Examinations

For Whom

Those that are not able to fulfill the requirements for direct membership before the commencement of examinations.

Those that cannot follow the online video and would not be able to attend a physical workshop.

Exam Prep

While the Institute might organize lectures/revision classes to help candidates for the first few diets of examinations.

Initial direct members through VIVA, physical/online workshop route are encouraged to start training centres (There are Terms and Conditions).

Exam Modules

The examination would be in three stages

Module I (Certificate Level)

Module II (Diploma Level)

Module III (Professional Level).

There would be three or four examination circles per annum.

Draft Syllabus

Analyic Toolbox I

Mastering applications of Excel, Python and R.

A quick introduction to syntax, variable assignment, and numbers; Functions and Getting Help; Calling functions and defining our own, Booleans and Conditionals; Lists, indexing, slicing and mutating; For and while loops, list comprehensions; external libraries, open, read, and write to files; NumPy arrays, Pandas, Series and DataFrames. Anatomy of an Excel chart; choosing appropriate chart, actual vs target chart; funnel chart, Seaborn package. Charts and statistical procedures in R/R studio. Sharing code workfile.

Analyic Toolbox II

Practical introduction (examples and illustrations) to other software like: Microsoft PowerBI, QlikView, NodeXL, River Logic, SAS, Simul8, SPSS, STATA, Tableau, TIBCO Spotfire, MATLAB, CSVKit, Data Wrangler, etc

Fundamental of Business Analytics

Evolution and development of business analytics; Conceptual reviews (BI, Analysis, Analytics, Data Science, etc) Prerequisites for Data Science; Tools/Skills Used in Data Science; Lifecycle of a Data Science Project. Data management and infrastructure; data curation; data stewardship; issues related to retention and reproducibility; the organizational, policy, and social impacts of big data; etc

Quantitative methods and Business Metrics

Mathematical methods: calculus, integral calculus, matrices and linear algebra, set theory, differential equations, complex numbers, sequences and series, logic and proofs, multivariate calculus, convergence of sequences and functions, and partial differential equations.

Statistical Methods: Stochastic processes, application of descriptive and inferential statistics, elementary probability, univariate random variables, bi-variate random variables, generating functions, statistical estimation theory, hypothesis testing and confidence intervals, regression and time series models, categorical data, data forecasting.

Operations Research and Management Sciences: Linear programming, transportation model, etc

Business Metrics: Sales Metrics, Marketing Metrics, Financial Metrics, Human Resource (HR) Metrics, Project Management Metrics, Product Performance Metrics, etc

Computer and Informatics

The Nature of Information and informatics, Cyborgs and the History of Computers, Modeling and Problem Solving, The Hertz Modeling Relation, Data and Knowledge Representation, Storing the Data Gathered, Digital Number representation, Text and multimedia encoding, Classical Logic, Propositional Logic: Formalizing Natural Language, Cryptography, text frequency analysis, "Freakonomics", Information and Uncertainty, Computing Models: Heuristics and algorithms, Flow chart representation. Examples: Sorting, Hanoi Problem, Artificial Intelligence, Human-computer interaction, robots, and cyborgs; spotting, dissecting, and publicly refuting - false claims and inferences based on quantitative, statistical, and computational analysis of data. Spotting misinformation; causal fallacies; statistical traps; interpreting scientific claims; fake news and social media; refutation techniques; information assurance and cyber-security; Introduction to Information Architecture; navigation, labeling, taxonomy and information personas; Metadata Design and schema; information retrieval, cyberinfrastructure and cloud computing, complex network, data mining, security and privacy.

Analytical Approaches and Techniques I and II

Approaches: A/B testing, data discovery, descriptive analytics, optimization, predictive analytics, prescriptive analytics, etc

Techniques: cluster, comparative, decision tree, factor, machine learning, multivariate, regression, segmentation, sentiment, simulation, time series, etc

Machine Learning: How Models Work; Basic Data Exploration; Load and understand your data; Building Machine Learning Model; Model Validation; Measure the performance of the model; Test and compare alternatives; Under-fitting and Overfitting; Fine-tune model for better performance; Random Forests; Using a more sophisticated machine learning algorithm

Geospatial Analysis: Get started with plotting in GeoPandas; Coordinate Reference Systems; Representing the Earth's surface in 2 dimensions; Making interactive heatmaps and choropleth maps; Manipulating Geospatial Data; Finding locations with just the name of a place; Joining data based on spatial relationships; Proximity Analysis; Measuring distance; Explore neighboring points on a map

Deep Learning: Intro to Deep Learning for Computer Vision; A quick overview of how models work on images; Building Models from Convolutions; Scale up from simple building blocks to models beyond human capabilities; TensorFlow Programming; Introduction to code writing using TensorFlow and Keras; Transfer Learning; Building highly accurate models even with limited data; Data Augmentation; Trick for increasing amount of data available for model training; Stochastic Gradient Descent; Back-Propagation; Build models without transfer learning; Dropout and Strides for Larger Models

Feature Engineering: Baseline Model; Building a baseline model as a starting point for feature engineering; Categorical Encodings; Different ways to encode categorical data for modeling; Feature Generation; Combining data from multiple rows into useful features; Feature Selection; Getting the best set of features for your model.

Practical applications (examples and case studies) using Python or any other tools .

Data Management

Getting Started with SQL and BigQuery; Learn the workflow for handling big datasets with BigQuery and SQL; Select, From & Where; The foundational compontents for all SQL queries; Group By, Having & Count; Get more interesting insights directly from your SQL queries; Order By; Order your results to focus on the most important data for your use case; As & With; Organize your query for better readability; Joining Data; Combine data sources.

Understanding data management process, tools and applications using different platforms: Microsoft Access, Hadoop, Microsoft SQL Server, MongoDB, Neo4, SAP HANA, etc.

Implementing Analytic Project

Practical issues (with simulated and real case studies):

Ethics, governance, data law, research, analytics project/case studies (in data Journalism, behavioral analytics, cohort analytics, collections analytics, visual analytics, cyber analytics, financial analytics, fraud analytics, health care analytics, marketing analytics, pricing analytics, retail sales analytics, risk and credit analytics, supply chain analytics, talent analytics, transportation analytics, operations analytics, and so on)