Data Science using R 5/5 (6)

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Machine Learning using R training program is designed by the highly professional industry experts. It includes certain analytical skill set which exactly fulfil the requirement of the industry. There are number of job opportunities with this skill set.
A complete skill set with an exposure to get hands on the industry’s most usable analytical tools.

    24 Sessions
    192 Hours

    Skills you will master

    R Programming
    Visualisation in R
    Regression Modeling
    Classification Modeling
    Supervised & Unsupervised Learning
    Machine Learning Algorithms
    Excel Dashboards
    Visualisation in Tableau


    10:00 AM - 01:00 PM
    04:00 PM - 07:00 PM
    04:00 PM - 07:00 PM
    Mob : +91-9810600764
    Address : M8, Lower ground Floor, Sector 14 OLD DLF Gurgaon 122001
    Email : info@palin.co.in


    About The Program

    Data Science With R Course

    Our Program is designed by the industry experts. These skills fulfil the requirements of jobs related to data. Now days there are huge requirements of  analytics skills with R with SQL and tableau as dashboarding tool in the industry. This program will cover Regression (Linear & Logistic), Hypothesis Testing, ANOVA, Cluster Analysis, Customer Segmentation, Time Series Analysis and Machine Learning overview (Random Forest, Decision Tree), SQL queries for data manipulation and dynamic dashboard creation and reporting on tableau.Training includes Industry specific data as well as assignments, case study, project.

    During the training participants will get hands on



    and will be going through



    After the completion of the course you will be able to crack the interviews with most of the renowned organizations like Accenture, IBM, WNS, EXL, Bank of America, American Express, Fractal Analytics, British Telecom, Cognizant and many starts up companies.


    Please rate this

    Career Advisor
    Vinit Kumar, Consultant
    Palin Delivers real world relevance with activities and assignments that helps students build critical thinking and analytic skills that will transfer to other courses and professional life.
    Program Description
    Data Science with R includes Data Analytics in Excel, Excel Dashboards and Advanced Charts, Fundamental of SQL, SQL joins, Clauses, functions, fundamental of R, Programming with R, Data Manipulation, Importing and Exploration Techniques using R, Visualisation in R, Statistics Essentials, Regression Modeling & Classification Modeling, Supervised & Unsupervised Learning, Machine Learning Algorithms, Visualisation in Tableau, Advanced charts and dashboards in Tableau amd a Real Time Project to get hands on the tools with a specified domain expertise which leads you to become a data scientist.
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       Session 1 - Introduction to Data Science

    - What is Data Analytics & Data Science
    - Different types of Data Analytics (Descriptive, Predictive, Prescriptive)
    - What is Artificial Intelligence
    - What is IOT
    - Machine Learning (Supervised & Unsupervised Learning)
    - Deep Learning (Artificial Neural Networks, CNN)
    - Overview of Banking, Healthcare, Telecom domain
    - Working with multiple data sources – RDBMS (SQL Server, Oracle, My SQL, DB2), NOSQL (MongoDB, Cassandra, CouchDB)
    - Real world Applications of Machine Learning & Deep Learning
    - What to expect from this course (Salary, Market trends, job roles, Domain)

       Session 2 - Introduction to Data Analytics using Excel

    -Excel Tables
     Sparklines
     Conditional Formatting(Functions: Logical, Text, Aggregate)
     Excel Data Tables
     Introduction
     Slicers to filter your table data
    -Pivot Tables
     Creating Pivot Tables
     Use multiple ways to filter data in Pivot Tables
     Slicers in Pivot Tables
    -Power Pivot
     Introduction to Power Pivot and it’s uses
     Pivot Tables v/s Power Pivot Tables
     Creating Power Pivot Tables
     Tables & Charts Combination
     Power Pivot Hierarchies
     Power Pivot using Slicers

       Session 3 - Dashboards - Excel

    -Flexible data aggregation using pivot tables and pivot charts to create Dashboards
     Introduction to Dashboards
     Types of Dashboards
     Analytical Dashboards
     Dynamic Dashboards with Live Data
     Excel Charts for Dashboards
    -(Pie Chart, Stacked Bar Chart, Scatter Chart, Bubble Chart, Stock Chart, Radar Chart)
    -Data Filter in Pivot to get Subsets of data & Advance Charts
     Dashboard Data & Formats
     Waterfall Chart
     Band Chart
     Gantt chart
     Thermometer Chart
     Histogram
     Pareto Chart
     Funnel Chart
     Box and Whisker Chart
     Waffle Chart
    -Reporting Layouts using formulas to aggregate the data as an alternative to Pivot Tables
     Power view visualisation
     Combination of power view visualisation
     Interactive Nature of Charts in Power View Visualizations
     Slicers & Tiles in Power View

       Session 4 - Data Analysis & Data Visualisation using Excel

    -Data Gathering & Transformation from multiple sources
     Combining Multiple worksheet (Vlookup/Hlookup/Index/Match)
     Get and Transform data
     Concatenate Formatting
    -Discover and combine data in mashups
     Append Data
     Merging Data
     Data Blending
    -Data model creation
    -Data Exploration, Analysis and Visualisation

