R - Version 3.0.0

In the words of the R Project Contributors, “R is a free software environment for statistical computing and graphics”. This is a very neat one-line summary but only tells half the story. Since the birth of R in 1997 as an alternative implementation of the S language (originally developed at Bell Laboratories in the 1970s), R has grown to become one of the most widely used statistical programming languages in the World today. This growth has been centred around an open and active community who continue to contribute add on ‘packages’ of code, which put the latest algorithms straight into the hands of the user.

Many factors have contributed to R’s rapid growth. First, it is free, available under Free Software Foundation’s GNU General Public License. This has helped embed the language at academic institutions around the world and ensures that R is really a ‘take-home’ language. The wide uptake in academia means that today, researchers are using R to implement their latest ideas and algorithms. Another of R’s advantages over its competitors is its reporting capabilities. The ease at which high-quality graphics can be created and written to a multitude of devices has seen R set the standard for graphical reporting. Tools such as ‘Sweave’ and ‘knitr’ add to the reporting, negating the copy and paste activities and providing one-click reporting. Today R is thriving due to the relative ease at which it links with various data sources and other programming languages. Many business intelligence systems and statistical reporting platforms now offer R connectivity is part of their extended offering. This enables users of such systems to go beyond a the pre-defined set of algorithms and power up their statistical capabilities.

Quick facts about R

  1. R is the highest paid IT skill (Dice.com survey, January 2014)
  2. R most-used data science language after SQL (O'Reilly survey, January 2014)
  3. R is used by 70% of data miners (Rexer survey, October 2013)
  4. R is #15 of all programming languages (RedMonk language rankings, January 2014)
  5. R growing faster than any other data science language (KDNuggets survey, August 2013)
  6. R is the #1 Google Search for Advanced Analytics software (Google Trends, March 2014)
  7. R has more than 2 million users worldwide (Oracle estimate, February 2012)

Pls send us your query , will answer back within 24Hrs: Thanks in advance for contacting us

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Pre-requisites

Interest in statistical data analysis, basic statistical knowledge

Target Audience:

  • Biological research
  • Business Analytics
  • Business management
  • Data Analyst
  • Data Manager
  • Data miners
  • Data Scientist
  • Economics research
  • Engineering research
  • Mathematics research
  • Pharmaceutical research
  • Statisticians
  • The course is also targeted towards applied researchers, post-doctoral researchers and PhD students that would like to develop their skills in data analysis using R.

CURRICULUM

SECTION 1: GETTING STARTED

  1. Introduction to R
  2. Course Logistics

SECTION 2: YOUR FIRST R SESSION

  1. Finding your way around R - you will be introduced to the R software and will install and work with it for the first time. In this video, you will find instructions on downloading/installing R, as well as simple navigation, accessing help etc.
  2. Basic Commands - This lecture gets you started with using R by discussing basic commands such as assignment, case sensitivity, comments etc
  3. Operators - In this lecture, you will be introduced to operators in R - Arithmetic and Logical.
  4. Miscellaneous- This lecture deals with different items, including finding and removing objects , infinite, missing and indefinite values working with packages, and R preferences
  5. Intro to R Studio - This lecture provides you a brief intro of the R Studio IDE.

SECTION 3: BASICS - OBJECTS AND DATA TYPES

  1. Data Types - you will be introduced to Object/Data types in R. In this lecture, you will review different Data Types supported in R, including integer, double, complex, logical, date and character. You will also see about working with Data Types
  2. Object Types - In this lecture, you will be introduced to Object types in R, including vectors, arrays, matrices, factors, lists, data frames and tables. You will learn about attributes of an object - intrinsic and non-intrinsic. You will be introduced to the class of an object.
  3. Vectors - This lecture focusses on Vectors. You will learn to create a numeric vector, and perform arithmetic and mathematical operations. You will learn to replicate a vector, and create sequences. Finally you will see about logical and character
  4. Arrays and Matrices - This lecture focusses on Arrays and Matrices. You will learn about creation, subsections, and operations on Arrays and Matrices. You will review transposing. Then you will see some special operations that are matrix-specific.
  5. Factors and Lists - This lecture focusses on Factors, where you will learn about creation, levels and ordering a factor. It also focusses on Lists, where you will see about list values, names, and modifying a list.
  6. Data Frames and Tables - This lecture focusses on Data Frames where you will learn about creation, referencing, and working with data frames. It also deals with Tables, where you will see about creation, and the underlying tabulate() function.

