Certified Business Analytics Professional

About This Course

About This Course

Business Analytics is the scientific process of deriving insights from raw data to support decision making. Through this business analytics course you will learn:

  • Fundamentals of Predictive Analytic techniques and How and Where to use them
  • Learn the basics of statistical techniques and R language with hands-on to manage, manipulate and analyze data
  • Solving business problems thru analytics via Case Studies
  • Hands-on with Data sets to derive business insights from raw data

Business Analytics comprises of tools and methods that give the business users (i.e. decision makers) insight and understanding of not just what happened, but why it happened, with the purpose of using this insight to prescribe action.

Course Duration:

5 Days

Introduction to Business Analytics

This section provides an overview into the world of analytics. You will learn about various applications of analytics and analytics cycle.

  • What is analytics and why is it so important?
  • Applications of analytics
  • Different kinds of analytics
  • Various analytics tools
  • Analytics project methodology
  • Business Analytics vs. Business Analysis

R Fundamentals

R is the most popular software/language for data management & statistical analysis of data. It is free and open source. This section covers on the first step of analytics on how to manage and manipulate data and datasets. Also learn how to start understanding the story your data is narrating by summarizing the data, checking its variability and shape.

  • Installation of R & R Studio
  • 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
  • Merging and sorting data
  • Summarizing data, measures of central tendency
  • Measures of data variability & distributions
  • Using R language to summarize data

R Data visualization

Data visualization is extremely important to understand what the data is saying and gain insights in a snap. Visualization of data is a strong point of the R software.

  • 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

R Data preparation

Real world data is rarely Clean, 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. This section teaches you all the data preparation techniques.

  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment
  • Transforming variables
  • Derived variables
  • Binning data
  • Modifying data with Base R

Hypothesis testing and ANOVA in R

With 93% confidence we can say that there is a 70% chance, people visiting this site thrice will buy this product. In this section, we cover on 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.

  • Introducing statistical inference
  • Estimators and confidence intervals
  • Central Limit theorem
  • Parametric and non-parametric statistical tests
  • Analysis of variance (ANOVA)
  • Case Study

R Predictive analytics

  • Correlation and Linear regression
  • Logistic regression
  • Segmentation for marketing analytics
  • Time series forecasting
  • Decision Trees

Text Analytics, Document and Word Classification & Sentiment Analysis

  • What is text mining?
  • Tools for text mining
  • Text mining packages in R
  • Use cases of text analytics
  • Text mining process
  • What is document & word classification
  • Steps for document & word classification
  • Techniques for classification
  • Case study on classifying news articles
  • What is sentiment analysis
  • Why is sentiment analysis done
  • Real world applications of sentiment analysis
  • Steps for sentiment analysis
  • Sentiment scoring
  • Dictionary creation
  • Algorithms for sentiment scoring
  • Case study - Analyzing sentiments in tweets for smartphone companies

CASE STUDY : Solving an actual business problem through analytics connecting all the concepts you had studied in this course.

Certified Business Analytics Professional

Exam Platform : KALAM

Exam Format : Multiple Choice Question (MCQ)

Exam Questions : 25 Questions

Exam Duration is : 90 Minutes

Exam Pass Mark : 70%

Exam Fees : Inclusive in the Course Fees

Free Add-on : Free Membership access to KALAM Cybersecurity Collaboration & Community Skills Validation Platform

Certified Business Analytics Professional :

Coming Soon

Truly Inspiring & well-conducted

I gained various technical skills that now assist me in my day to day job in business predictive analytics.

Hands-on Labs with latest tools

Very beneficial as the course taught me how to prepare data and how to model it for the best results

Trainer was patient and knowledgeable

Taking this course was a stepping stone and I'm very pleased with the learning based on real-world case studies and data models

Frequently Asked Questions

What is the prerequisite for this course?
None, anyone who like data and mathematics can join
Where can I get the Course Schedule?
Reach out to us via our Contact Us page with details on your location and interested course. We will find the nearest training partner to assist you for F2F / online class.
Is it necessary to take the exam on 5th day?
The exam voucher validity is 6 months, you can take exam within 6 months from your course date. We recommend you to take the exam at the earliest.
With this 5-day course, will I become a professional?
A professional in 5 days is just a marketing gimmick. We get you started with the right knowledge and assist you through the process of achieving professionalism after the class with mentoring and guidance through our Community Ecosystem platform, KALAM.