Data Science Course with R Programming

Introduction to Data Analytics, Data Types and Data Models Study of Business Applications Business Analytics Types Statistics Basics – central tendencies, distributions, f-test, central limit theorem, anova Regression Hypothesis Testing Clustering and K-means Market Basket Analysis Classification and Decision Trees Time-series Analysis

MS Excel Introduction
Data formatting and security options
Excel cheat sheet of shortcuts
Excel formulaes and functions
Filters, validations, grouping and other data handling techniques
vlookup, pivot and other advanced functions
Macros
Data analysis, simulation, scenarios, locking, sharing and tracking
Charting and Dashboarding

History & Evolution of R
R – Features
R Environment Setup and installation in multiple OS
Local environment set up
Understanding R Basic Syntax and operations like constants, expressions, function calls, symbols, data structures, sub-setting and more
Defining R command prompt
What is R script file?
Analysing Operations and Programs

Studying Vectors
Defining Factors
Defining Lists, Data frames, Matrixes and arrays
Understanding R variables
Data type of variable
Finding variable & Deleting variable
Explore numeric variables
Exploring relationship between variables
Learning of algorithms
Learning of curves, analysis of errors and trade-off between recall and precision
Concepts of cross-validation

Studying types of operators
Defining Arithmetic & Relational Operators
Logical & Assignment operators
Miscellaneous operators
What is R decision-making?
Understanding R if statement
R if..else statement
Defining R switch statement

Loops – R repeat loop
Loops – R while loop
Loops – R for loop
Loop control statement
Statement – R break statement
Statement – R next statement
Functions in R
Defining Built-in Functions
User-Defined Function
Lazy Evaluation of Function
Loops and conditionals
Understanding switch and break function
Applying of functions
Understanding apply, sapply, lapply, tapply, mapply
Control Structures
User defined functions
Data Manipulation and Visualization using R
Hypothesis Testing

Rules of string construction
Understanding String manipulation
R vectors & Vector creation
Accessing vector elements
Understanding Vector manipulation
R list & Creation
Name list elements
Access list elements
Manipulate list elements
Merge lists
Convert list to vector

Name columns and rows
Access array elements
Manipulate array elementsCalculate across array elements
Studying R factors
Understanding Factors in data frame
Change the order of levels
Generate factor levels

How to extract data from data frame?
Expanding data frame
R packages & R data reshaping
Joining columns & Rows in a data frame
Merging data frames
Understanding Melting & casting
Melt & Cast the molten data
Solving Statistical data in R
Management of data with R
R data structures Saving and loading
Exploring data structure

Getting & Setting the working directory
Input as CSV file
Read a CSV file
Analyse CSV file
Writing into CSV file
Understanding R excel file
Installing xlsx package
Verify & Load xlsx file
Input as xlsx file
Reading the Excel File
Data import and Data Export

Toggle ContentWrite the binary file
Read the binary file
Understanding R – XML Files
Reading XML File
Understanding first node
XML to data frame
Studying R JSON file
Install RJSON Package
What is Input data
Read the JSON File
Convert JSON to Data Frame

Studying R Data bases
Understanding RMySQL Package
How to connect R to MySQL?
Querying the tables
Query with filter clause
How to update rows in the table?
How to insert data into tables?
How to create tables in MySQL?
Dropping Table in MySQL

Defining Pie-charts Title & Colour
Slice percentages & chart legend
Understanding 3D pie chart & R bar charts
Bar chart labels, title & colours
Group bar chart & stacked bar chart
R boxplots & creating the boxplots
Understanding Boxplots with notch

Range of X and Y values
Line chart title, colour & labels
Understanding Multiple lines in a line chart
Create the scatterplots
Studying Scatterplot matrices
Model of Measurement of Central Tendency
Defining numeric data with uniform and normal distributions
R mean, median, & mode
Studying Mean
Applying trim option
Applying NA option
Measurement of spread in variance and standard deviation
Visualization of numeric variables in boxplots
Visualization of numeric variables in histograms
Visualization of Relationships between Scatterplots

