Data Analytics is the science of extracting information, knowledge and wisdom out of raw data. The very existence of companies in the new economy is dependent on how good their analytics team is, at sifting through, making sense of and applying insights derived from the analysis of data.
The Foundations of Data Analytics program has been specially created for India by academicians from Northwestern University, School of Professional Studies and top industry experts.
The program has been designed for fresh graduates and working professionals alike. Professionals who are willing to commit their time towards skill-enhancement and preparation for a career in analytics can participate in this program alongside their current jobs. Unlike the PBA program that covers advanced modeling methods used by a small niche of the industry, the Foundations in Data Analytics program does not include any of the models or tools that are highly mathematical. Instead it focuses on skilling students in the core areas of analytics.
The Structure of the course is as follows:-
- The total program duration is 5 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).
- Weekend classes will be of 4 hours duration at Bridge learning centers.
- Online self-study requirement will be 10 hours every week.
The program is designed to be delivered across three core courses along with one advanced specialization.
- Module 0: Foundation in Statistics
- Module 1: Introduction to Business Analytics
- Module 2: Modeling Methods
All courses in the core and parallel streams are compulsory and students will need to attain a passing grade in each module to ensure certification and participation in the placement process.
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 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 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 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
The student will be awarded with a Certificate from 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.
- Acquire hands-on experience working with leading statistical tools and software packages (such as R ) in predictive modelingtatistical tools and software packages (such as R ) in predictive modeling.
Students applying for this program can be Graduates in any discipline, with an aptitude for numbers and the willingness to learn.
- 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.
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