BECOME AN EXPERT BUSINESS ANALYST IN
12 WEEKS

50%

Pay hikes for Analytics professionals is 50% more

$51 Billion

Analytics industry to grow to $51bn by 2016

190K

190K jobs by 2018, Mckinsey Global Report

56+ REVIEWS

4590+ LEARNERS

"Once a new technology rolls over you, if you`re not part of the steamroller, you`re part of the road." - Stewart Brand

17,999

OUR MENTOR-DRIVEN PROGRAM

Learn the fundamentals

Learn intermediate concepts

Learn advanced concepts

Getting Started

Know history and mission of BGAcademy.
Get a walkthrough on the Course plan.
Setup and get familiar with R Studio which we will be using as our Integrated Development Environment (IDE).

Start with Basics

Familiarize with Business Analytics.
Introduction to R programming.
Manipulate and visualize the data using R.

COURSE SYLLABUS

Introduction to Business Analytics

What is Business Analytics?
Evolution of Business Analytics
Scope of Business Analytics
Data for Business Analytics
Decision models
Companies using R extensively
Role of a data scientist

Introduction to R programming

Why R
Installing R studio desktop
Understanding R studio
Setting your work directory
Installing packages and libraries in R studio
The Google for R
Important R packages
Data mining GUI in R
Graph GUI in R
Learn swirl

Basic Operations in R

Operators
Built-in functions
Performing various operations using built in functions
Type of data structures in R
Data types : vectors
Working with missing values
Data types : matrices

Basic Operations in R(Contd..)

Data types : arrays & general array operations
Data types : lists & general list operations
Data types : Data frame & general data frame operations
Factors

Data Manipulation in R

Data acquisition (Import & Export)
Subsetting variables
Creating new variables
Renaming and recoding variables
Reshaping data
Merging & concatenating datasets
Using dplyr To manipulate data frames
Data type conversion
Data values

Functions in R

Control and flow operators
Make a script in R
Writing functions in R
Creating R package

Data Visualization in R

Types of visualization
Graphs in R
Line plots
Bar charts
Pie charts
Histograms & density plots
Scatter plots
3-D & parallel coordinates

Statistics and Data exploration

Why study statistics
Applications of statistics
Types of statistics
Population vs sample
Types of data
Types of statistical variables
Summarize the data
Make decisions using summary statistics

Project I

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project I cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Random Variable and Normal Distribution

Random variables
Expected value
Probability distribution
Standard deviation and variance
Types of distributions
Understanding normal distribution
Skewness & Kurtosis
Types of sampling

Central Limit Theorem and Confidence Interval

Central limit theorem
How does central limit theorem work
Practical applications of CLT
Confidence Interval & probability
How to interpret confidence interval
P-value; z-score
Confidence intervals for unknown mean and unknown standard deviation
T-distribution
Poison distribution

Hypothesis Testing

Learn how to state null and alternative hypotheses
Understand type-I and type-II errors
Conduct one-sided hypothesis test for population

Analysis of Variance

Comparing more than two groups
Difference between ANOVA and t test
ANOVA explanation
One way ANOVA
Variance within and between groups
ANOVA table R2 and adjusted R2
Interpretation of various cases

Linear Models in R

Introduction to regression
Why do regression analysis
Types of regression analysis
OLS regression
Dependent and independent variable(s)
Steps to implement a regression model
Simple linear regression
Understanding terminology of each of the output of linear regression

Correlation and Regression

Correlation
Strength of linear association
Least-squares or regression line
Linear regression model
Correlation coefficient; R
Multiple regression
Regression diagnostics
Detection of collinearity: simple signs

Assumptions in Regression Analysis

Assumption 1 and explanation - Residuals and non normality
Assumption 2 and explanation - Heteroscedasticity
Assumption 3 and explanation - Additivity
Assumption 4 and explanation - Linearity
Assumptions 5 to 8 and their explanations - Independence Assumption
Residual plots

Model Selection in R

Fitting the model
Diagnostic plots
Comparing models
Cross validation
Variable selection
Relative importance
AIC
Dummy variable
Box-Cox transformations

Creating the model

Residuels vs fitted
Residuels vs regression
Diagnostic plots

Logistic Regression

Binary response regression model
Linear regression output of proposed model
Problems with linear probability model
Logistic function
Logistic regression & its interpretation
Odds ratio
Goodness of fit measures
Confusion matrix

Project II

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project II cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project II cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project II cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

INTERESTED IN CORPORATE TRAINING?

