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IIHT’s **Data Science with R Programming course** is a complete bootcamp for data Science specialization training course from IIHT which provides detailed learning in data science, project life cycle, statistical methods and data acquisition. The course allows participants to master an array of machine learning concepts like data analysis, deployment, Univariate analysis, multivariate statistics and business application analysis, probabilistic analysis, hypothesis testing and categorical data analysis, calculating test statistics, and continuous data analysis and data cleaning through R. Students will also gain expertise working with dataset using R, web scrapping, test mining and analysis, choosing statistical tools for text analysis, and market-based analysis. With the **online Data Science with R Programming course** students get hands-on experience in the way of getting industry exposure in the latest technologies. The course gives a comprehensive training by industry leaders to help students master the course.

Data science is a multidisciplinary blend of algorithm development, data inference and technology placed in order to solve analytically complex issues. Data is at the heart of Data science – There is plenty of information that is being streamed and stored across enterprise data warehouse. Mining it can give one valuable information and by use of advanced capabilities, we can help build with it. Data science is all about using data in ways that are creative so as to generate business value.

For organizations, having data scientists working for them is essentially having the ability to predict the future from the past and present data, allowing organizations to have a competitive edge over their rivals. Data science is set to dominate every industry, from retail to healthcare, finance and public sector. With Data science teams playing a critical role in business strategies today, enterprises can make informed decisions that will better their operational efficiency in many ways. However, without well trained professional expertise which will make the translation of cutting-edge technology into actionable insights, Big Data would be obsolete. Like every technology, data science also depends on how it is used to solve business or real life problem.

- There are plenty of job opportunities in Data Science analytics and management presently and it is predicted to grow in the future. A lot of IT professionals are prepared to invest time and money for training.
- The next few years will see the data science market size evolve into at least one-thirds of the global IT market from the present one-tenth. The current demand for qualified data professionals is only the beginning.
- The demand for Data science skill is growing rapidy despite the huge deficit on the supply side. This is a global problem and despite it being a ‘hot’ job, there are several unfilled jobs across the world because of lack of required skills.
- Today, India has the highest concentration of analytics globally. Despite this, the scarcity of data science talent is acute with demand growing even more as more and more Multinational companies are outsourcing their work to India.
- The high demand for skilled data science professionals is now boosting the wages for qualified professionals, making data science pay big for the right skills.

**Why Data Science at IIHT?**

- Introduction to project life cycle, Hadoop Streaming, Mapreduce, Market Basket Analytics, Hortonworks Data platform, Data Science in real world, and Data Acquisition.
- Understanding algorithms in Machine Learning
- Study tools and techniques of evaluation, experimentation and Project Deployment
- Get expert, practical training in roles and responsibilities of Data Scientists
- Learn all basics of Big Data and methods of R integration with Hadoop
- Learn from industry experts the concept of analysis segmentation and prediction with Clustering
- Live projects on analytics, Recommender systems, customer profiling, fraud detection, failure detection, and sentiment analysis.
- Blended learning methods to get optimal results and maximum participation from the students.

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The unique one-of-its kind blended learning module has the following features:

- Personalized learner homepage: There is a provision for students to use personalized dashboards that students can customize, view the status of the course material. Students can also compare their performances with their classmates’.
- Catalog for recommended courses: View course catalog and sort levels.
- Powerful search: Global search the entire Learning Management System of IIHT whenever you need.
- Course Details: Learners get to view the details of the course like deadlines and upcoming project.
- Prework: PDFs and documents to help you prepare for upcoming courses.
- Core Content: Virtual classrooms, activities and assessments
- Cloud Labs: Easy access to labs anytime, integrated within the Learning Management System.
- Download Reference Materials: Learners can download reference materials from resources section any time
- Fluidic Player: Learn at your own pace.
- Allows students to take notes for revision
- Gamification: Students can compete with one another to do better!

