Overview
Business Analytics is a practice of iterative and methodical exploration of an organization’s data with an emphasis on statistical analysis. With the current shift to the digital age, the modern organization has data that are often under-utilized for business planning and market research opportunities.
Gartner predicted global demand for 4.4 million big data analytics in 2015, but employers are expected to fill fewer than half of those jobs. While 70% of enterprise-level organizations invest in external data, 100% of these organizations are expected to make that investment by 2019.
This 4-day specialized course covers the concept of Business Analytics and its key importance to any organization. This course provides an overview of the world of analytics and will learn the various applications of analytics and analytics cycle. Participants will also learn how to create hypotheses, statistically test, and validate through data and present with clear and formal numbers to support decision making.
In this course, we will be using R & R Studio (free and open-source tool) to manage and manipulate data and datasets by summarizing the data, checking its variability and shape. At the end of the course, participants will be given a case study to solve an actual business problems through analytics connecting all the concepts learned in this specialized.
Prerequisites
Participants who wish to take up for this course are recommended to have preferably minimum of 1-2 years of experience in business domain or data/business analysis. However, if the participant does not have any experiences, he/she can still consider taking up the course and ITEL Learning Systems will advise him/her accordingly.
Who Should Attend?
Course Outline
- 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
- 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
- Using R language to summarize data
- Need for data visualization
- Components of data visualization
- Utility and limitations
- Introduction to the grammar of graphics
- Using the ggplot2 package in R to create visualizations
- Introducing statistical inference
- Estimators and confidence intervals
- Central Limit theorem
- Parametric and non-parametric statistical tests
- Analysis of variance (ANOVA)
- Case Study
- Needs & methods of data preparation
- Handling missing values
- Outlier treatment
- Transforming variables
- Derived variables
- Binning data
- Modifying data with Base R
- What is text mining?
- Tools for text mining
- Text mining packages in R
- Use cases of text analysis
- Text mining process
- What is document and 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
- Algorithm for sentiment crossing
- Case Study: Analyzing sentiments in tweets for smartphone companies
- Correlation and Linear regression
- Logistic regression
- Segmentation for marketing analytics
- Time series forecasting
- Decision Trees
