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Predictive Business Analytics Program

OVERVIEW

Data is the new oil, and there is an ever increasing demand for millions of professionals across the world to drill deep into that data, refine information and insights, and sell it to the world at large. As a result, there is a huge increase in demand for Analytics professionals who understand this domain. The science of statistics and machine learning, the technology of modern software and databases (like R, Hadoop, SAS or SQL), and the art of business decision making all come together to allow companies and governments to make sense of the millions of data points that they have access to. In fact, the very existence of  companies in this emerging scenario will depend on the performance of their analytics teams .

Keeping the growing demand of Business Analytics in mind, Bridge School of Management in association with Northwestern University, School of Professional Studies has designed its Predictive Business Analytics (PBA) program. This 11 month certificate program has been exclusively curated for India by the world-renowned faculty from Northwestern University, School of Professional Studies and top industry experts.

To Be Announced
11 Months
Gurgaon
Virtual learning 24×7 weekend classes
with industry facility
9821622722

Some of the key features of Predictive Business Analytics program are:

  • Certification on Predictive Business Analytics from Bridge School of Management & Northwestern University, School of Professional Studies, USA
  • Comprehensive coverage on various analytical tools and concepts like R, SAS, SQL, Tableau, Hadoop etc
  • Advanced Analytics: Learn Text Analytics, Machine Learning, Marketing Analytics, and Risk Analytics
  • Online Material: 24×7 accesses to practice material, videos, quizzes and mock tests to ensure learning efficiency
  • Best in class industry expert trainers from leading companies
  • Capstone Project: A unique learning opportunity to work on a real-life business cases
  • Placement Assisted: Outstanding placement and salary hike records, through our 360 degree placement assistance and recruitment process

The PBA program is designed for both working professionals and recent college graduates. All classes are held over the weekend to ensure working professionals can participate in the program alongside their current jobs.

About Instructor

Anuj Batta

Principal Data Scientist – Matrix Nodes, Professor of Analytics and Data Science – NU Qualification:- EPBA – SDA Bocconi, EPBM – IIM Calcutta, MBA – NIMS, Jaipur, MCA – U.P. Technical University,

Ayan Dutta

Head – Portfolio & Collections Risk, Sbi Card Predictive Business Analytics Qualification:MS tat From Indian Statistical Institute, Kokata

Course Structure

The Structure of the course is as follows:-

  • The total program duration is 11 months.
  • Learning Methodology – Blended Mode with Face to Face Classes on weekends and online learning during weekdays (via advanced state-of-the-art Virtual Learning Environment (VLE).
  • Tools Covered During the program – Advanced Excel, RDBMS, SQL, R, Tableau, SAS.

The program is designed to be delivered across five core courses along with one advanced specialization.

core course
  • PBA Module 0 – Foundations of Statistics
  • PBA Module 1 – Introduction to Business Analytics
  • PBA Module 2 – Modeling Methods
  • PBA Module 3 – Advanced Modeling Methods
  • PBA Module 4 – Analytics Communication and Management
Advanced specialization courses (One Specialization):
  • PBA Module 5 – PBA Specialization – Option 1
  • PBA Module 6 – PBA Specialization – Option 2

All courses in the core, specialization and tools are compulsory and students will need to attain a passing grade in each module to ensure certification and participation in the placement process.

Curriculum

PBA Module 0: Foundations in Statistics

This course introduces students to numerical and visual summarization of data, introductory probability, common probability distributions, sampling distribution and basic concepts of statistical inference. Applications of statistics in business decision making are emphasized through case studies and data analysis. Discussion questions are provided with the goal of facilitating logical thinking and taking quantitative approaches towards solving business problems. Appropriate use of statistical techniques and correct interpretation of statistical findings in business analytics are vital in order to help business make sense of data. This course will help students in developing basic understanding of statistics and drawing inferences based on data. This course will also prepare the pathway for students to delve into big data and predictive analytics in the next modules of the course.

Session Details:

  • Session 1: Descriptive Statistics
  • Session 2: Probability
  • Session 3: Normal Distribution and its Applications
  • Session 4: Statistical Inference

PBA Module 1: Introduction to Business Analytics and R Programming

This course focuses on building the programming and analytics skills necessary to build analytics solutions to business problems. The course begins with a general introduction to the subject of business analytics: what it is, how does it add value in an organization, and what are characteristics of organizations which successfully use analytics to drive operations. Using that as a foundation for later thought and action, the course moves into the fundamentals of programming using the mathematical and analytics language R. Through a mix of online content and student activities, the concepts of how to program in R are delivered. Students perform weekly activities including discussion boards, quizzes and assignments as well as exploration of the larger online data science and analytics community to prepare for applying analytics to business scenarios. The course wraps up with application of the analytics skills to reading and understanding business cases and a review of how hypothesis testing can be applied in business situations to promote better operations and results.

Session Details:

  • Session 1: Fundamentals of business analytics and R
  • Session 2: Say hello to R
  • Session 3: Introduction to programming for analytics
  • Session 4: Descriptive statistics using R
  • Session 5: Reporting and Visualization in R
  • Session 6: Data manipulation using R
  • Session 7: Inferential Statistics using R – I
  • Session 8: Inferential Statistics using R – II

PBA Module 2: Modeling Methods

This course serves as an introduction to statistical analysis as used in predictive modeling to support business decision making. Students will learn basic techniques in the formulation, parameterization, and selection of the right model for the right business problem. This course covers different types of analytics and a variety of statistical topics, including multiple linear regression, logistic regression, correlations and goodness of fit. Through the use of practical examples with hands-on training, discussion of the major decisions focused on making sense of data, integration of fact-based predictions into everyday decision making, the student will be exposed to a comprehensive, managerial and practical approach to predictive modeling techniques.