SECTION 4: IMPORTING DATA INTO R

  1. Importing Text Files you will learn about Importing into R. In this lecture, you learn about importing text files as a data frame, and then as a vector.
  2. Spreadsheets - Excel Files - In this lecture, you will learn about importing data in an Excel file into R as a data frame. As part of the exercise, an Excel file "phones.xls" has been provided to you.

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SECTION 5: DATA MINING/MANIPULATION

  1. Vector Operations- it covers data mining/manipulation by object Type. In the first lecture, you will start with Vectors - subscripts, ordering, statistics, applying functions, subdivision and sampling.
  2. Array Operations- This lecture focusses on Array Operations: subscripts, Outer Product, and applying functions.
  3. Matrix Operations - This lecture deals with Matrix Operations: Subscripts, diagonal matrix, Matrix multiplication, cross product, inverse of a matrix, solving linear equations, and least squares regression.
  4. Data Frame Operations - In this lecture, you will learn about Data Frame Operations: accessing a subset, adding rows/columns, combining data frames, obtaining summaries, and modifying the data frame.
  5. Factor Operations - This lecture introduces you to Factor Operations: summarizing data at different levels of a factor, creating a factor out of numeric data, generating a factor out of patterns, re-ordering levels based on data, and unclass().
  6. Operations on Text - This lecture deals with Text Operations: length, parts of a string, Concatenation, Pattern recognition/replacement.
  7. Operations on Dates - This lecture deals with Operations on Dates: creation, formatting, arithmetic, System date/time, POSIX time, and some built in date constants

SECTION 6: LOOPS AND CONDITIONS

  1. Loops and Conditions - This Section introduces Loops and Conditions, where you will learn about conditional statements: If-then, While and Repeat. You will also see about the For Loop. The contents of this section apply in Section 10 where you will learn about Functions.

SECTION 7: STATISTICS

  1. Descriptive Statistics- this deals with the use of R in Statistics, where you will see about using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, Non-Linear Regression, and Trees. In this lecture, you will focus on Descriptive Statistics, including Mean, Quantile, MAD, Variance, Range, Covariance and Correlation.
  2. Probability Distributions - In this lecture, you will focus on Probability Distributions, where you will learn about working with the PDF, CDF, quantile function and generating a random sample, for a variety of Probability Distributions including Normal, T, Binomial, Uniform, Exponential, etc
  3. Hypothesis Testing - One and Two Sample T-tests - In this lecture, you will focus in on Hypothesis Testing: One and Two Sample T-tests, where you will learn about One and Two Sided Tests.
  4. Hypothesis Testing - KS-test and F-test - In this lecture, you will focus on the KS-test to determine whether two samples are statistically similar. You will also learn about the F-test that tests two samples based on their variance.
  5. Linear Modeling - Working with Formula Objects - This lecture deals with the creation of Formula Objects to be used in R linear models. It discusses the use of operators in a Formula and how they are different from their typical mathematical meaning.
  6. Linear Modeling - Generating a Linear Model- In this lecture, you will learn how to use a formula object, and a data set to generate a linear model. Then you will see about mining the model, getting information out of it, and performing an ANOVA.
  7. Linear Modeling - Updating a Linear Model - In this lecture, you will learn about updating a Linear Model: simulating the addition and deletion of model terms. You will also see about making a permanent change to the Model Formula.
  8. Generalized Linear Models- In this lecture, you will review Generalized Linear Models, in situations where the response variable has a non-Normal Probability Distribution. You will learn about generating the model, mining it for information, and performing an ANOVA. The lecture will also show you how to generate a Logistic Regression model of Low Birth Weight data. The exercise that follows is based on the same Low Birth Weight data set;
  9. Non-Linear Regression- In this lecture, you will learn about using R for Non-Linear Regression. You will see the creation of Formula Objects, as well as Model Generation. You will review how to mine the model.
  10. Tree Models - In this Lecture, you will learn how to generate a Tree Model out of data that has discrete classes in it. You will review how to fine-tune and control the tree structure.

