ABOUT BSE INSTITUTE LIMITED ABOUT IIT MADRAS

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2 ABOUT BSE INSTITUTE LIMITED BSE Institute Limited (BIL) is a wholly owned subsidiary of BSE Ltd. BIL inherits from BSE knowledge and insights into the capital markets industry, garnered over the past 143 years. BSE Institute Limited offers a bouquet of courses related to financial markets for students interested in achieving and upgrading their skills in this field, ranging from Bachelors program from Mumbai University, International PG programs and more than 100 short term certification courses. BIL is also involved in some special initiatives like BFSI Sector Skill Council and Zone Startups. BSE Institute Limited was assessed and certified with ISO 9001:2008. ABOUT IIT MADRAS Indian Institute of Technology Madras is one among the foremost institutes of national importance in higher technological education, basic and applied research. In 1956, the German Government offered technical assistance for establishing an institute of higher education in engineering in India. The first Indo-German agreement in Bonn, West Germany for the establishment of the Indian Institute of Technology at Madras was signed in The Institute was formally inaugurated in 1959 by Prof. Humayun Kabir, Union Minister for Scientific Research and Cultural Affairs. The IIT system has sixteen Institutes of Technology. The first of these to be instituted are at Kharagpur (estb. 1951), Mumbai (estb. 1958), Chennai (estb. 1959), Kanpur (estb. 1959), Delhi (estb. 1961), Guwahati (estb. 1994) and Roorkee (estb. 1847, joined IITs in 2001). IIT Madras is a residential institute with nearly 550 faculty, 8000 students and 1250 administrative & supporting staff and is a self-contained campus located in a beautiful wooded land of about 250 hectares. It has established itself as a premier centre for teaching, research and industrial consultancy in the country. The Institute has sixteen academic departments and a few advanced research centres in various disciplines of engineering and pure sciences, with nearly 100 laboratories organised in a unique pattern of functioning. A faculty of international repute, a brilliant student community, excellent technical & supporting staff and an effective administration have all contributed to the pre-eminent status of IIT Madras. The campus is located in the city of Chennai, previously known as Madras. Chennai is the state capital of Tamilnadu, a southern state in India.

3 ABOUT BUSINESS ANALYTICS PROGRAM COURSE STRUCTURE Business Analytics - The Next Big Thing With a population size in billions, we as a country are synonym to ever growing talent pool; catering to the needs of each and every sector possibly known to human kind. This has resulted in India becoming an absolute hub for all those foreign companies looking to find one of the best outsourcing places. The rapid developments in the field of business analytics shall change the world of businesses; as well as business models; and help them improve exponentially too. If you want to succeed in any industry, you need to study newer trends & improvise in your processes. There is practically no sector which has remained untouched from the reach of business analytics. Data analysts, with the right set of skills and expertise; are in demand in a wide range of industries where businesses are realizing the potential of enabling analytics in their organization. Indian Institute of Technology Madras is one among the foremost institutes of national importance in higher technological education, basic and applied research. The Institute has sixteen academic departments and a few advanced research centers in various disciplines of engineering and pure sciences, with nearly 100 laboratories organized in a unique pattern of functioning. Foreseeing the future of analytics industry, Both BSE Institute and IIT Madras endeavor to provide world-class education to the students through collaborative programs that shall lead to opportunities of joint research and facilitate industry exposure for students of both the institutions. The use of business analytics has grown exponentially in all areas, including financial services, healthcare, government, retail, e-commerce, media, manufacturing, and the service industry. This has led to an increase in the demand for employees with an analytical approach to management who can utilize data, understand statistical and quantitative models, and are able to make better data-driven business decisions. In association with Centre for Continuing Education - IIT Madras, Certificate Program in Business Analytics enables working professionals with in depth understanding of the key technologies used in analytics, viz. data mining, machine learning, visualization techniques and statistics. The program is designed on a schedule that minimizes disruption of work and personal pursuits, spread over one year (part-time) focusing on overview of the field of analytics so that you can make informed business decisions. India s analytics industry is currently estimated to be $2.03 billion annually in revenues & it is expected to nearly double by 2020, a study conducted by Analytics India Magazine and AnalytixLabs has found. Thus, it is set to bring equal opportunities for all - starting from fresher to experienced professionals in the space. Semester - I Introduction to Data Sciences Concepts of business intelligence and business analytics Simple data retrieval vs data processing Role of data scientists vs data analysts Hidden facts in data, unearthing the facts Evolution of data science, popular techniques & algorithms Concepts of predictive and prescriptive analytics Representing the data in a common model Concepts of data clusters, data distribution, time series data, text processing Introduction to Data Management Identifying the OLTP data to be used for analytics (Core banking, ATMs, Net banking, loans, cards etc.) Sizing the transactions in terms of number of records and size Ingestion of data to data lake (structured and unstructured data from banking subsystems, ETL process, initial data load and incremental data load) Abstraction of data, use of meta data Typical architecture of a BA solution for banks Hardware and software required for managing large data De normalization, regular integrity checks, moving Big Data and NoSQL data bases, scaling out thru hardware/clusters