       Session 5 - Introductionto SQL

    -What is RDBMS?
    -What is SQL?
     What is SQL
     SQL Process
     SQL Architecture
    -Why SQL?
    -SQL Commands
     Data Definition Language
     Data Manipulation Language
     Data Control Language
     Data Query Language
    -Fundamental of SQL
    -SQL Syntax
     Select Statement
     Distinct Clause
     Where Clause
     AND/OR Clause
     IN Clause
     Between Clause
     Like Clause
     Order by Clause
     Group by Clause
     Count Clause
     Having Clause
     Create Index Statement
     Drop Table Statement
     Drop Index Statement
     Truncate Table
     Alter Table
     Update Statement
     Delete Statement
     Commit Statement
     Roll back Statement
    -SQL Data Types
     Exact Numeric Data Types
     Approximate Numeric Data Types
     Date and Time Data Types
     Character Strings Data Types

       Session 6 - SQL Continue

    -SQL Operators
     Arithmetic Operators
     Comparison Operators
     Logical Operators
    -SQL Sorting
    -SQL Joins
     Relationships in SQL
     Understanding Joins
     Types of Joins
    • Inner Join
    • Outer Join
    • Right Join
    • Left Join
    • Full Join
    • Self Join
    • Cartesian Join
     Concept of Keys
     Brief about Function
    -SQL Union Clause  The Union All Clause
    -SQL Alias Syntax -SQL Truncate Table -SQL Using Views

       Session 7 - Practical & Doubt Session

     Performing dataset with MYSQL

       Session 8 - Introduction to R Pragramming

    -R Programming
     Introduction to R Programming
     Data Types in R
     Functions in R
     Summarizing data by using various functions
     Indulge into a class activity to summarize the data
     Various subsetting methods

       Session 9 - Data Importing and Data Manipulation in R

    -Data Importing
     Data import technique in R
     Import data from spreadsheets and text files into R
     Install packages used for data import
     Connect to RDBMS from R using ODBC and basic SQL queries in R
     Perform basic web scrapping
    -Data Manipulation in R
     Know the various steps involved in data cleaning
     Functions used for data inspection
     Tacking the problem faced during data cleaning
     How and when to use functions like grep, grepl, sub, gsub,regexpr, gregexpr, strsplit
     How to coerce the data

       Session 10 - Data Exploration in R

    -Data Exploration in R
     What is data exploration
     Data exploring using Summary(), mean(), var(), sd(), unique()
     Using Hmisc package and using summarize, aggregatefunction
     Learning correlation and cor() function and visualizing the same using corrgram
     Visualizing data using plot and its different flavours
     Boxplots
     Dist function

       Session 11 - Data Visualization in R

    -Data Visualisation in R
     Gain understanding on data visualization
     Learn the various graphical functions present in R
     Plot various graph like tableplot, histogram, boxplot etc
     Customize graphical parameters to improvise the plots
     Understand GUIs like Deducer and R commander
     Introduction to spatial analysis

       Session 12 - Statistics Essentials

    -Fundamentals of Statistics
     Basic statistics; descriptive and summary
     Inferential statistics
     Statistical tests
    -Data Prep and Reduction techniques
     Need for data preparation
     Outlier treatment
     Flat-liners treatment
     Missing values treatment
     Factor Analysis

       Session 13 - Introduction to Analytics

    -Basic Analytics
     Statistics Basics Introduction to Data Analytics and Statistical Techniques
     Types of Variables, measures of central tendency and dispersion
     Variable Distributions and Probability Distributions
     Normal Distribution and Properties
     Central Limit Theorem and Application
     Hypothesis Testing Null/Alternative Hypothesis formulation
     One Sample, two sample (Paired and Independent) T/Z Test
     P Value Interpretation
     Analysis of Variance (ANOVA)
     Chi Square Test
     Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)
     Correlation
    -Introduction to Business Analytics
     Relevance in industry and need of the hour
     Types of analytics – Marketing, Risk, Operations etc
     Future of analytics and critical requirement

       Session 14 - Modeling Techniques

    -Basics of regression analysis
     Linear regression
    • Simple Linear Regression
    • Multiple Regression
     Logistic regression
    -Interpretation of results
    -Multivariate Regression modeling

       Session 15 - Machine Learning Algorithms

    -Classification Modeling
     K-Nearest Neighbours (K-NN)
     Support Vector Machines
     Decision Tree Classification
    • Understand what is Decision Tree
    • Algorithms for Decision Tree
    • Greedy approach: Entropy and information gain
    • A perfect decision tree
     Random Forest Classification
    • Understand the concept of random forest
    • How random forest work
    • Features of random forest

       Session 16 - Predictive Modeling & Forecasting

    -Time series modeling
    -Cross-sell and Up-sell opportunities and modeling
    -Churn prediction models and management

       Session 17 -Unsupervised Learning

    -Basics clustering
    -Deciles analysis
    -K-Means Clustering
    -Hierarchical Clustering
    -Cluster evaluation and profiling
    -Interpretation of results

