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Data Science Training - Using R and Python

Get better with each session of Data Science Training. Talk and understand the language of data and communicate your findings with different techniques and tools that you will gain expertise with the help of this online Data Science Training.

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#1

Data Science is the most demanding IT skill

$106 K+

The average salary for a Mid-Career Data Scientist is $106,010. (Payscale)

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You Should Join Our Classes If You Are:

  • Just starting off & aren’t sure where to start from
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  • Looking to brush up your skills & master the course
  • Willing to get better in your current or new job

190 K

United States leads the data science job market, requiring 190,000 data scientists by next year.

$16 B

Data science industry is expected to touch US$ 16 billion by 2025

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Instructor-led Live Online Data Science Classes


Starting
Duration
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08 Jun

WEEKDAY - Filling Fast

8 Weeks

09.00 - 10.30 PM EST

USD 1499

USD 1274

Flat 15% Off

12 Jun

WEEKDAY

8 Weeks

09.00 - 10.30 PM EST

USD 1499

18 Jul

WEEKEND

8 Weeks

08.00 - 11.00 AM EST

USD 1499

29 Jun

WEEKDAY

8 Weeks

09.00 - 10.30 PM EST

USD 1499

09 Jul

WEEKDAY

8 Weeks

09.00 - 10.30 PM EST

USD 1499

USD 899

Flat 40% Off

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Detail
WEEKDAY - Filling Fast

08 Jun


8 Weeks

09.00 - 10.30 PM EST

USD 1499

USD 1274

Flat 15% Off

Enroll Now
WEEKDAY

12 Jun


8 Weeks

09.00 - 10.30 PM EST

USD 1499

WEEKEND

18 Jul


8 Weeks

08.00 - 11.00 AM EST

USD 1499

WEEKDAY

29 Jun


8 Weeks

09.00 - 10.30 PM EST

USD 1499

WEEKDAY

09 Jul


8 Weeks

09.00 - 10.30 PM EST

USD 1499

USD 899

Flat 40% Off

Early Bird Discount

Enroll Now

Easy Installments

Enroll Now and pay Later (on request)

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Data science Training Course Roadmap

Scroll through the concepts that we cover in our Data Science Course

Data Science & R

    • What is Data Science
    • Understanding the Data
    • Importance of Data Science
    • How is data science different from BI and Reporting?
    • End to End Data Science Project Life Cycle
    • How does Predictive Analysis Work
    • Primer to R programming
    • What is R? Similarities to OOP and SQL
    • Installation of R and R-studio
    • 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 long and wide format
    • Hands-on Assignment, Real Scenarios, MCQs, Practice Tests on IR

Python & Statistical Analysis

    • What is Python?Importance of Python in Data Science
    • Python Installation Guidelines
    • Numerical parameters to represent data
    • NumPy for mathematical computing
    • Scientific computing with Python
    • Text, color map, markers, widths with Matplotlib.
    • Importing and exporting datasets in Python
    • Feature Engineering: Feature Selection and Extraction
    • Statistical parameters to represent data
    • Apply function for parallel processing with Python
    • Statistical and Non-statistical Analysis
    • Population and Sample
    • Statistical Analysis Process, Data Distribution
    • Hands-on Assignment, Real Scenarios, MCQs, Practice Tests on Python, Statistics

Machine Learning

    • Types of Machine Learning
    • What is Supervised, Unsupervised and Reinforcement
    • Decision trees and Random Forest
    • Advantages of tree-based models?
    • Algorithms used in Machine Learning techniques
    • Difference between Data Science, Machine Learning and AI
    • Gradient Descent in Boosting Algorithms
    • Multiple linear regression
    • Linear regression vs Logistic regression
    • Understanding Naïve Bayes, Bayes theorem
    • Clustering - K-Means & Hierarchical
    • Hands-on Assignment, Real Scenarios, MCQs, Practice Tests on Machine Learning

AI, Deep Learning, NLP

    • Working with Neural Network
    • Functioning & Usage
    • Convolutional Networks
    • Recurrent Neural Networks
    • AutoEncoders
    • Long Short Term Memory
    • Deep learning with Keras
    • What is TensorFlow?
    • Deep learning with TensorFlow
    • Gradient Descent in Neural Networks
    • Defining and composing models, and deploying TensorBoard.
    • Important Parameters of Perceptron
    • Recursive Neural Tensor Network theory
    • Hands-on Assignment, Real Scenarios, MCQs, Practice Tests on Deep Learning & NLP

Big Data Hadoop

    • Hadoop Installation and Setup
    • Hadoop Architecture, Distributed Storage
    • Hadoop Cluster and its Architecture
    • Hadoop’s Key Characteristics & components
    • Understanding HDFS and MapReduce
    • Hadoop Hive, the architecture of Hive
    • OOPs Concepts, Functional Programming
    • Data Frames and Spark SQL
    • Stream Processing Frameworks and Spark Streaming
    • Machine Learning Using Spark (MLlib)
    • Apache Flume and Apache Kafka
    • Maintenance, Monitoring, and Troubleshooting

Data Visualization with Tableau

    • Why data visualization is it important for Data-Analyst
    • Tableau workbook walkthrough
    • Instruction of creation of your own workbooks
    • Building interactive dashboards using Tableau
    • Connecting to Excel Files & Text Files
    • Demo of few more workbooks
    • Organizing Data and Visual Analytics
    • Boolean, If-Then calculations, and Case
    • Calculations and Expressions
    • Maps, Custom Geocoding, Polygon Maps
    • Integrating Tableau with R
    • Box and Whisker's Plots
    • Gantt Charts, Waterfall Charts, Pareto Charts
    • Hands-on Assignment, Real Scenarios, MCQs, Practice Tests on Tableau

Data Science training Certification Course Roadmap

  • What all do we cover in our Data Science Courses

    The Data Science learning path that you get to cover at JanBask Training is very informative and engaging. It has been prepared after vigorous market research on the trends of Data Science, industry needs, etc. Take a look at the things that we cover in this course.

