Business Analytics
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Domain Track Title :Business Analytics

Domain Track Title :Business Analytics

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Business analytics is all about taking in and processing historical business data. Analyzing that data to identify trends, patterns, and root causes. Making data driven Business decisions based on data insights.

This domain deals with hands on in Python, Tableau and Excel and its implementation with various case study and project. Once these tools are understood in its second part it deals with its implementation in popular field like Finance, Marketing and Agriculture. In the last part it has capstone project to provide you overall feeling of domain. This consists only of project and will give you full freedom and flexibility to apply your learning in your own area of interest.

Track Total Credits ( 0-13-9)

Courses Division:

  • (CUBA2219): Data analytics with Python (0+2+2)
  • (CUBA2211): Business analytics through Excel (0+2+1)
  • (CUBA2220): Data analysis with Tableau (0+2+1)
  • (CUBA2210): Marketing Analytics (0+2+1)
  • (CUBA2212): Financial Analytics (0+2+1)
  • (CUBA2213): Agriculture Analytics (0+2+1)
  • (CUBA2221): Business Analyst Capstone (0+0+2)

Domain Track Objectives:

  • To understand and apply data analytics tool in different business and agriculture domain
  • Data extraction: Investigate data to establish new relationships and patterns
  • Predictive Analytic and Predictive Modelling: Analyze the correlation between different variables
  • Logistic Regression: Analyze the possibility of default and generate customer records
  • Problem analysis: Understand and explore problems in business
  • Data interpretation: Use tools such as Excel, Tableau  and python to interpret data
  • Problem-solving: Use analytics to solve business problem.

Domain Track Learning Outcomes:

  • Students can apply data analytics tool in different domain
  • Students will be able to make inferences using different analytics tools.

Career Scope:

  • Big Data Analytics Architect
  • Big Data Engineer
  • Financial Analyst
  • Marketing Analytics Manager
  • Business Intelligence and Analytics Consultant
  • Analytics Associate Metrics and Analytics Specialist Database Administrator
  • Fraud Analyst
  • Retail Sales Analyst
  • Statistician
  • Data Scientist
  • Data Visualization Analyst etc.

Domain Syllabus:

