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Data Science with R

Course Brief

How big is BIG? Become a big data expert through an intensive training program customised across various levels designed specifically for you. It will make participants solve real-time problems with huge datasets.Through this intensive program we aim to train the participants in a way that they are prepared to appear for International Certifications. In this course, you will master the basics of this beautiful open source language, including factors, lists and data frames. Learn Graphs, Statistics, Data Structures and Regression techniques. Understand advanced analysis using LDA, KNN, QDA, Support Vector Machines. Also learn to Fit a Neural Network for the given data set.
With the knowledge gained in this course, you will be ready to undertake your very own data analysis. Leverage the power of R by signing up for this course today!

Introduction- Definition - DS in various fields - Examples - Impact of Data Science - Major Activities - Toolkit - Data Scientist - Compare with others - Data Science Team

    Learning Outcomes:

    • Understanding Data Science and related fields
    • Be able to identify major activities of data science for the given problem
    • Understanding role of Data Scientist and how it differs from a data engineer and a data analyst.
    • Be able to choose deployment model for organization
    • Understand how to create a data science team.

Introduction to R : What is R - Data Science with other languages - Features of R - Environment - R at a glance.

Basics of R(Series & Ctrl Statements): Assignment - Modes - Operators - special numbers - Logical values - Basic Functions - Generating data sets - Control Structures.

Vectors:Definition- Declaration - Generating - Indexing - Naming - Adding & Removing elements - Operations on Vectors - Recycling - Special Operators - Functions for vectors - Missing values - NULL values - Filtering & subsetting. Exercises.

    Learning Outcomes:

    • Understand the how R differs from other languages
    • Be able to write R scripts for given problem
    • Be able to generate series
    • Be able to handle data in vectors and get required results from given data sets

Descriptive Statistics: Introduction - Descriptive Statistics - Central Tendency - Variability - Mean - Median - Range - Variance - Summary-Exercises.

Graphics : : Introduction - Types - Packages - Basic graph - Histograms - Stem Leaf Graph - Box Plots - Scatter Plots - Bar Plots.

    Learning Outcomes:

    •  Understand the importance of statistics, types and statistics in real world
    • Be able to find the central tendency, summary of given data sets
    •  Understand the importance of graphical output and various graphs
    • Be able to plot various graphs for the given data set
    • Implement Descriptive Statistics in R

Arrays: Creating Arrays - Dimensions & Naming - Indexing & Naming - Functions on Arrays.

Matrices : Creating Matrices - Adding rows/columns - Removing rows/columns - Reshaping - Operations - Special functions.

Lists: Creating - Naming - Accessing elements - Adding - Removing - Special Functions - Recursive Lists.

Data frames: Creating - Naming - Accessing - Adding - Removing - Special functions - Merging Exercises.

Functions: Creating - Functions on Function Object - Scope of Variables - Accessing Global Environment - Closures - Recursion - Creating New Binary Operator.

    Learning Outcomes:

    • Understand various data structures in R
    • Be able to choose suitable data structure for the given data set
    • Be able to retrieve the required result from the given data set
    • Be able to solve the problems by creating functions
    • Be able to merge and split the data sets
    • Be able to apply statistics on various data structures

Linear Regression: : Inferential Statistics - Types of Learning - Linear Regression- Simple Linear Regression - Coefficients - Confidence Interval - RSE - R2 - Implementation in R - lm - functions on lm - predict - Plotting - fitting regression line Exercises.

Multiple Linear Regression: Introduction- comparison with simple linear regression - Correlation Matrix - F Statistic - Response vs Predictors - Deciding important variable - Model fit - Predictions.

Generating a model - Interactive terms - Non Linear Transformations - Anova - lm with polynomial Exercises.

Classification & Logistic Regression : Classification - Examples - Logistic Regression Definition - Estimating coefficients - Predictions - Multiple Logistic Regression - More than 2 response classes - Implementation in R - glm - predict Exercises.

    Learning Outcomes:

    • Understand Inferential Statistics, types and regression concepts
    • Understand the population and sample for the given data set
    • Be able to understand how to fit a model for the given data set
    • Be able to find the relation between response and predictors
    • Be able to predict the values for given data set based on sample data set

Mr. P.V.N.Balarama Murthy
Data Science with R

Mr. P.V.N.Balarama Murthy, is an M.Tech(CSE) having over 10 years of teaching and technical training experience. He is specialist in Data Science and Bigdata. He has experience in deploying hadoop clusters. As technical trainer, he has trained a number of people in C,C++, Java, Oracle, Hadoop (Administration, Development with MR, PIG, Hive, Flume, Sqoop) and Data Science with R. He has guided to his credit 15+ students to get Hortonworks certifications for Hadoop.

A dedicated, resourceful and result oriented instructor that he is, it is helping shape up careers of students.

Ms. Jyothi SanjeevaMani
Data Science with R

Ms. Jyothi SanjeevaMani has over 15 years of satisfying teaching and technical training experience. She is a Research Scholar of Big Data Analytics from a reputed university. As a technical trainer she trained many students in industry oriented subjects like C, C++, Java, MySQL, Oracle (SQL, PL/SQL), Python, Linux, Openstack, BigData - Hadoop(MapReduce, Pig, Hive, Sqoop, Flume), Data Science with both Python and R.

She is an Asst.Professor with the Department of IT at The Keshav Memorial Institute of Technology (KMIT).

She is a dedicated, resourceful and a result oriented instructor, who strives to help students change marginal grades into good grades.

  • Are there any pre requisites for this course?

    No requirements are needed to learn R. The only knowledge that needed to learn R is basic statistical knowledge.

  • Why you should learn R first for data science

    • R is becoming the lingua franca for data science. That's not to say that it's the only language, or that it's the best tool for every job. It is, however, the most widely used and it is rising in popularity.
    • Beyond tech giants like Google, Facebook, and Microsoft, R is widely in use at a wide range of companies including Bank of America, Ford, TechCrunch, Uber, and Trulia.
    • R is popular in academia: R isn't just a tool for industry. It is also very popular among academic scientists and researchers.
    • Learning the "skills of data science" is easiest in R. To do this, you'll need to master the 3 core skill areas of data science: data manipulation, data visualization, and machine learning. Mastering these skill areas will be easier in R than almost any other language.

  • Can I just enroll in a single course? I'm not interested in the entire Specialization.

    No you cannot enroll for individual skill sets within a defined course on teleuniv.

  • How long does it take to complete this Specialization?

    Most learners are able to complete the Specialization in about 3 months.

  • Do I need to take the courses in a specific order?

    We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.

  • What will I be able to do upon completing this Specialization?

    This specialization will unlock great career opportunities as a Hadoop developer. Become a Hadoop expert by learning concepts like Pig, Hive, Flume and Sqoop. Get industry-ready with some of the best Big Data projects and real-life use-cases.

  • Can I attend a demo session?

    We have limited number of participants in a live session to maintain the Quality Standards, hence, participation in a live class without enrollment is not possible. However, we can create a demo login for one demo session.

  • What are the payment options?

    You can pay by Credit Card, Debit Card or Net Banking from all the leading banks. We use a Payment Gateway.

  • Do you provide placement assistance?

    Teleuniv is associated with Keshav Memorial Institute of Technology, one among the top performing colleges in Hyderabad and hence lot of recruitment firms contacts us for our students profiles from time to time. Since there is a big demand for this skill, we help our certified students get connected to prospective employers. Having said that, please understand that we don't guarantee any placements however if you go through the course diligently and complete the assignments and exercises you will have a very good chance of getting a job.

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