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- What is R?
- Why R?
- Installing R For Windows, Mac OS, and Linux
- R environment
- How to get help in R
- Writing and executing scripts

- Installing packages
- Understanding R data structure
- Variables in R
- Scalars
- Vectors
- Matrices
- List
- Extracting elements from vectors
- Vector arithmetic
- Simple patterned vectors
- Character vectors
- Data frames
- Cbind,Rbind, attach and detach functions in Factors
- Getting a subset of Data
- Converting between vector types

- Using functions in R
- Apply Function Family
- Commonly used Mathematical Functions
- Commonly used Summary Functions
- Commonly used String Functions
- User defined functions
- local and global variable
- Working with dates
- R Programming
- While loop
- If loop
- For loop
- Arithmetic operations

- Importing data
- Reading Tabular Data files
- Reading CSV files
- Importing data from excel
- Loading and storing data with clipboard
- Accessing database
- Saving in R data
- Loading R data objects
- Writing data to file
- Writing text and output from analyses to file
- Saving and retrieving image files
- Manipulating Data
- Selecting rows/observations
- Rounding Number
- Creating string from variable
- Search and Replace a string or Number
- Selecting columns/fields
- Missing Values
- Working with Missing Values
- Merging data
- Relabeling the column names
- Reshaping
- Modifying Data Frame Variables
- Recoding Variables
- The recode Function
- Reshaping Data Frames
- The reshape Package
- Combining Data Frames
- Under the Hood of merge

- Data sorting
- Data Aggregation
- Road Map for Aggregation
- Mapping a Function to a Vector or List
- Mapping a function to a matrix or array
- Mapping a Function Based on Groups
- There shape Package
- Finding and removing duplicate records
- Character Manipulation
- Basics of Character Data
- Displaying and Concatenating Character
- Working with Parts of Character Values
- Regular Expressions in R
- Basics of Regular Expressions
- Breaking Apart Character Values
- Using Regular Expressions in R
- Substitutions and Tagging

- Data Visualization
- Base graphics system in R
- Bar Charts and Dot Charts
- Box plot
- Histogram
- Pie graph
- Line chart
- Scatterplot
- Labels, legends, titles, axes
- Quick plots (qplot function)
- Building graphics by pieces (ggplot function)
- low level graphics functions
- Adding to plots and setting graphical parameters
- Exporting graphics to different formats
- Developing graphs
- Cover all the current trending packages for Graphs

- Module 1 Introduction
- Introduction
- What is data science and why is it so important?
- Applications of data science
- Various data science tools
- Data Science project methodology
- Tool of choice-Python: what & why?

Case study - Univariate Analysis
- Mean, Median Mode
- Variance, Standard Deviation
- Covariance
- Correlation
- Standard Error
- Noramal distribution and Fisher’s Distribution
- Business Application Analysis
- Hands-on: working on some dataset using
- R/python
- Module 2 – Multivariate Statistics
- Multivariate analysis
- Euclidean Norm and Distance
- Dot Product and Projection
- Mean Vector, Covariance Matrix, Precision Matrix
- Making stratified samples
- Mahalanobis Distance
- Multivariate Normal Distribution
- Bootstrapping and sub-setting
- Making samples from the Data
- Business Application Analysis
- Hands-on: working on some dataset using
- R/python
- Module 3 – Probabilistic Analysis
- Probability Theory:
- Events and their Probabilities
- Rules of Probability
- Conditional Probability and Independence
- Distribution of a Random Variable
- Moment Generating functions Central
- Limit Theorem
- Expectation
- Business Application Analysis
- Hands-on: working on some dataset using R/python
- Module 4 – Hypothesis Testing & Categorical Data
- Analysis
- Hypothesis Testing
- Sample and Population
- Formulate the Hypothesis
- Select an Appropriate Test
- Choose level of Significance
- Calculate Test Statistics
- Determine the Probability
- Compare the Probability and Make Decision
- Hands-on: working on some dataset using
- R/python
- Analyzing the categorical Data
- Proportional Test
- Chi Square Test
- Fisher’s Exact Test
- Mantel Henszel test
- Business Application Analysis
- Hands-on: working on some dataset using R/python
- Module 5 – Continous Data Analysis & Data Cleaning
- Analyzing the Continuous Data
- One Sample T-Test
- Two Independent Samples Tests
- Paired T-test
- Wilcoxon Test
- Anova
- Kruskal Wallis Test
- Hands-on: working on some dataset using
- R/python

- Data Cleaning and Preprocessing
- Label Encoding
- One Hot Encoding
- Finding Missing Values
- Feature Selection Parameters
- Outliers Detection in Realtime
- Miscellaneous Data Cleaning Techniques

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