1. (CUBA2219) : Data analytics with Python (0-2-2)
  • Module: I Introduction to Data Analytics
  • Data type, Data Analytics process, exploratory data analysis (EDA), EDA – Graphical Technique, Data analytics conclusion and prediction
  • Module: II Statistics analysis and Business application
  • Introduction to Statistics, Statistical and non-statistical analysis, Major categories of statistics, Statistical analysis – population and sample, Statistical analysis process – Data distribution, dispersion, Histogram, Correlation and inferential statistics, Regression
  • Exercise – Based on case studies
  • Module: III Mathematical and scientific computation
  • Numpy basic – ndarray and basic arithmetic operation, Slicing, coy and view, Mathematical function of numpy,
  • Introduction to Scipy, Scipy sub package Integration and Optimization, Calculation of Eigenvalues, Eigenvector Scipy sub package – Statistics weave and IO
  • Practice project – Solving linear algebra using Scipy, perform CDF and PDF using Scipy
  • Note – Two projects to be submitted (including one practice project)
2. (CUBA2211) : Business analytics through Excel (0-2-1)
  • Module: I Introduction to Business Analytics
  • Analytics - Type and Area of analytics, Custom formatting – Introduction and example, Conditional formatting – Introduction and example, logical functions, VLOOKUP Function, HLOOKUP Function, Index and Offset, Statistical function, Sum IFS, Count IFS, Percentile and Quartile, STDEV, MEDIAN and RANK
  • Exercise and example for every function
  • Module: II Analyzing data and Dash boarding
  • Pivot table – Concept of Pivot table, grouping on pivot, Custom calculation, Slicer. Exercise with examples.
  • Dash boarding concept, Principles of great dashboard, Creation of chart in excel, Chart formatting, Thermometer chart, Pareto chart, Form controls in excel, Interactive dashboard from controls, Chart with checkbox, Interactive chart
  • Exercise and example for every function
  • Module: III Business analytics
  • Histogram, Solver addin, Goal seek, Scenario manager, Data table, Descriptive statistics, Moving average, Hypothesis testing, ANOVA, Covariance, Correlation, Regression, Normal distribution,
  • Exercise and example for every function
3. (CUBA2220 ) : Data analysis with Tableau (0-2-1)
  • Module: I Introduction and Visualization
  • Download and installation, loading data from Excel and user interface, Core topic – Dimension vs measures, discrete vs Continuous, application of discrete and continuous fields, and aggregation in tableau.
  • Creating charts in Tableau – Bar Chart, stacked bar chart, line chart, scatter plot, Dual axis charts, and combined axis charts. Funnel chart and cross tabs, Highlight tables, Maps, measure name and Measure values
  • Module: II Analytics in Tableau
  • Working with metadata, Data types, rename, hide, unhide and sort, default properties of fields. Filters – Dimension, Data, Measure, Visual, Interactive, Data source, Context
  • Applying analytics – Sets, Parameters, Group, Calculated fields, Data Function, Text Function, Bins and Histogram, Sort, reference and trend line, Table calculation, Pareto Chart, Waterfall Chart
  • Module: III Dash boarding and data connection
  • Dashboard in tableau, working layout, Objects in dashboard, making interactive dashboard, action in dashboard, Dashboard for Mobile, Storyline – Case study
  • Modification to data connection, Edit data source, Union, Joins, Data blending, level of detail, Fixed LOD, Include LOD, Exclude LOD, Publish to Tableau Public
4. (CUBA2210) : Marketing Analytics (0-2-1)
Module: I
  • Overview of the marketing process,
  • Transformational role of analytics in marketing,
  • Metrics for Measuring Brand Assets,
  • Customer Lifetime Value (Major emphasis on B2C).
Module: II
  • 2.1 Market Segmentation, segmentation variables,
  • 2.2 Segmentation techniques using cross-tabulation,
  • 2.3 Regression, clustering, and conjoint-analysis (Major emphasis on B2C).
Module: III
  • 3.1 Marketing experiments (like price cut vs sales, advertising effectiveness etc with respect to B2C),
  • 3.2 Sales Forecasting, sales forecasting methods including moving average, exponential smoothing, and regression.
Project/Case Studies

Sales Forecasting and marketing experiments of UMBC shall be a good one for the students to get the exposure. The students shall be divided into several groups according to the product lines of UMBC. Each group shall get the past sales data of the UMBC product lines and using those they have to use the sales forecasting methods to estimate the future. This project data can be used to develop an original case study on UMBC based on the output of the project.

5. (CUBA2212) : Financial Analytics (0-2-1)
Module I
  • 2.1 Quick introduction to Python
  • 2.2 Understanding data in finance, sources of data
  • 2.3 Cleaning and pre-processing financial data
  • 2.4 Exploratory Data Analysis in Finance
Module II
  • Building Models using Accounting Data
  • Understanding stock price behaviour, time series analysis in finance
Module III
  • Forecasting stock prices
  • Credit risk modelling
Case studies/Project -
  • Security Assessment

A framework can be defined as a supporting structure to an entity, which in this case is the investment option. We extend this definition of a framework through a visualization approach that supports a range of key data points of each listed security. First, we introduce and define how that framework is designed; second, we apply it to the creation of profiles for stocks, bonds, mutual funds, and ETFs; and third, we present the resulting collection of profiles. Throughout this chapter we show how the framework is constructed with blueprints for each security type.

  • Portfolio Construction

This Project presents a variety of Portfolio Construction data visualizations that draw from activities top-down managers employ. These activities include determining asset allocations with sector and industry weights before security selection represented with visualizations that show current and proposed asset allocations of a portfolio, sector leadership, and sector alpha factors. In addition, two common risk management techniques, overlap of holdings and stress tests, are included as indirect inputs toward Portfolio Construction activities. These types of techniques manage firm risk from a business perspective to influence Portfolio Construction decisions. They also present clients with rationale for amendments and changes in their portfolio(s).

