Analytics (R) Specialist


Description
Course Structure
Pre-requisites
Certification
FAQs
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Course Objectives

R is today the most sought after statistical package for analytics professionals. R is the most comprehensive programming language for analytics solution building. It has packages for almost all algorithms and can be used across industries. It has the best visualization library and best of all it is free.

This program focuses on 4 aspects to help you become a complete analytics professional -
(A) Statistics foundation to help you appreciate data science algorithms
(B) Comprehensive training on R programming with focus on analytics solution building
(C) Training on Data Science algorithms with real life data and solution building using R
(D) Training on Machine Learning algorithms with real life data and solution building using R

As part of the program you will work on real-life case studies on analytics solution building. You will work on multiple assignments, case studies and practice exercises to hone your skills.

The program includes bonus sessions on text minning, social media analysis & sentiment analysis

At the end of the course you will be able to build analytics applications using R

Course duration: 284 hours
Mode: Classroom / Online Instructor Led through Virtual classroom

Key Features

  • 84 hours of instructor led live training on weekends
  • Hand-on practice on 18 real life case studies
  • Access to LEAP - our analytics learning platform
  • Personal attention from faculty
  • Performance evaluation
  • Placement assistance
  • 200 hours of self learning
  • Practice exercises and assignments to enhance skills
  • Faculties from IIT/IIMs with rich industry experience
  • Full access to video lectures for self paced learning
  • 100% moneyback guarantee
  • Internship opportunity to work on dashboarding & insight generation projects
Data exploration
Introduction to type of data variables, data summarization techniques, building a data dictionary, univariate analysis, bivariate analysis, outlier treatment, missing value treatment.
Hours = 2
Case Study = Building a data dictionary and exploratory data analysis report for a client after you recieved client data.
Assignment = Generate a exploratory analysis report
Tool = Excel
Descriptive statistics
Measures of central tendency. Measures of dispersion. Range. Skewness. Interpretation of histograms. Research methodologies.
Hours = 2
Case Study = None
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = Excel
Inferential statistics
Basics of probability theory. Bayes Theorem. Probability distribution functions - Uniform, Bernoulli, Binomial, Normal, Log Normal, T. Continous probability distributions. Hypothesis testing - 1 Sided tests, 2-Sided tests. F test. T test. Chi Sq Test. ANOVA.
Hours = 3
Case Study = Application of inferential tests
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = Excel
R fundamentals
R overview. Installation. Packages & walkthrough. Data structures (Vector, array,factors, data frames, lists). Vector calculation. Arithmetic & logical operators. Subsetting. Missing, indefinite & infinite values.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Control flow basics
For loops. While loops. Nested loops. Disadvantage of using loops. Alternates to loops.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Functions
Understand the structure of function. Build your own function. Usage of parameters and default values. Usage of return.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Packages
How to search & choose a new package. Package installation & updates. Help and learn. Access package functions. Hack a function. Build your own package.
Hours = 2
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Environment objects
Save, load & delete objects.
Hours = 1
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data import & export
Import & export from Excel. Import & export from MySQL. Import & export from text file. Export to image & PDF. Present output in HTML webpage.
Hours = 3
Case Study = Case study on importing data from excel, formatting it in R using automated code and presenting insights from it in a web page
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data manipulation basics
Sort & rank. Data Aggregation. Merging.
Hours = 3
Case Study = Case study on data manipulation
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data manipulation advanced
Apply, Lapply, Tapply, By, Replicate functions. Dplyr. Tidyr.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data Visualization fundamentals
Plot function. Changing parameters. Drawing basic charts. Adding chart elements.
Hours = 3
Case Study = Case study on plotting of stock market data
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data Visualization advanced
Qplot, Ggplot, Maps..
Hours = 3
Case Study = Case study on US airport data based map visualization
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Linear Regression
Introduction to linear regression technique & its uses. Details of ordinary least squares estimation technique. Modeling steps. Variable handling. Model statistics interpretation. Validation of linear regression assumptions. Metrics to measure model performance.
Hours = 7
Case Study = Case study on predicting house prices using real data.
Assignment = Case study on insurance claims using real data.
Tool = R
Logistic Regression
Introduction to logistic regression technique & its uses. Maximum likelihood estimation technique. Modeling steps. Dependant variable definition. Variable handling. Weight of Evidence & Information Value. Variable reduction. Model statistics intepretation. Metrics to measure model performance.
Hours = 6
Case Study = Case study on predicting churn for a large telecom operator using real data.
Assignment = Case study on predicting customer cross sell for a large retailer using real data.
Tool = R
Time series forecasting
Learn basic concepts of time series modeling. Basic techniques for forecasting. Smoothing techniques. Decomposition. Understanding the fundamentals of ARIMA. ARIMA modeling, model estimation & interpretation. Forecasting with regression and time series data. ARIMAX or dynamic regression models to build forecasting models with multiple regressors.
Hours = 6
Case Study = Case study on predicting sales for a large european retailer using real data.
Assignment = Case study on predicting call volumes for a call centre.
Tool = R
Clustering
Introduction to clustering. Types of clustering & their uses. K-Means clustering. Hierarchical clustering.
Hours = 3
Case Study = Case study on retail customer segmentation using K Means clustering techniques on real data.
Assignment = Case study on product categorization using hierarchical clustering on real data.
Tool = R
Introduction to machine learning
How do machines learn? Choosing a machine learning algorithm. Using R for machine learning.
Hours = 1
Case Study = None
Assignment = None
Tool = R
Classification using Nearest Neighbors
Understanding classification using nearest neighbors. The kNN algorithm - Calculating distance, Choosing an appropriate k, Preparing data for use with kNN.
Hours = 3
Case Study = Case study on diagnosing breast cancer using kNN algorithm.
Assignment = Case study on classifying the IRIS dataset using KNN
Tool = R
Classification using Naive Bayes
Understanding Naïve Bayes - basic concepts & algorithm.
Hours = 3
Case Study = Case study on filtering mobile phone spam with the naive Bayes algorithm.
Assignment = Case study on using Naïve Bayes Classifier to predict cancer
Tool = R
Classification using decision trees and rules
Understanding decision trees. C5.0 decision tree algorithm. Understanding classification rules.
Hours = 3
Case Study = Case study on identifying risky bank loand using C5.0 decision trees.
Assignment = Case study on identifying poisonous mushrooms with rule learners.
Tool = R
Neural Networks
Understanding neural networks. Activation functions. Network topology. Training neural networks with backpropagation.
Hours = 3
Case Study = Case study on modeling the strength of concrete with neural network.
Assignment = Case study on fitting a neural network model to predict the median value of owner-occupied homes (medv) using all the other continuous variables available.
Tool = R
Support Vector Machines
Understanding SVM. Classification with hyperplanes. Finding the maximum margin. Using kernels for non linear spaces.
Hours = 3
Case Study = Case study on optical character recognition using SVM.
Assignment = Case study on classifying telecom churn using SVM
Tool = R
Market basket analysis
Understanding association rules. Apriori algorithm.
Hours = 3
Case Study = Case study on frequently purchased items for a large retailer.
Assignment = Case study to use transactions from an open source dataset to find association rules using Apriori
Tool = R
Text mining
Main concepts and components of text mining, text mining tasks and approaches. An understanding of the art of the possible in Text Analytics - the applicability, components and benefits.
Hours = 3
Case Study = Case study on analysis of book reviews on Amazon
Assignment = Practice case study on text mining on book reviews
Tool = R
Social media analysis
Learn different text mining techniques to discover various textual patterns from the social sites. Learn how to – (1) Access twitter data. (2) Build frequent term network (3) Topic modelling (4) Analysis of followers & retweets
Hours = 3
Case Study = Case study to access a twitter account and create several visualizations to draw interesting insights
Assignment = Practice case study on text mining using another twitter account
Tool = R
Sentiment analysis
Understand sentiment analysis and its key concepts. Sentiment polarity. Opinion summarization. Feature extraction. Classification based algorithms. Application of SVM.
Hours = 3
Case Study = Case study to perform sentiment analysis on tweets
Assignment = Practice case study on sentiment analysis using another twitter account
Tool = R

