Data Science With R


Description
Course Structure
Pre-requisites
Certification
FAQs
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Course Objectives

The Data Science with R Certification course has been developed to impart an in-depth knowledge of data science techniques and their application using R to solve business problems. It builds a strong conceptual foundation and provides hands-on training on R language, R packages, functions and R studio to learn and apply predictive modeling, forecasting, classification and association techniques.

As part of the program you will work on real-life industry-based projects across multiple domains like social media, retail, entertainment and e-commerce using our Learning Management System. You will work on multiple assignments, case studies and practice exercises to hone your skills.

At the end of the course you will be able to solve analytics problems using R and build real world industry solutions

Course duration: 156 hours
Mode: Classroom / Online Instructor Led

Key Features

  • 56 hours of instructor led live training on weekends
  • Hand-on practice on 10 real life case studies
  • Access to LEAP - our analytics learning platform
  • Personal attention from faculty
  • Performance evaluation
  • Placement assistance
  • 100 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

Is this course for you?

You should take this course if you are a:

  • Student (UG/PG) and want to build data science skills to become data scientist
  • IT professional looking for a career switch to data science and analytics
  • Job seeker who wants to start a career in data science
  • Analtyics/Data Science professional who wants to learn advanced modeling techniques
  • Enthusiast who has genuine interest in data science and wants to grow his skills

What are the pre-requisites of the course?

There are no pre-requisites for this course. The course starts from scratch which makes it easy to understand for everyone and provides in-depth knowledge.

At the end of the course you will be entitled to Simplify Analytics 'Data Science with R' Certificate, provided you fulfil the following terms:

  • Completion and submission of at least 5 projects/case studies
  • Attend at least 85% of the sessions
  • Clear the final online test by minimum 60%
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 "Data Science With R" 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