Session Details:

  • Session 1: Actionable Insights, Statistical review, and Simple Linear Regression
  • Session 2: Multiple Linear Regression
  • Session 3: Regression Diagnostics
  • Session 4: Variable Selection Methods
  • Session 5: Logistic Regression
  • Session 6: Classification Trees
  • Session 7: Regression Trees
  • Session 8: Multinomial Logit or Multinomial Logistic Regression

PBA Module 3: Advanced Modeling Methods

Advanced Modeling Methods gives business analysts the foundation and tools to implement various prediction algorithms that will allow them to make better data driven decision for their business. This course places emphasis on modeling techniques to gain insight into hidden data patterns while using the technology stack of R. Topics covered will include various classification algorithms such as Cluster Analysis, Neural Networks, Discriminant Analysis, and Support Vector Machines. Students will also be introduced to various topics in Time Series, and other statistical based methods including Principal Component Analysis and Affinity Analysis. Lastly, students will learn about the structure of Networks and how tools such as Sentiment Analysis can provide valuable insight into their customer base.

Session Details:

  • Session 1: Cluster Analysis
  • Session 2: Discriminant Analysis
  • Session 3: Support Vector Machines
  • Session 4: Principal Component Analysis
  • Session 5: Neural Networks I– Introduction to Neural Networks
  • Session 6: Neural Networks II– Calibration, Propagation, and Hidden Layers
  • Session 7: Time Series I– Decomposition and Regression
  • Session 8: Time Series II– Autoregressive and Moving Average Models

PBA Module 4: Analytics Communication & Management

In this section we will focus the keys to becoming a predictive analytics enabled organization. Next we will examine how to take the results from data mining and predictive analytics to create powerful and convincing visual analytics. We will focus on creating info graphics, PowerPoint presentations, Dashboarding software and writing simple code in R to create powerful charts and infographics. We will also learn methods to create lasting impact on the audience while designing such infographics, visuals and dashboards. We will learn different examples from the industry on the best practices. Lastly, we will examine how an organization can use predictive analytics as a competitive advantage and the privacy and security concerns that may accompany that transformation.

Session Details:

  • Session 1: The Analytics Project Lifecycle
  • Session 2: Pre-Analysis
  • Session 3: Data Gathering
  • Session 4: Execution
  • Session 5: Post-Analysis and Adjustment
  • Session 6: Data Visualizations
  • Session 7: Visualization Infrastructure

Session 8: Additional Data Visualizations

ADVANCED SPECIALIZATION COURSES

PBA Module 5: PBA Specialization – Option 1

This course serves as an introduction to statistical analysis as used in marketing analytics to support marketing decision making. Students will learn basic techniques in the formulation, parameterization, and selection of the right model for the right marketing problem. This course covers different types of analytics and a variety of statistical topics, including conjoint analysis, consumer choice modeling, multidimensional scaling, factor analysis, and experimental design.

Session Details:

  • Session 1: The Strategic Marketing Process and Segmentation Analytics
  • Session 2: Product Design via Conjoint Analysis
  • Session 3: Conjoint Simulators and Choice Models
  • Session 4: Product Positioning via Perceptual Mapping
  • Session 5: Competitive Analysis
  • Session 6: Pricing and Marketing Mix Models
  • Session 7: Digital Marketing Analytics: Experimentation and Attribution Modeling
  • Session 8: Group Project Report and Presentations

PBA Module 6: PBA Specialization – Option 2

This course builds on the previous introductory material to provide analysts with the tools to quantify, mitigate, and manage risk in a business setting. Students will identify business risks, design risk analyses, and use technologies—primarily the R programming language—to run risk analytics and simulations.

Session Details:

  • Session 1: Introduction to the Concept of Risk
  • Session 2: Discrete and Continuous Probability Distributions
  • Session 3: Joint Distributions and Convolutions
  • Session 4: Factor Models
  • Session 5: Principal Component Analysis
  • Session 6: Advanced Time Series: ARIMA / GARCH Models
  • Session 7: Simulation and Monte Carlo Methods (Advanced)
  • Session 8: Bayesian Networks / Event Tree Analysis (Advanced)

Outcome

The student will be awarded with a Certificate in Predictive Business Analytics, jointly from the Northwestern University, School of Professional Studies and Bridge School of Management. The certification will require a minimum level of academic performance and also cover areas such as attendance, discipline and adherence to school policies.
By the end of the program, students will be able to:

  • Strategically navigate business data and understand patterns therein
  • Apply analytics techniques and tools to real-world business contexts for improved decision making
  • Assess the strengths and limitations of analytics and predictive modeling techniques for different business applications and varying data conditions
  • Acquire hands-on experience working with leading statistical tools and software packages (such as R®, SAS®, Tableau ®, SQL and multiple Big Data tools and techniques) in predictive modeling and the visual analytics of results
  • Effectively communicate the actionable insights stemming from analytical work to multiple stakeholders

Selection

Eligibility Criteria
Students applying for this program can be Graduates in any discipline, with an aptitude for numbers and the willingness to learn.

Selection Process
Step 1: Candidates will have a short self-assessment quiz.
Step 2: The quiz will be followed by a personal interview with a senior member of the academic team.
Step 3: All successful candidates in Interview and Assessment quiz will be issued the Letter of Offer from the Registrar, Bridge School of Management.

Center Info

1st Floor, Tower B Infinity Towers,
DLF Cyber City
Gurgaon – 122001
Contact – 9821622722

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