Is this course for you?

You should take this course if you are a:

  • Student (UG/PG) looking forward to getting started in analytics career
  • BI professional or data analysts looking for upgrading their skills
  • Job seeker who wants to get started in analytics
  • Analytics professional who wants to gain skills in R and learn analytics solution building

What are the pre-requisites of the course?

Prior knowledge of a programming language will be helpful but not mandatory. The course starts by focussing on building a strong foundation of fundamentals & moves on to advanced aspects to provide a comprehensive understanding.

At the end of the course you will be entitled to Simplify Analytics 'Analytics (R) Specialist' Certificate, provided you fulfil the following terms:

  • Completion and submission of at least 6 projects/case studies
  • Attend at least 85% of the sessions
  • Clear the final test by minimum 60% marks
What is the mode of this training course?
Classroom & Online instructor led. Classroom sessions are held at multiple training centres located in Delhi-NCR region. Live online sessions are conducted through our "Virtual Classroom". This will allow you to attend the course remotely from anywhere through your desktop/laptop/tablet/smartphone. Video recording of each session is provided at the end of live session.
Do I need to have computer programming background to take the course?
No, you don’t need to have a programming background to learn analytics. The program has been designed in a way that it starts from scratch and makes it easier to learn for everyone.
What if I miss a class?
You can attend the missed session, in any other live batch. You can also use the video recording of the session you missed.
What kind of placement assistance is offered by Simplify Analytics?
We are committed to getting you placed. All our courses include - Real life projects + Internship + Certificate + Interview QnA + Resume building & sharing + Job search guidance + Interview call assistance.
What if I still have doubts after attending a live session?
You can retake a class as many times as you wish across multiple batches. Also, we conduct separate doubt clearing sessions to help our students. We make sure that you understand all the concepts and are able to build solutions.
What if I want to cancel my enrollment post registration? Will I get a refund?
Yes, we have a 100% money back policy which allows you to cancel your enrollment after the first two classes (before third class). If you are not satisfied from the program, all your money will be refunded back to you.
What are system requirements?
You will require a laptop or workstation with a minimum 2 GB RAM & i3 processor (or equivalent) to practice & submit assignments. No constraint on OS.
Thank you for choosing "Analytics (R) Specialist" Training Program

Course reviews
  1. Simplify Analytics - Course reviews
    5.00 out of 5

    Vaibhav Nellore

    Very knowledgeable!! Asks stimulating questions..Jaydeep is too good at explaining ideas; well designed course with great content.

  2. Simplify Analytics - Course reviews
    5.00 out of 5

    Rohit Kumar

    Great teaching techniques help you dwell into the field of analytics. would really recommend to anyone looking for a career in analytics