4 Introduction to Business Metrics Need for business metrics (for different roles of people in banks) Real time metrics, near real time metrics, offline metrics (enabling spot decisions and approvals in banking sector) Decision enabling and decision making based on data (analysis of past data of account holders, pattern of the transactions, volume of transactions, linking account holder-relatives-business) Samples of 100+ business metrics in banks How to arrive at business metrics from raw master and transaction data Aggregate metrics - count of transactions, sum of transacctions, distribution of transactions based on types How to arrive at a set of business metrics for a specific bank, design the metrics, implement the metrics, getting feedback Introduction to Applied Business Statistics Customer-based metrics, employee based metrics, branch/atm/loan type based metrics, geography based metrics Patterns of new account openings, account closures, loan repayments, loan defaults Customer profiling - age bukets, profession buckets etc., gradation of customers using metrics Statistics using R Preparing and loading data for R Identifying the influencing data and resultant data Identifying specific packages to run on core bank transaction data, ATM data, loan data etc. Examples on Exploratory analysis - numerical summary, rule, plots, multi variate Examples on standard mathematical analysis - mean/median/percentile/variance, distribution based on frequency/poisson/sampling Examples on statistics analysis - inferential, correlation, error types Running regular statistics packages for average, 98th percentile, etc. on core bank trsnactions data Running linear regression on ATM data Running neural packages on netbanking data Semester - II Regression & Classification for Business Applications Introduction to CART Logistic regression Campaign based metrics on banking products, measuring campaign success, measuring new sell vs up sell success Employee training metrics, customer education metrics Metrics on technology improvement success Trend analysis - transactions, customers, branches etc. Decision tree classification Bayesian classification Machine Learning What cannot be done by manual analysis Supervised learning Training data, test data - banking customer footprints, ATM and net access data Linear regression KNN Applying Clustering, representation learning Unsupervised learning Introduction to artificial neural networks

5 Tools & Techniques for Data Visualizatin & Communication Getting data to data mart for faster visuals Metabase open source, Apache superset open source visualizers Building dashboards using individual charts Implementing security on visual data visibility COURSE DETAILS Course Structure: One year duration I 2 Semesters I Alternate Weekends session Eligibility: Plotting data based on classifications/ buckets, time, trending Drill downs to provide more details Time Series Modeling Concepts and examples of Curve fitting, segmentation, classification Preparing raw data with right unit of time Models - auto reggressive, moving average, integrated Apply time series modeling on Automatic alerting based on data dashboards ATM data Prediction and forecasting thru Apache Spark ML Lib packages Diploma holders / Graduates from any recognized University. Candidates from Engineering / MCA /BCA / Mathematics / Economics background will get an edge over the remaining applicants. Freshers can also apply. Admission Procedure: The admission committee takes into account candidate s educational qualification and work experience (If any). On successful evaluation of relevant credentials, an offer letter is issued to the candidate stating the admission guidelines and fee structure. Candidate has to accept the offer, and pay the admission fee to confirm the enrollment for respective PGP program. Project Work Using docker containers Ingest large sets of data in postgres and Apache kudu Carry out exploratory analytics - use of impala Build aggregates and data marts Build visuals and dashboards using metabase and apache superset Present the business metrics CAREER PATH 35+ VP YEARS DIRECTOR 15+ YEARS ANALYST 0-2 YEARS SENIOR ANALYST 2-4 YEARS MANAGER 4-6 YEARS AVP 6-10 YEARS AVERAGE SALARIES IN ANALYTICS Among the highest paying jobs globally!

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