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  • Learn R & Business Analytics in this comprehensive course
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What does it mean?

Introduction to business analytics

  • What is analytics and why is it so important?
  • Applications of analytics
  • Different kinds of analytics
  • Various analytics tools
  • Analytics project methodology
  • Case study
In this section we shall provide you an overview into the world of analytics. You will learn about the various applications of analytics, how companies are using analytics to prosper and study the analytics cycle.

Fundamentals of R

  • Installation of R & R Studio
  • Getting started with R
  • Basic and Advanced Data types in R
  • Variable operators in R
  • Working with R data frames
  • Reading and writing data files to R
  • R functions and loops
  • Special utility functions
  • Merging and sorting data
  • Practice assignment
R is the most popular software/language for data management & statistical analysis of data. It is free and open source. This module is all about learning how to manage and manipulate data and datasets, the very first step of analytics. We shall teach you how to use the R language to work with data using a case study.

Univariate statistics in R

  • Summarizing data, measures of central tendency
  • Measures of data variability & distributions
  • Using R language to summarize data
  • Practice assignment
This is where you shall learn how to start understanding the story your data is narrating by summarizing the data, checking its variability and shape. We shall take you through various ways of doing this using the R language and also solve a real-world case study

Data visualization in R

  • Need for data visualization
  • Components of data visualization
  • Utility and limitations
  • Introduction to grammar of graphics
  • Using the ggplot2 package in R to create visualizations
  • Practice assignment
Data visualization is extremely important to understand what the data is saying and gain insights in just one glance. Visualization of data is a strong point of the R software and you will learn the same in this module.

Hypothesis testing and ANOVA in R

  • Introducing statistical inference
  • Estimators and confidence intervals
  • Central Limit theorem
  • Parametric and non-parametric statistical tests
  • Analysis of variance (ANOVA)
  • Case study
With 95% confidence we can say that there is a 75% chance, people visiting this site thrice will enroll for the course :). In this module, you learn how to create a hypothesis, statistically test it and validate it through data and present it with clear and formal numbers to support decision making.

Data preparation using R

  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment
  • Transforming variables
  • Derived variables
  • Binning data
  • Modifying data with Base R
  • Data processing with dplyr package
  • Using SQL in R
  • Practice assignment
Real world data is rarely going to be given to you perfect on a platter. It will always be dirty with missing data points, incorrect data, variables needing to be changed or created in order to analyze etc. A typical analytics project will have 60% of its time spent on preparing data for analysis. This is a crucial process as properly cleaned data will result in more accurate and stable analysis. We shall teach you all the techniques required to be successful in this aspect.

Predictive analytics in R

1. Correlation and Linear regression

  • Correlation
  • Simple linear regression
  • Multiple linear regression
  • Model diagnostics and validation
  • Case study
A statistical model is the core of predictive analytics and regression is one of the most powerful tools for making predictions by finding patterns in data. You shall learn the basic of regression modelling hands-on through real world cases

2. Logistic regression

  • Moving from linear to logistic regression
  • Model assumptions and Odds ratio
  • Model assessment and gains table
  • ROC curve and KS statistic
  • Case study
Logistic regression is the work-horse of the predictive analytics world. It is used to make predictions in cases where the outcomes are dual in nature i.e. an X or Y scenario where we need to predict if X will be the case or will Y, given some data. This is a must-know technique and we shall make you comfortable with it through real world problems.

3. Segmentation for marketing analytics

  • Need for segmentation
  • Criterion of segmentation
  • Types of distances
  • Clustering algorithms
    • Hierarchical clustering
    • K-means clustering
  • Deciding number of clusters
  • Case study
Learn why and how to statistically divide a broad customer market into various segments of customers who are similar to each other so as to be able to better target and meet their needs in a cost effective manner. This is one of the most essential techniques in marketing analytics.

4. Time series forecasting

  • What are time-series?
  • Need for forecasting
  • Trends, seasons, cycles
  • Exponential smoothing-Holt Winters method
  • Case Study
The ability to forecast into the future is very important for any business and it is necessary to have as accurate a forecasting as possible for corporate planning for finance, sales, marketing, strategy etc. In this module learn the techniques of forecasting without being mis-led by seasonal and cyclical impacts.

5. Decision Trees

  • What are decision trees?
  • Entropy
  • Gini impurity index
  • Decison trees algorithms
    • ID3
    • C4.5
    • CART
    • Regression trees
Decision trees are one of the most popular classification and prediction methods for helping in decision making. Learn the various decision tree algorithms and learn how to create a decision tree model.
Solving an actual business problem through analytics – Simulating an analytics project Simulation of an actual analytics project where you shall be completely hands-on and you will understand how everything you have learnt so far comes together to solve a business problem through analytics
See a class video

We would love to hear from you regarding any query that you may have be it about the course or about your career.

Contact us for more info

Or email us at info@edvancer.in

Or call us at +91 8080928948

  • Edvancer’s content is better than other institutes with whom I enquired and at much economical cost. After the course I got a job as a Campaign Management Analyst in ICICI Lombard.

    Rohit Kashid – Campaign Analyst, ICICI Lombard
  • It was a great experience and pleasure to learn from Edvancer.  The online class room is as good as a real class room. It was highly interactive with brainstorming on many ideas. The course content also depicts real life scenarios. Altogether it was a great learning experience.

    Vinodh S, Sr. Specialist Architect, Sapient Corp.
  • sumit kamra - Edvancer's Student

    The course was of very high quality and engaging. The interactive atmosphere and live examples were refreshing. The instructor had the real world experience to understand our needs and was easily reachable at any point of the time. I highly recommend this course.

    Sumit Kamra, Project Manager, ICICI Bank
  • The data science course provides an in-depth understanding of analytics with hands-on experience on R & Python using case studies from varied domains. You get all one needs for excelling in the field of analytics. The faculty have a very good grasp of all the concepts and the Edvancer team is very supportive.

    Girish Punjabi, Senior Business Analyst, IKen-IIT Bombay
  • I got a great job as Sr. Analyst with a 75% pay hike post this course! The course is a perfect blend of analytics tools and techniques. If you want to learn real stuff in analytics and not just the theoretical concepts, this course is for you.

    Ashish Kumar – B.Tech, IIT Madras

Benefits of taking the business analytics course

  • Learn fundamentals of predictive analytics techniques and how & where to use them
  • Learn R hands-on to manage, manipulate, cleanse and analyze data
  • You will not just learn the techniques and tools in isolation but will combine and apply them to derive business insights from raw data
  • Analytics talent demand is much more than the available skilled supply. Become employable in this fast growing new age field by demonstrating the skills learnt through this course
  • Use these new-age skills in your existing role to become more efficient and effective

Who should take this course

This course is for students pursuing their graduation/post-graduation and for working professionals who have completed their graduation in any field. There are no other prerequisites but you do need to have a quantitative bent of mind. For those who hate maths or numbers, while we shall try to make you as comfortable as possible, the analytics field itself may prove to be a challenge for you.