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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.

Next Class Begins in 2 days - 27 Aug 2019

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Why Data Science Certification?

Facts and Figures that will tantalize you to sign up for Data Science Certification Training Stats and Data should also include Keywords

#1

Data Science is the most demanding IT skill

$106 K+

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

190 K

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

40%

Of data science tasks will be automated by 2020.

$16 B

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

Dive deep into the Data Science Career

Learn about Career benefits, in-demand skills, average salaries and tips to Crack Job Interview.

2.2 K

Students has successfully completed Data Science training and now working for top MNC's

Instructor-led Live Online Data Science Classes


Upcoming Data Science Classes

Starting
Duration
Price
27 Aug

27 Aug ( 6 Weeks)

09:00 PM - 10:00 PM EST

USD 799
Discount on Call
11 Sep

11 Sep ( 6 Weeks)

09:00 PM - 10:00 PM EST

USD 799
26 Sep

26 Sep ( 6 Weeks)

09:00 PM - 10:00 PM EST

USD 799
06 Oct

06 Oct ( 6 Weeks)

09:00 PM - 10:00 PM EST

USD 799
Detail

27 Aug ( 6 Weeks)

09.00 - 10.00 PM EST

USD 799
Discount on Call

11 Sep ( 6 Weeks)

09.00 - 10.00 PM EST

USD 799

26 Sep ( 6 Weeks)

09.00 - 10.00 PM EST

USD 799

Earn your Data Science Certificate

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Data science Training Course Roadmap

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

Data Science Introduction

  • Data Science Introduction & Business Statistics

    1. What Data Science is and Why Data Scientists are in demand
    2. Foundation of Data Science, overview to Data science
    3. Data Science motivating, Data Mining, and Data Analysis
    4. Introduction to Analytics, Types of Analytics, and Analytics Methodology
    5. Difference between Business Intelligence and Data Science
    6. Analytics Terminology, Analytics Tools, Analytics vs Data Science
    7. Overview to R, and Big Data, Hadoop, Spark, and Machine Learning
    8. Lifecycle Probability, Analytics Project Lifecycle
    9. Types of Variables, measures of central tendency and dispersion
    10. Variable Distributions and Probability Distributions
    11. Descriptive Statistics Introduction to Advanced Data Analytics
    12. Statistical inferences for various Business problems
    13. Computing basic statistics, Comparing the means of two samples
    14. Summarizing Data, Data Munging Basics

Hadoop and Spark Overview

  • Big Data Hadoop and Spark Overview

    1. Overview of Big Data and Hadoop
    2. Hadoop Cluster Architecture
    3. Map Reduce Concepts, Advanced Map Reduce Concepts
    4. Daemons of Hadoop and functionalities
    5. Implementation of Spark Applications
    6. Importing MySql and Creating hivetb
    7. Apache Spark vs Hadoop
    8. Advanced Hive Concept and Data File
    9. PIG, Module 7, HIVE, HBASE, Module-9, Module-11, and SQOOP
    10. Overview to Spark, Apache Spark, and Spark Core Architecture
    11. Spark Internals, and Spark Streaming
    12. Spark RDD Optimization Techniques
    13. Functional Programming in Spark
    14. Spark Execution Environment - SparkContext, SQLContext, SparkSession

Python & Intro to R Programming

  • Python & Intro to R Programming

    1. Getting Started with Python
    2. Installation of python/Jupyter Notebook/ SPYDER
    3. Importing and exporting data from python into various formats
    4. FunctionsUser defined functions, Parameters, Nested functions
    5. Local and Global variables, Alternate Keys, Sorting Lists and Dictionaries, Sorting Collections
    6. Errors in Python, Abnormal termination, Exception handling methods
    7. Ignoring Errors, Assertions and effective usage of assertions
    8. Plotting in Python, Creating Data Frames, Data Manipulation, Slicing and Dicing
    9. Why R and importance of R in Analytics?
    10. Installation of R and R-studio
    11. Data types, Variables, Operators, Decision making
    12. Arrays And Lists- Create Access the elements
    13. Data Frames- create and filter data frames,Building And Merging data frames.
    14. Functions And Importing data into R
    15. Graphics in R - Types of graphics

Machine Learning

    1. What is supervised learning
    2. Algorithms in Supervised learning
    3. Trees for Prediction (Linear)
    4. Trees for classification models
    5. Advantages of tree-based models?
    6. Building Decision Trees using R, Decision nodes and leaf nodes
    7. Linear Regression & Logistic Regression
    8. Decision Trees and Random Forest Test
    9. Boosting algorithms-Gradient Boosting, Adaptive boosting-Adaboost , Xgboost.
    10. How to measure the effectiveness of k-NN?
    11. Creating a Sigmoid Function from Linear Equation
    12. Naive Baye's classifier, Model Training, Validation and Testing
    13. Unsupervised Learning -Clustering
    14. Introduction to Deep Learning

Data Visualization with Tableau

    1. Introduction to Tableau & Installation of Tableau Public
    2. Design Flow, Data Viewing
    3. Connecting Tableau to various Data Files
    4. Measures and Dimensions
    5. Colors, Labeling and formatting, Exporting Worksheet
    6. Basics of TableauA-B Ad-hoc Testing, Aliases
    7. Reference Line, Anomaly detection
    8. Sorts and Filters,Time Series
    9. Chart plotting,Heat Maps
    10. Data Joining, Data Blending
    11. Advanced Concepts of TableauTrend Line Analysis
    12. Dashboard Creation
    13. Formatting in tableau
    14. Forecasting using Exponential Smoothing
    15. Granularity and Trimming, Seasonality, Animations

Data Mining & Data Visualization in R

  • Data Mining,Manipulation,Exploration & Visualization

    1. Data Mining, Text Mining, and Text analytic Process
    2. Sentiment Analysis, Statistical Analysis, and Cluster Analysis using R-Rattle
    3. Predictive Modeling using Decision Trees
    4. Supervised learning, Un- Supervised learning, Reinforcement learning
    5. Neural Network,Support Vector machine
    6. Evaluating & Deploying Models ,Evaluating the performance of Model on Validation data
    7. ROC, Sensitivity, Specificity, Lift charts, Error Matrix
    8. Deploying models using Score options
    9. Analytics in Excel, Data Preparation and Data Exploration in Excel, and Network Analysis using NodeXL
    10. Elements of Data Visualization, Data Visualization & Graphical functions in R
    11. Creating a bar chart, dot plot, scatter plot, pie chart
    12. Plotting with base graphics, Lattice graphics, and Other plotting functions
    13. Customizing Graphical parameters to improvise the plots
    14. Plotting with Plotting and coloring in R
    15. Infographics vs Data Visualization

Data Science training Certification Course Roadmap

Course Curriculum

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Data Science Corporate Training

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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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