       Session 18 - Introduction to Tableau

    -Introduction to Data Visualization with Tableau
    -Data Import and Management
    -Data Type and Operation
    -Visualizations Deep Dive
    -Data Organization and Scripting
    -Playing with Time Dimension
    -What is Your Location?
    -Incremental Loading and Blending
    -The World is Your Visualization
    -Statistical Analysis with Tableau & R
    -Sharing Insights with Enterprise Dashboards
    -Analyse Project data and extract meaningful information from it
    -Create Dashboards and Stories from data sets

       Session 19-20 - Performing Reports on Dataset (LIVE)

    -Project: A dashboard should be created that would contain
     A list of Orders returned by the Customers (in terms of refund)
     Top 10 Countries mapped on the World Map that had most of the refunds
     Predict the refund for next 1 year category-wise
     Actions to be Performed to Create the Dashboard
     Create hierarchies and folders in the dataset provided
     Generate a list of order returned from customers and compare it to the original sales, sort the visualization in ascending
     Order in terms of returned order, for top 10 countries in terms of refunds
     Map top 10 countries, with most of the refunds on the world map
     Predict the returned sale with lowest and actual forecast of the data
     Add a URL action to represent details of the countries
     Finally publish your work to Tableau Server/ Online

       Session 21-24 - Domain Specified Live Project

    -Live Project to get hands on tools with one of these specified domains
     Specialization in Banking
     Specialization in Healthcare
     Specialization in Telecom
     Specialization in Insurance

    Read More
    Program Highlights
    Instructor led training is the live and interactive training by the industry experts
    Live Case Study
    Helps you work on industry specific data so as to get an hands on experience.
    Assignments which helps in conceptual understanding, Prepared for interview training & techniques.
    Lifetime Access
    Access to all the recording for the lifetime and avoid notes maintaining chios.
    24 X 7 Support
    Consultants are available online for proper guidance about the course.
    After successful completion of the training and case study Palin will certify you as Palin certified Data Analyst.
    Career Counselor
    Avail career guidance and Professional guidance for resume building, unlimited opportunities and interviews.

    Highly motivated with leadership skills having Master’s Degree in Statistics. Holds good working experience in different domains like Retail, Banking, Healthcare FMCG, Marketing analytics using SAS, R, Python, STATA.
       Who can go for Data Science using R?
    Any graduate who’s good at maths or stats and willing to work on Advance Modeling with R as Data Analyst, Business Analyst etc can go for Data Science using R training.
       What is Data Science using R?
    Advance modeling techniques in R like Linear & Logistic regression, classification algorithms, clustering are used to analyse large data and helps bringing inference and decision making for different business needs.
       What is the future of Data Science using R?
    When it comes to data science, R is a great language to master. First of all, it’s a language friendly to beginners — there are certain features in the language that make it easy to get started and develop prototypes quickly.
       Pre-requisites for Data Science using R?
    Candidates who are good at maths or stats is an advantage for them. Any Graduate/diploma candidate can apply for this training and get mentored by the experts of the industry.
       Is this a classroom training or online?
    Palin Analytics offers you both the training modes. You can opt either classroom or online.
       Which one is better Online or Classroom?
    Both the training modes are good to get trained. But online training is more better than the classroom training because online trainings are live interactive where you can raise your concerns at any point during the session. Additionally recording of the same session will be provided which you can access anytime anywhere.
       What all topics will be covered in Data Science using R?
    R Analytics includes Basic Fundamental of R, Data Manipulation in R, Data import techniques in R, Data Exploration, Data Visualisation in R, Data Mining, Linear & Logistic Regression, ANOVA, Z-Test, Hypothesis Testing, Cluster Analysis, Customer Segmentation, Time Series Analysis, Decision Tree and Random Forest. Real Cases to get hands on working experience on R with Advance Modeling, Reporting and dashboards in Tableau and SQL
       After the course for which companies I can apply for?
    Almost every organisation which has a large customer base is shifting to R Analytics. Companies like Amazon, Infosys, TCS, Accenture, IBM, Flipkart, Airtel are highly in need for analytics professionals.
       What are pay packages for fresher in Data Science using R?
    An Entry-Level Data Scientist, IT earns an average salary of Rs 579,714 per year. The highest paying skills associated with this job are Machine Learning, R, and SQL.
       What is the process for Data Science using R?
    We'll be starting from Fundamental of R to understand working with files and databases. Then we'll cover advance statistical techniques to get hands on business analytics with regression modeling, classification & unsupervised learning and then we'll cover reporting and dashboarding using tableau and SQL.


      Thank you so much guys for your support in both training and placement. You guys have the best trainers who provides the industry exposure to the freshers that helps candidate to get placed.
      Thank You Palin Analytics !!

      I’ve completed R training in palin. I would recommend you palin because of their trainers, placement assistance and training quality of course.

      I was working in a BPO and then i came to know about palin analytics through a friend of mine and their counselors guided me towards analytics and its scope in the industry.now I can proudly say that I took a good decision by joining data science course by palin analytics as they helped me getting placed and provide industry specific training as well.thank you palin for giving me a right path.

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