SECTION 8: GRAPHICS

  1. Univariate Plots - I: - This deals in Graphics, where you will learn to create 2-dimensional Univariate and Multi-variate plots. You will also learn about formatting various parts of a plot, covering a range of topics like Plot Layout, Region, Points, Lines, Axes, Text, Color and so on. In this lecture, you will see about using function plot() to plot a vector, time-series object and a bar-plot for factor data. You will also see about using function barplot() to generate a bar plot.
  2. Univariate Plots - II - In this lecture, you will review more Univariate plots: Piecharts, Histograms, Boxplots, Quantile-Quantile plots and the Kernel Density Estimation Plots.
  3. Multivariate Plots - I - The first lecture on Multi-variate plots deals mainly with Scatterplots. You will first learn to use function plot() to generate a scatter plot of two variables. Then you will use plot() to generate a matrix of scatterplots in the same figure - 3 different flavors. Next, you will see about using function pairs() to generate a matrix of scatterplots.
  4. In this lecture, in addition, you will use plot() to generate multiple boxplots in the same figure. You will also learn about qqplot() to generate a quantile-quantile plot of two sample quantiles.
  5. Multivariate Plots - II - In the second lecture on Multivariate Plots, you will learn to generate a Coplot, a Stars/Segment diagram, and a Cleveland Dot Plot.
  6. Formatting a Plot - Points - The remaining lectures in Section 8 focus on formatting a plot. You will start in this lecture with Points: using function par() to change the point type, adding Points, and identifying and labeling points on a plot through user input.
  7. Formatting a Plot - Lines - In this Lecture, you will learn to format Lines in a Plot - Line type, Line width. You will then learn to add lines through existing points, draw lines through the Plot, and finally to add segments/arrows to a Plot.
  8. Formatting a Plot - Regions and Layout - In this Lecture, you will learn to format the plot region if it is a single Plot, and the Plot layout if it is a grid of plots. You will learn concepts such as Device Region, Figure Region, Plot Region, and Margins. The exercise in this lecture is a repeat of what you see in the concept video and as a result, there is no exercise video.
  9. Formatting a Plot - Axes - In this Lecture, you will learn to format the axes of a plot, including box type, axis scale, and axis display. You will see about adding an axis to a plot.
  10. Formatting a Plot - Text - In this Lecture, you will see about formatting Text on a plot: Titles, Adding text, Margin Text, Text Position, Annotation, Text size, and Font/Style.
  11. Formatting a Plot - Color - In this Lecture, you will learn about working with Plot Color: use of constants such as color(), palette() and adding contiguous colors from the spectrum. You will see about adding color to text, foreground and background of a plot.
  12. Miscellaneous -Section 8 comes to a close with a discussion on miscellaneous items such as global vs. local changes to plot parameters. You will also learn how to add a polygon and shapes - circles, squares, rectangles etc - to a plot.

SECTION 9: EXPORTING DATA OUT OF R

  1. Text files - Section 9 deals with Exporting out of R - Text and Graphics. In this lecture, you will learn about exporting a vector. You will also see about exporting a data frame into a text file.
  2. Graphics - In this Lecture, you will learn about exporting Graphics: as a jpeg, and then as a pdf file.

SECTION 10: WORKING WITH FUNCTIONS

  1. Creating Functions - Section 10 deals with creating and working with User Defined Functions. The concepts on Loops and Conditions from Section 6 apply here. In this Lecture, you will learn about creating a function and adding comments.
  2. Arguments of a Function -This Lecture deals with Arguments of a Function: Optional arguments, the … argument, Local vs. global, lexical scope, Function as an argument, and Lazy evaluation.
  3. Others -This Lecture deals with Function concept such as multiple outputs, nesting functions and loops/conditions.

SECTION 11: PREDICTIVE MODELING

  1. Hypothesis Testing in R
  2. Linear Regression
  3. Logistic Regression
  4. Cluster Analysis Decision Trees
  5. Regression models using GUI Rattle,
  6. R Commander,
  7. car,
  8. gvlma,
  9. ROCR packages,
  10. p values,
  11. parameter estimates,
  12. Confusion matrix,
  13. Sensitivity,
  14. Specificity,
  15. Information Complexity,
  16. MultiCollinearity,
  17. Heteroscedasticity,
  18. Model Output,
  19. Lift Charts,
  20. Model Curves.

