5 months

Online live hours : 45 | Project work hours : 170

Next Batch: 12 Nov, 2016

Rs.55,000 Enroll Now

About Course

Patterns emerge before the reasons for them become apparent.

Business Analytics is the intersection of business and technology. In this course you will study data through statistical and operations analysis, the formation of predictive models and the communication of these results to customers, business partners.

The course also provide knowledge of R, machine learning, base SAS and Tableau. These skills equip to become a successful Data Analytic professional. In-depth knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases. Resume building, mock up interviews are a part of the program to help you navigate to the career options accessible through our wide industry network.

Curriculum Download Brochure

    • Module 1 : Introduction to Business Analytics

      • Predictive Business Analytics
      • Analysis Vs Analytics
      • Extracting business value from the 4 V's of big data.
      • Analytics Life Cycle
      • Moving from Statistician to Data Scientist
      • Types of Analytics

    • Module 2 : Basic Statistics

      • Definition of Data
      • Meaning of Variable
      • Understanding Data Types
      • Measures of Central Tendency in Data
      • Understanding Skewness in Data
      • Measures of Dispersion
      • Understanding Data Distribution

    • Module 3 : Introduction To R Software

      • R Base Software
      • Understanding CRAN
      • RStudio The IDE
      • Basic Building Blocks in R
      • Sequence of Numbers in R
      • Understanding Vectors in R
      • Handling Missing Values in R
      • Subsetting Vectors in R
      • Matrices and Data Frames in R
      • Logical Statements in R
      • Using the Lapply, sapply, vapply and tapply functions
      • Looking at Data in R
      • Simulations in R
      • Date and Time Functions

    • Module 4 : Linear Regression & Logistic Regression

      • Covariance and Correlation in Data
      • Multivariate Analysis
      • Extract the Data in R
      • Univariate Analysis of Data
      • Apply Data Transformations
      • Bivariate Analysis
      • Identify Multicollinearity in Data
      • Identify Hetroscedasticity
      • Modelling of Data
      • Variable Significance Identification
      • Reason for using Logistic Regression
      • The Logistic Transform
      • Logistic Regression Modelling
      • Model Optimisation
      • Understanding the ROC Curve

    • Module 5 : Segmentation

      • Definition of Clustering
      • Understanding the working of Kmeans Algorithm
      • Cluster Size Optimisation vs Definition Optimisation
      • Case for Clustering on Customer Data Set
      • Creating Clusters
      • Using Clusters for Profiling

    • Module 6 : Machine Learning - R

      • What is Machine Learning?
      • Types of Problems and Tasks
      • Features, Models and Design of ML Study

    • Module 7 : Store data analytics and reporting using Base SAS

      • Create reusable pipeline's in Base SAS for ETL, Analysis and Reporting Jobs on a Retail Business Data
      • Extraction, Transformation, Loading Data , Analysis & Reporting

    • Module 8 : Data Visualization using Tableau

      • Visualizing Vanila, Analytical, Structured and Unstructured Data
      • Create Visualisation, Storyboard from Contextual Data
      • Visualizing Unstructured and Structured Data


    • 1. Do you provide placement assistance?

      YES. Sunstone is the India's largest Business School for working professionals and it's existing network includes more than 350 tech companies. Various companies contacts us for our students profiles regularly and the demand is ever growing for these skills. We regularly post such job opportunities to our certified students. Our career coaches also help prepare candidates' resumes and guide them for interview preparation. Having said that, please understand that we do not guarantee any placements however if you go through the course diligently and complete all required projects and problems, you can be almost sure of your chances of success for your next career move.

      Overall you will get access to career assistance service that will include identification of relevant opportunities, job postings and guidance for interviews and resume writing.

    • 2. What are the different business analytics roles in the industry?

      Instructors are industry leaders and have at decades of experience in various leadership domains.

      • Data Analyst
      • Business Analyst
      • Financial Analyst
      •  Marketing Analytics Manager
      • Pricing Analyst
      • Supply Chain Analyst
      • Website Analyst
      •  Fraud Analyst
      • Retail Sales Analyst
      •  Clinical Analyst
    • 3. What are the different companies working in business analytics?

      IT KPO Captive Niche
      Accenture Genpact Fidelity Brainmatics
      Infosys WNS Citibank Fractal Analytics
      TCS Evalueserve M&S Mu Sigma
      CTS EXL Wal-Mart Absolute Data
      Dell 24x7 Target Convergytics
      HP Buxton Barclays Crayon Data
      IBM Adventity RBS Infinite Analytics
    • 4. What kind of jobs am I likely to get after this training?

      Almost all the big corporations need analytics professionals. You can be hired in your own industry at entry/mid/senior level depending upon your experience and the training you have had. IT companies and Analytics KPOs hire people to work on client projects which cut across functional and vertical domains. If you have no prior work experience, you best option is to join one of these.
    • 5. Can senior experienced professionals switch to business analytics?

      Business analytics is the fastest growing industry across the world. As mentioned earlier it needs lot of analytics trained professionals at every level. This is one of those fields that people are switching to even very late in their careers because it is exciting and extremely well-paid with exceptionally bright growth prospects. The data revolution is here!

    • 6. What is the difference between Business Analysis and Business Analytics?

      Business Analysis is mainly concerned with functions and process. It has its own architecture domains (enterprise architecture, process architecture etc.), and typically improves performance by standardising processes, often by bringing technology to bear. In doing Business Analysis projects, the vast majority use 'waterfall' / SDLC type methodologies. Requirements here mean business requirements, which are often met by the way activities are organised (e.g. who needs to approve certain orders) and technology supporting business operations are configured. e.g An example project is defining and standardising business processes across different business units for companies that grow by mergers/acquisition. Other examples of Business Analysis are:
      • Creating a Business Architecture
      • Preparing a Business Case
      • Conducting a risk assessment
      • Requirements elicitation
      • Business Process Analysis
      • Documentation of Requirements
      Business Analytics is mainly concerned with data and reporting. Business analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. e.g. As example analytics project is an investigation of support ticket data within a large IT department to find a disproportionate amount of application support coming to an application. Other examples of Business Analytics functions/tasks are:
      • Using a data warehouse to report past performance.
      • Creating a dashboard to track key performance metrics.
      • Using statistical methods to predict future sales based on past sales.
      •  Running simulations to investigate different scenarios.
      Think of it in terms of before and after: BEFORE you have requirements defined etc, you use business analysis. AFTER the application is built, you can apply analytics to see if it performs as expected.

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