Business Analytics with Excel, SAS and R

Download Course Content

Quick ViewEligibilityFeaturesFAQCourse Syllabus

Course Overview
The training is designed to provide knowledge & skills to become a finest Business Analyst by detailing from the very basics to complex concepts & models of the statistical programming language R, covering – data analytics, various types of regression techniques, classification, decision trees, clustering, time series modeling, market basket analysis, modern machine learning and more. Well-designed challenging, practical and focused hands-on exercises are embedded as part of the course.

WHAT IS ANALYTICS
==========================
DEFINATION #1
Analytics is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis, used by companies committed to data-driven decision making.

DEFINATION #2
Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.

APPLICATION OF ANALYTICS

  • Portfolio analytics
    A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.
  • Risk analytics
    Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers.
    Credit scores are built to predict individual’s delinquency behavior and widely used to evaluate the credit worthiness of each applicant. Furthermore, risk analyses are carried out in the scientific world and the insurance industry
  • Digital analytics
    Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automations.
    This also includes the SEO (Search Engine Optimization) where the keyword search is tracked and that data is used for marketing purposes.
    Even banner ads and clicks come under digital analytics. All marketing firms rely on digital analytics for their digital marketing assignments, where MROI (Marketing Return on Investment) is important.

ANALYTICS POPULAR SOFTWARE AND TOOL

  • Statistical software are programs which are used for the statistical analysis of the collection, organization, analysis, interpretation and presentation of data.
  • R (STATISTICAL PROGRAMMING LANGUAGE)
    R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing the R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is a GNU package .he source code for the R software environment is written primarily in C, FORTRAN, and R.
    R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. While R has a command line interface, there are several graphical front-ends available.
  • SAS (STATISTICAL ANALYSIS SYSTEM)
    SAS is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.

Who can join this course

This course is for students pursuing their graduation/post-graduation and for working professionals who have completed their graduation in any field. There are no other prerequisites but you do need to have a quantitative bent of mind. For those who hate mathematics or numbers, while we shall try to make you as comfortable as possible, the analytics field itself may prove to be a challenge for you.

  • Engineering and IT Students – B.Tech. / BE, BCA, MCA, B.Sc.-IT, M.Sc.-IT, B.Sc.-Statistics, M.Sc.-IT Statistics
  • Commerce & Finance Students – B.Com / M.Com, Economics Graduates, MBA or BBA
  • Highly recommended for people aspiring for jobs that required data handling – Research, Marketing, IT Services, Big Data & more
  • People who are already employed, but want to up skill themselves in the domain of Business Analytics
  • As a prerequisite, participants should already have attended the Introduction to Business Analytics course or have knowledge of the basics of Business Analytics.

After completing this course, the learners will be able to:

 

  • Understanding of basic statistical concepts and types of data
  • Understanding of sampling techniques
  • Understanding of frequency distributions and measures of central tendency, dispersion and shape
  • Knowledge of the one-way analysis of variance and correlation
  • Knowledge of linear regression and linear programming
  • Optimize business situations that involve whole numbers, such as employees to deploy
  • Optimize business decisions that take multiple input variables to predict between two possible outputs
  • Model decisions under a variety of future uncertain states, depending on the decision maker’s proneness or aversion to risks
  • Compute correlation where, at first glance, there seem to be none – correlation between data points in a time series
  • Compute the regression model for time series data that has correlation within itself
  • Optimize business situations where two variables do not move in a linear fashion
  • Test hypothesis for experiments involving different treatments.
  • Model continuous outcomes that depend on more than one input variable.
  • Group data points dynamically based on the similarities among the members of each group
  • Master a data mining concept i.e. is combining the data from different sources, cleansing the data and preparing the data for analytics.
  • Understand how to use statistical techniques in real time scenario.
  • Complete understanding of predictive modeling concepts.
  • Learn how to build complex statistical model, how to test it and how to deploy it.
  • How does the certificate process work?

Within each of our certified courses we have graded projects and exams. To receive the certificate you need to complete and submit these graded projects and exams and achieve the passing mark to receive the certificate.

  • Do you offer placement services?

We offer you placement assistance in terms of CV preparation and interview preparation. If and when corporate approach us for filling their openings we shall ask you to apply.

  • What is analytics?

Please visit website pages for more info

  • How does a self-paced course work?

In this version once you have enrolled for the course and made the payment you shall be able to access the videos and course content on the Learning Management System (LMS) within one working day. You can login to the LMS by clicking on the student login button on the website and start viewing the content at your own time and pace. Each course will be divided into various modules which you will go through in a serial manner at your own pace. You can leave the module and start again from the point at which you left off. Instructions will be provided regarding what needs to be done in each module.

The modules will have videos, interactive presentations and other material. You will need to complete and submit the graded project and final exam through the LMS itself. The exam scores will be available immediately along with the correct answers while we shall grade the project and provide the feedback to you at a later date.

You can access the content through the LMS whenever you want. You have a life-time access to the content.

