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Course Overview

Get introduced to the basics, evolution, and scope of business analytics. Learn R and data manipulation, functions and data visualizations in R.

Understand the various types and applications of statistics as well as the types of data and statistics variables. Master the art of making informed decisions using summary statistics.

Learn random variables, expected value, probability distribution, standard deviation, variance and the types of distributions. Learn how to state null and alternative hypotheses, understanding Type-I and Type-II errors. Conduct one-sided hypothesis test for population.

Apply correlation, strength of linear association, least-squares or regression line, linear regression model, Gain expertise in multiple regression, regression diagnostics and detection of collinearity: simple signs.

Learn about fitting of model, diagnostic plots, comparison of models, cross validation, variable selection, relative importance and Box-Cox transformations.

Master binary response regression model and linear regression output of proposed model. Work on the various problems with linear probability model, logistic function, logistic regression & its interpretation and the various odds ratio, goodness of fit measures and confusion matrix.

Highly Experienced

Mentors

Mentors

Develop 2 Real Time Projects

Lifetime Access to Dashboard

24x7

Coding Support

Coding Support

Internationally Recognized Certification

Course Syllabus

- 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

- 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

- 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

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

- 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

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

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

- 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

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

- 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

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

- 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

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

- 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

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

- Residuels vs fitted
- Residuels vs regression
- Diagnostic plots

- 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

Projects Which Students Will Develop

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.

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.

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

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.

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.

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

Customers Feedback

The training provided is very good, especially on AngularJS. It would have been good if we have involved in the final project on AngularJs development. Overall the training met the expectation.

The way of ACADGILD Mentor , Krish Ram teach is easily understand and easy learning with good clarity about the subject knowledge along with practical examples.He always makes us to understand by giving real time and interesting examples.The support and co-operation from the team is great and i am feeling happy to doing course here.

I joined AcadGild's Front End Development course about a month back. So far my experience has been great. And now I am really glad I took this course. The focus on coding while learning sessions and the bunch of tips after session to solve assignments gave the load hands on experience.

I am Jayanth Kumar T, a Senior Research Engineer. I took up the Front End course at AcadGild. The course definitely helped me add skills to my resume.The mentors were disciplined, knowledgeable and provided a good support. Thank you all for the support during the course.

My name is Vyshali Bhat. My mentor, Mr. Jayant has been very supportive and it was a very good learning experience for me. I wish to thank the Acadgild team for all the support and guidance.

FAQ's

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.

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

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.

- Microsoft® Windows® 7/8/10 (32- or 64-bit)
- 4 GB RAM(Recommended)
- I3 or higher processor

After doing this course, one will gain good knowledge in statistics and the following:

- Data manipulation
- Exploratory data analysis
- Data visualization
- Linear models