The Business Analytics Expert program is designed to hone your expertise in data analytics and help you master the implementation of data science concepts such as data exploration, visualization and hypothesis testing. Our Business Analytics course will train you to apply statistics and predictive analytics techniques in a business environment, transforming you to become job-ready.
Simplilearn’s Business Analytics Expert master’s program will provide you with extensive expertise in data analytics. With this Business Analytics Expert course, you will learn to master statistical and analytical tools such as hypothesis testing, clustering, decision trees, data visualization, regression models, data blending, data extracts, R Studio, ad-hoc analytics, heat map, treemap, Waterfall, Pareto, Gantt charts, and forecasting.
Special focus has been placed on predictive analytics like regression, clustering, and smoothening techniques. The course will also give you expertise in business visualization techniques using Tableau and Power BI. You will gain skills to build visualizations, organize data and design dashboards. The entire learning experience is tied together with demos and projects to be executed on CloudLabs/Virtual Machines. After completing all aspects of the training, you will be prepared for the role of business analyst.
Why become a Business Analytics Expert?
Data is of paramount importance for the digital economy. Analytics professionals are in high demand in the current market is forecast to grow substantially over the next few years.
What projects are included in this Business Analytics course?
This Business Analytics Expert Master’s program includes 15+ real-life, industry-based projects on different domains to help you master concepts of business analytics and intelligence, such as decision trees, data visualization, data blending, and more. Projects are as follow:
Project 1: Examine how large companies like Amazon and Flipkart make use of business intelligence tools to perform category analysis.
Project Title: Category Performance Analysis
Description: According to the Performance Evaluation Program, the subcategories yielding consistent profit for the last four years are awarded the Best Performing Subcategories. Help the manager identify the top subcategories based on profits and use advanced dashboard features to portray a complete picture for subcategory sales.
Project 2: Learn how stock markets like NASDAQ, NSE and BSE, leverage data science and analytics to arrive at consumable data from complex datasets.
Domain: Stock Market
Description: As a part of the project, you will import data using Yahoo data reader for the following companies: Yahoo, Apple, Amazon, Microsoft and Google. Perform fundamental analytics including plotting closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all stocks.
Project 3: See how banks like Citigroup, Bank of America, ICICI and HDFC make use of data science to stay ahead of the competition.
Description: A Portuguese banking institution ran a marketing campaign to convince potential customers to invest in a bank term deposit. Their marketing campaigns were conducted through phone calls, and sometimes the same customer was contacted more than once. Your job is to analyze the data collected from the marketing campaign.
Project 4: Learn how leading healthcare industry leaders make use of data science to leverage their business.
Domain: Health Care
Description: Predictive analytics can be used in healthcare to mediate hospital readmissions. In healthcare and other industries, predictors are most useful when they can be transferred into action, but historical and real-time data alone are worthless without intervention. More importantly, to judge the efficiency and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred.
Project 5: Understand how leading retail companies like Walmart, Amazon and Target make use of data science to analyze and optimize their product placements and inventory.
Description: Analytics is used in optimizing product placements on shelves or optimization of inventory to be kept in the warehouses using industry examples. Through this project, participants learn the daily cycle of product optimization from the shelves to the warehouse. This gives them insights into regular occurrences in the retail sector.
Project 6: Understand how insurance leaders like Berkshire Hathaway, AIG and AXA make use of data science by working on a real-life insurance-based project.
Description: Use of predictive analytics has increased greatly in insurance businesses, especially for the biggest companies, according to the 2013 Insurance Predictive Modeling Survey. While the survey showed an increase in predictive modeling throughout the industry, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling, compared to 69% of companies with less than that amount of premium.