Live Virtual Learning Data Analyst MasterClass Program

• The learning path in this master’s program is specifically designed to give you the tools you need to be successful in Data Science and help you obtain a competitive edge. By the end of this Data Analyst Master’s Program, you will:
  • Understand essential statistical concepts including measures of central tendency, dispersion, correlation, and regression
  • Master SQL concepts such as Universal Query Tool and SQL command
  • Write your first Python program by implementing concepts of variables, strings, functions, loops, and conditions
  • Understand the nuances of lists, sets, dictionaries, conditions and branching, objects and classes in Python
  • Work with data in Python, including reading and writing files, loading, working, and saving data with Pandas
  • Learn how to interpret data in Python using multi-dimensional arrays in NumPy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and execute machine learning using Scikit-Learn
  • Perform data analytics using popular Python libraries
  • Gain insights on several data visualization libraries in Python; including Matplotlib, Seaborn, and Folium
  • Gain an in-depth understanding of the basics of R, learning how to write your own R scripts
  • Master R programming and understand how various statements are executed in R
  • Understand and use linear and non-linear regression models and classification techniques for data analysis
  • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
  • Gain a foundational understanding of business analytics using Excel
  • Get introduced to the latest Microsoft analytics and visualization tools (Power BI)
  • Grasp the concepts of Tableau Desktop 10, become proficient with Tableau statistics and build interactive dashboards
  • Become an expert on visualization techniques such as heat map, treemap, waterfall, Pareto, Gantt chart, and market basket analysis

   

This Data Analyst MasterClass 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 follows:
 
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 a 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.
 
Domain: Banking
 
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: Healthcare
 
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.
 
Domain: Retail
 
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.
 
Domain: Insurance
 
Description: The 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.