SECTION 12: OTHER TOPICS

  1. Forecasting,
  2. web analytics,
  3. social media analytics,
  4. text mining using various packages in R,
  5. epack plugin in R Commander,
  6. tm package,
  7. wordcloud package
  8. twitteR package,
  9. introduction to social network analysis and social media analytics.

SECTION 13: OTHER COMMANDS

  1. Some MORE comments
  2. Congratulations! You have now finished this course! 
  3. A couple of callouts
  4. Use typeof(), mode(), class() and unclass() 
  5. Practice using R real world data sets

Registration Details

Course Fee:
Single Nomination:
USD 599/- OR INR 22000/-

Avail Special Discounts Avail Special Discounts Avail Special Discounts Avail Special Discounts
5% Discount for Early Bird Registrations (15 Days in advance to the program date) 5% Discount on Task force of 4 to 7 10% Discount on task Force of 8 and above 10% discount applicable to BA/PMP/CSBA/IREB/CSTE / CSQA/CISSP/CFPS/CSPM/CAPM /CISA/ Qualified Professionals, IIBA/PMI /SEG /CII/SPIN /CSI and NASSCOM Members

NOTE: Only one discount option is applicable at any time

India Course Dates, Venue & Timings:

Sl.No. State City Batch1-Date Batch1-Date Batch2-Date Batch2-Date Batch3-Date Batch3-Date Batch4-Date Batch4-Date Venue Contact
1 Andhra Pradesh Vijayawada 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Vijayawada- Spectramind , DBS center Vijay
2 Andhra Pradesh Vizac/ Vishakhapatnam 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Vizac- SpectraMind , Naga Chambers, Level 3 & 4, D/No. 12-1-16 Plot No. 49, Survey No 1051, Opposite HDFC bank, Waltair Main Road, Visakhapatanam, 530002 Vijay
3 Assam Guwahati 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
4 Assam Dibrugarh 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
5 Bihar Patna 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 DBS Center, Patna Jason
6 Calicut Kozhikode 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
7 Chhattisgarh Raipur 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 DBS center ,Raipur Jason
8 Delhi Delhi 23-Jan’16 24-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND,Paharpur Business Centre, 21, Nehru Place Greens, New Delhi - 110019 Rama Gopal :
9 Goa Goa 7-Dec’15 14-Dec’15 21-Dec’15 28-Dec’15 1-Jan’16 5-Jan’16 1-Feb’16 5-Feb’16 SPECTRAMIND Kaushik
10 Gujarat Ahmedabad 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Ahmedabad - SpectraMind, 101 – 104, GCP Business Centre, Opp. Memnagar Fire Station, Vijay Cross Road, Memnagar, Ahmedabad, 380014 Mr.Alok
11 Gujarat Vadodara 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Ahmedabad - SpectraMind, 101 – 104, GCP Business Centre, Opp. Memnagar Fire Station, Vijay Cross Road, Memnagar, Ahmedabad, 380014 Mr.Alok
12 Haryana Gurgaon 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Gurgaon - SpectraMind, Level 9, Spaze i-Tech Park, A1 Tower, Sector - 49, Sohna Road, Gurgaon, 122018 Rama Gopal :
13 Haryana Chandigarh 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Chandigarh - SpectraMind, Level 4, Tower-A, Godrej Eternia, plot number 70, Industrial Area 1, Haryana, Chandigarh Kavita
14 Jammu and Kashmir Jammu 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
15 Jammu and Kashmir Srinagar 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
16 Jharkhand Ranchi 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
17 Karnataka Bangalore 16-Jan’16 17-Jan’16 ** 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND,DBS center , Cunningham road , Bangalore Namratha
18 Karnataka Mysore 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 DBS center , Mysore Namratha
19 Karnataka Hubli 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 DBS center , Hubli Namratha
20 Kerala kochi - - - - 2-Jan’16 3-Jan’16 6-Feb’16 7-Feb’16 SPECTRAMIND,ThomasMount ,ICTA Building,Changampuzha Nagar P.O.,Cochin- 682033 Mr.Manoj:
21 Kerala Trivandrum 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Hotel Classic Avenue, Thampanoor, Trivandrum, Kerala. Mr.Manoj
22 Madhya Pradesh Indore 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Indore – SpectraMind , DNR 90, Unit Nos. 301, 3rd floor, 569/3, MG Road, Indore, 452003 Arun
23 Maharshtra Nagpur 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
24 Maharashtra Mumbai **23-Jan’16 24-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND,DBS Heritage,Prescot Road,Opp. Cathedral Sr. School,Fort, Mumbai 400001. DBS Heritage (From Airport instruct the car / cab driver to drive to Fort, FashionStreet. It’s near Siddharth College, Budha Bhavan. Also there are schools like J. P. Pettit School & Cathedral Sr. School Mr.Vasudev
25 Maharashtra Pune 30-Jan’16 31-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND,Panchasheel tech park,Yerwada, Pune Mr.Manish
26 Manipur Imphal 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
27 Nagaland Dimapur 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
28 Orissa Bhubaneshwar 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Vani Vihar, Bhubaneshwar Mr. Satya Deep
29 Rajasthan Jaipur 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 DBS center,Jaipur Mr.Manish
30 Rajasthan Udaipur 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
31 Tamilnadu Chennai 9-Jan’16 10-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND,CHENNAI, CitiCentre , Level 6, 10/11 Dr.Radhakrishna Salai,Chennai,Tamil Nadu,600 004,India Mr.Balaji
32 Tamilnadu Coimbatore 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Coimbatore , SpectraMind ‘Srivari Srimath”, 3rd floor, Door No.1045,Avinashi Road, Coimbatore, 641 018 Mr.Balaji
33 Telangana Hyderabad 4-Jan’16 10-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND , Flat 617, 6th Floor ,Annapurna block, Aditya enclave, Ameerpet, Hyderabad-500016 Jason
34 Uttar Pradesh Varanasi 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik
35 Uttar Pradesh Noida 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Noida - SpectraMind, Tapasya Corp Heights, Ground Floor, Sector 126, Uttar Pradesh, Noida Rama Gopal :
36 Uttar Pradesh Lucknow 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 Lucknow – SpectraMind , 4th Floor, Halwasiya Court, Hazratganj, Uttar Pradesh, Lucknow, 226001 Mr.Sandeep
37 Westbengal Kolkata 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SpectraMind , The Legacy, 1st Floor, 25-A, Shakespeare Sarani, Kolkata, 700017 Mr.Hamid :
38 West Bengal Bagdogra 16-Jan’16 17-Jan’16 6-Feb’16 7-Feb’16 5-Mar’16 6-Mar’16 2-Apr’16 3-Apr’16 SPECTRAMIND Kaushik