12
WEEKS
COURSE DURATION

2
SESSIONS
EVERY WEEK

1.5
HOURS
EACH SESSION

200
HOURS
PROJECTS & ASSIGNMENTS

GREAT

CAREER

COURSE EXTRACT

Level	Intermediate
Pre-Requisites	None but some basic knowledge in Statistics is beneficial
Technologies/Tools	R, RStudio
Projects	2 (Fully Functional)
Batch Size	8 - 10 Students
Job Preparation	Yes
Certification	Yes
*247 support**	Yes

WE HELP YOU GET A GREAT JOB

ADDITIONAL BONUS WEEK

On course completion, you are ready to enter the exciting world of corporates . We show you how to make the right impression at job interviews.

INTENSIVE JOB INTERVIEW PRACTICE

Congratulations, you're now a programmer with awesome skills. In this bonus week, we'll help you prepare for your job interviews so that you can make a great first impression. This includes:

2 in-depth mock technical interviews

Guidance on how to answer real interview questions from top employers

Assistance with creating a resume that stands out

BUILDING AN ONLINE REPUTATION

We can help you build that online presence on sites such as Git, StackOverflow, Quora, and LinkedIn.

PROJECTS WHICH STUDENTS WILL DEVELOP

Exploratory Data Analysis

For this project, you should perform exploratory data analysis on different file types like XML, JSON, TEXT, .DAT (data will be provided by BGAcademy)

Write R Code and Creating 6 By 10 Matrix

This project aims at:
a) Writing R code to calculate the amount of money owed after n years inn a problem statement.
b) Creating 6 by 10 matrix of random integers chosen from 1 to 10 by executing some given conditions.

Linear Regression Model For DVD Sales Data Set

This project aims at creating a Linear Regression Model for DVD sales data set which contains the following details - “ Advertising, Sales, Plays and Attractiveness.

Predicting Stock Market Returns

Aim of this project is to build a stock trading system based on prediction models obtained with daily stock quotes data. Several models will be tried with the goal of predicting the future returns of the S&P 500 market index.

Multiple Linear Regression Model For General Motors (GM) Data Set

This project aims at creating a Multiple Linear Regression Model for General motors (GM) data set. The data set contains data based on several hundred used General Motors (GM) cars. The data allows us to develop a multivariate regression model to determine car values based on a variety of characteristics such as mileage, make, model, cruise control, etc.

CUSTOMER FEEDBACK

PRICING

17,999

MONEY BACK GUARANTEE

If you are unhappy with the course and opt out in the first week, you get a complete refund.

SCHOLARSHIPS

We subsidize our fees by 10% for military personnel, and college students with exceptional records. To apply for a scholarship, email enquiry@acadgild.com.

GROUP DISCOUNT

More than 3 people in a group or from the same Company can avail special discount. For more information contact us at enquiry@acadgild.com

FAQ'S

What is this course all about?

R is a programming language and software environment for statistical computing and graphics. R allows you to visualize data, run statistical tests, and apply Machine Learning algorithms. In this course, you will learn about the most effective data analytic techniques and gain practice by implementing them and getting them to work for you. More importantly, you'll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Who should do this course?

Graduate students, post graduate and PhD scholars (from statistical background), entry-level software professionals, and anyone who is interested in a data analytics career

Why should I do this course?

After completion of this course one can have a solid understand of practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

What are the pre-requisites for this course?