  • Showcasing Data for Effective Communications
  • This Project reuses the Waterfall chart and introduces the Cascade chart to represent cash flows data for for-profit organizations. Like variations on a theme, this section shows how to apply the Cascade chart technique to a variety of use cases. From Waterfall to Cascade, Summary to Detail, Single to Multi-year, each variation is tailored to serve different needs.
  • Financial statements are known for accuracy and attention to detail and employ a uniform method to gather and present data. The examples in this chapter maintain conventional standards of financial statements while introducing visuals to augment the standard tables. The visualizations reflect the direct and accurate stance of financial statements with the use of straight lines to indicate Summary 213 precise aggregate levels, negative and positive flows, and quantity changes across the years.
  • Visualizations of Performance

This project discusses the redesign of core component visualizations and shows how to integrate them within qualitative information and quantitative tables. The Fund Fact Sheet, for example, combines these redesigned visual components with textual description to convey qualitative information. In contrast, the quantitative table shown in the “Mutual Fund Comparison” section provides a column of visualizations next to corresponding numeric values to compare a short list of funds. In the former case, visualizations vary and represent different data sets with comparison points within the fund. In the latter, the visualizations are consistent and repeat for comparison across a list of funds. Both use cases produce a diverse set of mutual fund data visualizations.

6.(CUBA2213) : Agriculture Analytics (0-2-1)
  • Basic Statistics review,
  • Review of descriptive statistics,
  • Interpretation and visualization of agricultural data
  • Inferential statistics involves generating, from a limited data set, Information about statistical relationships and estimates about a population.
  • Hypothesis testing and ANOVA, Design of experiments.
  • Simple linear regression,
  • Multiple regression,
  • Time series analysis,
  • Growth and instability study in crop productivity,
  • Response surface methodology for input output optimization.
Case study-
  • Statistical analysis on price index of different agricultural commodities -
  • Description:
  • Aprice indexis a normalizedaverage(typically aweighted average) ofpricerelatives for a given class ofgoodsorservices in a given region, during a given interval of time. Price indices have several potential uses. For particularly broad indices, the index can be said to measure the economy's generalprice levelor acost of living. More narrow price indices can help producers with business plans and pricing. Sometimes, they can be useful in helping to guide investment.
  • Study under coverage-
  • Consumer price index, producer price index, export and import price index for agriculture commodities.
  • Agriculture crop production modeling and forecasting
  • Description:
  • Modeling and forecasting of agricultural crop production is essential for policy maker and researcher to predict the future behavior of production or productivity.
  • Study under coverage –
  • Different crop modeling and forecasting for area, production, productivity
7. (CUBA2221) : Business Analyst Capstone (0-0-2)
Gate Process for Project
  • Gate 0: Problem Identification
  • Gate 1: Data Collection
  • Gate 2: Model Development
  • Gate 3: Testing and Validation
  • Gate 4: Publication, Patent, Product
Project Covered -
  • Based on survey data collected from field on interested topic
  • Project based on Aadhar data
  • Project Based on Bank Data
  • College based analysis
  • Credit card related analysis project
  • Diabetes patient analysis
  • Facebook analysis
  • Indigo operation
  • IOT
  • IPL
  • Loan DB
  • Marketing Campaign
  • OYO
  • Stroke data
  • Telcome analysis
  • You tube analysis
Link for Project dataset -

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Our Main Teachers

Amit kumar is post graduate from Xavier Institute of Management, Bhubaneswar. He is certified from Virginia Polytechnic Institute and State University for “Business analytics and artificial intelligence” course. His interest lies in data analytics and business analytics. He is also certified for SQL, Tableau, R Programming and Python. His teaching experience is more than 3 […]