You can engage in discussions on the course forum with fellow students and the trainers. You can also email your queries to the course trainers and they shall reply in 24 working hours.

  • How does a online classroom course work?

This is a live, instructor-led, online classroom course where you shall login to a virtual classroom, which will be led by the course trainer, who will share his screen for the participants to view. You can interact with the trainer and fellow students from the comfort of your own through the audio or chat functionality or by sharing your screen. These sessions will be held on specific days and times for the duration of the course and you will need to be online at that time. You can even use mobiles and tablets to login to the session.

If you miss a session, then you can view a recording of the same from our LMS. Each session will be recorded and put on the LMS for you to re-visit at your convenience at anytime and from anywhere. You have life time access to the same. You will also have access to the presentation materials and exercise files on the LMS.

You can engage in discussions on the course forum with fellow students and the trainers. You can also email your queries to the course trainers after the session and they shall reply in 24 working hours.

  • How is an online course better than a classroom course?

Online course and offline/classroom course are essentially just two means towards the same goal. Worldwide the growing trend is to learn online at your own convenience at a cheaper cost.

The online, live instructor led online classrooms provide the advantages and interactivity of a physical classroom without the headache of traveling. The online self-paced courses offer you the option to learn anywhere, anytime at your own pace and you can also interact with fellow students and trainers through the discussion forums or through email.

  • Do I need to follow specific timelines to complete the self-paced course?

For the self-paced courses there are no deadlines but we encourage you to follow the time guidelines mentioned for each module of the course. The quizzes will be internally timed though there is no particular time to take them. You will have life-time access to the course

  • Who are your trainers?

Our trainers and content developers are all highly experienced in the industry and have a passion for teaching. Some of the sessions will be taken by very experienced and well placed guest trainers who are still working in the industry. This will also help you build a network in the industry.

  • Can I leave a batch in between and then re-join the next batch from where I left?

For the online classroom course we don’t encourage this but under unavoidable circumstances we shall consider your written request. Please note that accommodating new enrollments would be the priority for any new batch; post the new enrollments if there is space available only then will we be able to accommodate the previous batch students.

  • What is the refund policy?

For the online classroom courses, if you decide to cancel your enrollment you can do so anytime till 6 days post the start date and your entire fees shall be refunded to you in 15 working days’ time from the date of intimation to us. After this time we shall not be able to refund your fees.

For the online self-paced version of the course you can cancel anytime till 3 days post your enrollment for the course for a full refund of the fees. Post this we shall not be able to offer you a refund.

  • How many students in a batch?

We shall restrict the maximum number of participants in any batch to 25.

  • What if I miss a class?

If you miss a session, then you can view a recording of the same from our LMS. Each session will be recorded and put on the LMS for you to re-visit at your convenience at anytime and from anywhere. You have life time access to the same. You will also have access to the presentation materials and exercise files on the LMS.

  • What software tool do you use for the SAS courses?

SAS offers the free to download SAS University Edition which you can install in your own computer for anytime access. You will need a minimum of an Intel i3 processor or better and Windows 7 or 8 (or OSX) or better to install the same.

  • What are the system requirements for the online course?

You just need to have a decent internet connection [512kbps-1mbps] and a headset with mic to attend the online classes.

  • How can I make payments?

We offer you all kinds of secured payment options like credit cards, debit cards, net banking, online bank transfers, cheque payment and Paypal. If you do not see your preferred payment options kindly contact us here.

For cheque payments and online bank transfers your access to the course will start when the payments reflect in our account. You should send us a copy of the cheque or the online bank transfer receipt at info@ibat.in with your name, address, phone no. and course enrolled for details.

 

Introduction to Analytics

  1. What is Analytics?
  2. Popular tools
  3. Analytics use in different industries
  4. Analytics methodology

Business Analytics:

  1. Basic Statistics, Introduction to Measures of Central Tendency, Variation and Shape
  2. Types of data & variables
  3. Probability
  4. Distribution Types
  5. Introduction to Confidence Interval Estimate
  6. Hypothesis Testing
  7. Sampling Distribution
  8. Descriptive Statistics
  9. Tabular and Graphical Method
  10. Summarization Method
  11. Introduction to Probability and Distribution
  12. Different types of Regression Procedures
  13. Z test, T Test, Anova Test, Chi Square Test, F Test
  14. OLS regression – Introduction to Data modeling
  15. Developing and Validating Linear Regression Model
  16. Developing and Validating Multiple Regression Model
  17. Logistic regression

Basic analytics techniques using R or SAS with case studies (choose one)

  1. Introduction of R or SAS
  2. Data extraction and exploration using R or SAS
  3. Understanding of different packages
  4. Data visualization and analytics

Predictive modeling techniques using R or SAS

  1. Linear and Multiple regression with Insurance case study
  2. Logistic Regression with Banking case study
  3. Cluster analysis with Banking case study
  4. Time Series Modeling with Automobile case study
  5. Market Basket Analysis with Retail case study