Studying R Linear Regression
Steps to establish a regression
Understanding Im() Function
Defining Predict () Function
R multiple regression
Understanding Im() Function
Studying R logistic Regression
Create regression model

Studying R normal distribution
Understanding dnorm ()
Defining pnorm ()
What is qnorm ()
What is rnorm ()
R binomial distribution
What is dbinom ()
Understanding pbinom ()
What is qbinom ()
Defining rbinom ()
Studying R poisson regression
R analysis of covariance

Different time intervals
Multiple time series
R nonlinear least square
Studying R Decision Tree
What is R Random Forest?
Install r package
Defining R random forest
Install r package
R Survival Analysis
R chi square Test

ML Common Use Case
Understanding Supervised and Unsupervised Techniques
Defining Clustering
Studying Similarity Metrics
Types of Distance Measure: Euclidean
Studying Cosine Measures
Creation of predictive models
Classification using nearest neighbours

Statistical Model Building
Validation and Prediction
Use of statistical models for predictions
Case studies

You can look or a role of a Data Scientist which is same as the role of a Business Analyst, Data science programmer or developer and Business intelligence related profiles . Generally a Data scientist is responsible or working with complex data extracted from Hadoop environments and needs to have good knowledge of business, data, SAS, SPSS and data analytics tools and languages like R Programming. Our data science course is also good for starters as well as advance knowledge covering all topics right from the basic level. A lot of job opportunities also depend upon your background and past experience, however, sincere efforts put into attaining all the knowledge base from the course learnings will surely promise a bright future.

Big data is growing massively and so is the need for it’s analysis which has created plenty of job opportunities for Data Scientists with good skills in important Data Analysis tools and techniques. R programming is about 70% percent widely used tool from among all the Data analytics tools and languages because it is an open source free software easily extendable with lots of packages. Due to these reasons R Programming is an important skill acquired in the Data Science field of study.

Harvard Business Review tags Data Science as 21st century’s sexiest job title. With the boom in internet based market and more and more usage of gadgets, data is produced in voluminous quantities. No business decision can be taken without analyzing this data and extracting important facts from it which is the work of a Data Scientist. You can take benefits of this technology boom cycle by undergoing the Data Science course and learning to handle and understand data using important tools and languages like R Programming. However, you should have an interest in mathematics and statistics and be ready to quickly adapt the crucial concepts of this growing sector.

After completing your course with Ad2Brand, you can expect to be equipped with all necessary basic foundational as well as advanced concepts of R Programming and Data Science, theories of the most widely used algorithms, data manipulations, statistics and logics and hands-on experience of a Data scientist’s work, fit to apply for a good job role with a good package.

The course will stretch over a period of 3 months.

You can attend a demo session to understand the subject, see the quality of training and interact with the trainer regarding queries to decide your career path. We at Ad2Brand ensure to give you the best training experience with top content and trainers, we also prepare you for interviews and resume preparation and equip you with the best skills in the subject of learning. However, getting the job will depend upon your presentation of skills at the time of interview.

We have reliable and knowledgeable trainers who are the best in the industry qualified with in depth skills, certifications and experience.

Ad2Brand’s R Programming course is comprehensive and detailed, covering industry-exposure and important hands-on projects. You will learn statistics, reporting , functions, data structures, variables, control flow , graphical representation and much more. Upon completion you will get R Certification and expert skillset in R Programming.

Doubt clearance and resolution of queries is given utmost importance at Ad2Brand. You can raise n number of queries until you get a clear answers for your questions. Our trainers make sure to give individual attention to all students and one-on-one sessions can be arranged with trainers during as well as after completion of the training to get help on the needed topic. Also we make sure to maintain a limited number of students per class to maintain the quality of training and knowledge imparted.