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
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.
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.
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.
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.
Classification: : Linear Discriminant Analysis - Quadratic Discriminant Analysis - K-Nearest Neighbors- Exercises.
Support Vector Machines: Maximal margin classifier - Support Vector Classifier - Support vector machine - SVM with more than 2 classes - Exercises.
Neural Networks : Introduction - Nodes & Weights - Layered Architecture - Learning Rule - Implementation in R - Normalizing data - Creating training data sets - Fitting Neural Network - neuralnet - Plotting NN - Predictions - Denormalize - MSE - Exercises.
Clustering: Unsupervised Learning, Principal Component Analysis(PCA), Clustering Methods: K-means, Exercises
No requirements are needed to learn R. The only knowledge that needed to learn R is basic statistical knowledge.
No you cannot enroll for individual skill sets within a defined course on teleuniv.
Most learners are able to complete the Specialization in about 3 months.
We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.
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.
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.
You can pay by Credit Card, Debit Card or Net Banking from all the leading banks. We use a Payment Gateway.
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.