
    • Dealing Prediction problem
    • Forecasting for industry
    • Optimization in logistics
    • Segmentation in customer analytics
    • Supervised learning
    • Unsupervised Learning
    • Optimization
    • Types of AI: Statistical Modelling, Machine Learning, Deep Learning,
    • Optimization, Natural Language Processing, Computer vision, Speech
    • Processing, Robotics
    • Assumptions
    • Model development and interpretation
    • Sum of least squares
    • Model validation – tests to validate assumptions
    • Multiple linear regression
    • Disadvantages of linear models
    • Need for logistic regression
    • Logit link function
    • Maximum likelihood estimation
    • Model development and interpretation
    • Confusion Matrix – error measurement
    • ROC curve
    • Measuring sensitivity and specificity
    • Advantages and disadvantages of logistic regression models
    • Definition and computation of a probability
    • Measurement of central tendencies and its applications
    • Spreads, Distributions(Normal, Z-distribution, Binomial, Poisson) and
    • various types of probability distributions(Continuous and discrete)
    • Sampling and Sampling distributions
    • Measures of shape( Skewness and Kurtosis)
    • Measures of the relationship between variables(Correlation, causation)
    • Hypothesis Testing(t-test, Chi-square, Anova)
    • Measures of Dispersion( Variance, std. deviation, Range)
    • Prediction and Confidence interval-Computation and Analysis
    • Probability Distributions: Discrete Random Variables
    • Mean, Expected Value
    • Binomial Random Variable
    • Poisson Random Variable
    • Continuous Random Variable
    • Normal distribution
    • Using Software-Real Time Problems
    • Supervised LearningWhat is supervised learning
    • Algorithms in Supervised learning
    • Steps in Supervised learning
    • Regression & ClassificationRegression vs classification
    • Computation of correlation coefficient and Analysis
    • Performance and accuracy measurement of a Model
    • Naive Baye’s classifier, Model Training, Validation and Testing
    • Ordinary Least squares, Variable selection
    • R-Square coefficient and RMSE as strength of model, Prediction and
    • Introduction to Tableau
    • Installation of Trial Version of Tableau Public
    • Design Flow, Data Viewing
    • Connecting Tableau to various Data Files
    • Measures and Dimensions
    • Colors, Labeling, and formatting, Exporting Worksheet
    • Basics of TableauA-B Ad-hoc Testing, Aliases
    • Reference Line, Anomaly detection
    • Sorts and Filters, Time Series
    • Chart plotting, Heat Maps
    • Data Joining, Data Blending
    • Advanced Concepts of TableauTrend Line Analysis
    • Dashboard Creation
    • Formatting in tableau
    • Forecasting using Exponential Smoothing
    • Introduction to SQL and Databases
    • SQL developer installation
    • Data types
    • Data types and Operators
    • Create and Drop database
    • DDL, DML, DCL, TCL, Sorting commands and other keywords
    • Advanced SQL-Wildcards, Constraints, Joins, Unions, NULL, Alias, Truncate, Views, Subqueries

Course Curriculum

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Data Science Using R Corporate Training

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Our Reviews

6K + Learners | 1666 Reviews

Customer Reviews

Donny Papic

This course was amazing. Great way to learn some of the practical elements of data science. Coming from a maths background I really appreciated the fact that the theory behind the models we built was explained. A wonderful course and I am now about to enroll in other courses.


This is a pretty good beginner course for anyone totally new to this field. Still, this course is a subset of Machine Learning A-Z course by the same team. Almost everything covered in this course is covered in that plus it is Hands-On in Python and R. This course basically provides a whole workflow of a data science project.

Chenaj Teja Potu

An absolutely great course. I'm new to learning about Data Science and Kirill's explanation really is step-by-step and makes no assumptions about what you should know. He explains why each action is done and how it applies to the real world. By the time you finish this course, you'll really feel much more capable of analyzing data.

Bosun Sogeke

Really this course is very helpful and I have learned a lot of new areas in data science especially data preparation, data cleaning and data analysis. I have learned about data modeling using logistic regression which will be very helpful in my day to day work.

Kim Adams

Well-structured and practice-oriented course, I would recommend it to anyone who wants to quickly get up to speed in such core Data Science skills as data visualization, ETL, modeling, and communication.

Gururaju BM

Loved this course. Lots of good material and the instructor is great. He keeps things exciting and keeps challenging. This course kind of made me realize what I like the most and pushed me towards a career path. Definitely, recommend it if you are anything like me- a fresher with little knowledge in data science and interested in pursuing a career in this field.

Nibha Thakur

I think the course did well in terms of the flow of topics and covering the fundamentals. It does add great value to beginners. However, I expected it to cover data mining and visualizations in more detail. Anyway, kudos to super data science team for their efforts.

Debmalya Datta

Very good course, love the outline of a broad topic which still has lots of practical examples. The speed is exactly right for a beginner, he doesn't dwell too long on any subject or details but instead is focused on necessary practicalities. Good job!

Prakash Shekade

This is a good survey course that has helped me not only develop the skills necessary to break into the data science field but also to understand how the entire process works as a whole. A big thank you to the JanBask team.

Buddhadev Choudhury

This is an amazing course. Probably the best introduction possible to data science. The instructor is speaking in a very understandable way and spend time on the useful details. Thanks.


Very good course. Gave me the information and tools that I was looking for in order to apply for Data Science jobs. Data Science A-Z course by is very nice & interactive course. I am very impressed with the instructor’s approach in explaining all the data science concepts with related examples.Thanks a million.

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