Duration : 40Hrs [2 Days (20hrs) in-person training and rest( 20hrs )will be instructor led online]

Training take away : 100+ Exercises and 10+ Case Studies

Pls send us your query , will answer back within 24Hrs: Thanks in advance for contacting us

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Few FAQ's are discussed below,

What are some of the job profiles at the entry level in Analytics?
Business Analytics, Data Analyst, Data Manager,Data Scientist
Who are some of the major employers in Analytics?
There are mainly FOUR types of employers:
a. Large IT companies who have an Analytics practice;
b. Analytics KPOs;
c. In-House Analytics Units of large corporates; and
d. Nice Analytics firms.
Which are the some of the big Analytics companies with operations in India?
The Following is only a representative list:
a. IT - Infosys ,TCS,Wipro;
b. KPO - Genpact, WNS;
c. Citibank, Dell, HP, Spencers, Target, Walmart, Amazon, Facebook, Google; and
c. Niche: Brainmatics, Fractal Analytics, Mu Sigma.
What are the average fresher starting salaries in this field?
Between Rs 4 and 6 lakh per annum.
What is the future scope in this domain?
This is one of the most exciting fields. The Harvard Business Review named it as one of the 'sexiest jobs of the 21st century.' NASSCOM has estimated that from 50,000 today, the demand for Analytics professionals in India will grow to 2,50,000.
Which industry can one be a part of after doing this Analytics Edge program?
Analytics is fast emerging as an industry in its own right. However, analytics jobs and careers exist in most large companies in almost every industry vertical - banks, retail, online businesses, manufacturing, telecom, healthcare, you-name-it.
Can and should professionals with experience in some other fields switch to Analytics Edge?
This is one of those fields that people are switching to even very late in their careers because it is exciting and extremely well-paid with exceptionally bright growth prospects. The data revolution is just beginning!

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