    • What is Data Science?
    • Data Science Life Cycle
    • What is Machine Learning?
    • What is Business Analytics?
    • What is Artificial Intelligence?
    • How is data science different from BI and Reporting
    • End to End Data Science Project Life Cycle
    • knowledge of the concepts of data collection & data mining.
    • Why R and importance of R in Analytics
    • Installation of R and R-studio
    • Working Directories
    • Data Types, Operators, Loops-For and While
    • If-else statements, Nested statements
    • Working with Vector and Matrices
    • Reading and Writing Data in R
    • Working with Data, Manipulating Data
    • Objects, and Vectors
    • Why Python for data science?
    • Overview of Python- Starting with Python
    • Installation of Python
    • Python Editors & IDE’s
    • Understand Jupyter notebook & Customize Settings
    • Concept of Packages/Libraries
    • Installing & loading Packages & Name Spaces
    • Data Types & Data objects/structures
    • List and Dictionary Comprehensions
    • Control flow & conditional statements
    • Definition and computation of the probability
    • Measurement of central tendencies and its applications
    • Spreads, Distributions(Normal, Z-distribution, Binomial, Poisson)
    • Various types of probability distributions(Continuous and discrete)
    • Measures of Central Tendencies and Variance
    • 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
    • Correlation, Covariance, and Causation
    • Supervised Learning
    • Algorithms in Supervised learning
    • Regression & Classification
    • Regression vs classification
    • Computation of correlation coefficient and Analysis
    • Multivariate Linear Regression Theory
    • Coefficient of determination (R2) and Adjusted R2
    • Model Misspecifications
    • Economic meaning of a Regression Model
    • Bivariate Analysis
    • Naive Baye classifier, Model Training
    • ANOVA (Analysis of Variance)
    • What is Clustering
    • Supervised vs Unsupervised learning
    • Data Mining Process
    • Measure of distance
    • Hierarchical Clustering / Agglomerative Clustering
    • Non-clustering, K-Means Clustering
    • dimension reduction
    • Advantages of PCA
    • Calculation of PCA weights
    • Definition of a network (the LinkedIn analogy)
    • The measure of Node strength in a Network
    • What is Market Basket / Affinity Analysis
    • The measure of distance/similarity between users
    • Pre-processing, corpus Document-Term Matrix (DTM) and TDM
    • Why is Deep Learning taking off?
    • Advantage of Deep Learning
    • What is the difference between ML, DL and AI?
    • Why is deep learning important?
    • Sharing Variables
    • Activation Functions
    • Illustrate Perceptron
    • Training a Perceptron
    • Neural Networks with TensorFlow
    • Convolutional Neural Networks (CNN)
    • Convolution and Pooling layers in a CNN
    • Understanding and Visualizing a CNN
    • Recurrent Neural Networks (RNN)
    • What is Natural Language Processing?
    • What Can Developers Use NLP Algorithms For?
    • Open Source NLP Libraries
    • Topic Modeling
    • Sentiment Extraction
    • Lexicons and Emotion Mining
    • Hadoop Installation and Setup
    • Hadoop ecosystem components
    • Hadoop’s Key Characteristics
    • What is Big Data & its analytics
    • Hadoop Ecosystem and HDFS
    • Hadoop Core Components
    • Hadoop Cluster and its Architecture
    • Rack Awareness and Block Replication
    • YARN and its Advantage
    • MapReduce Framework and Pig
    • Apache Spark Next-Generation Big Data Framework
    • How Spark differs from other frameworks?
    • What is Scala
    • Scala in other Frameworks
    • Introduction to Scala REPL
    • Basic Scala Operations
    • Variable Types in Scala
    • Control Structures in Scala
    • Understanding the constructor overloading,
    • Various abstract classes
    • The hierarchy types in Scala,
    • Foreach loop, Functions and Procedures
    • Collections in Scala- Array
    • Overview to Spark
    • Spark installation, Spark configuration,
    • Spark Components & its Architecture
    • Spark Deployment Modes
    • Limitations of MapReduce in Hadoop
    • Working with RDDs in Spark
    • Introduction to Spark Shell
    • Deploying Spark without Hadoop
    • Parallel Processing
    • Spark MLLib - Modelling BigData with Spark
    • what is Kafka, Why Kafka,
    • Configuring Kafka Cluster
    • Kafka architecture
    • Producing and consuming messages
    • Operations, Kafka monitoring tool
    • Need of Apache Flume
    • What is Apache Flume
    • Understanding the architecture of Flume
    • Basic Flume Architecture
    • What is Data Visualization
    • Overview to Tableau 10.0
    • Installing Tableau, Establishing Connection
    • Tableau interface
    • Connecting to DataSource
    • Installation of Tableau Desktop
    • Architecture of Tableau
    • Connection to Excel, cubes, and PDFs
    • Data extraction, Data blending
    • Calculations to your workbook
    • Mapping data in Tableau
    • Custom Geocoding, Polygon Maps.
    • Web Mapping Services.
    • Background Images.
    • Dashboards and Stories

Course Curriculum

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Our Reviews


6K + Learners | 1625 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.

Jeffrey

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.

Zakaria

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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