Linux Programming: Installation (Ubuntu and CentOS), Basics of Linux, Configuring
Linux, Shells, Commands, and Navigation, Common Text Editors, Administering Linux,
Introduction to Users and Groups, Linux shell scripting, shell computing, Introduction
to enterprise computing, Remote access.
Introduction to Git/GitHub/Gitlab: Introduction to Version control systems, Creating
GitHub repository, Using Git – Introduction to get commands, Creating projects on
Github/gitlab and managing code repos.
Introduction to Cloud Computing: Cloud Computing Basics, Understanding Cloud
Vendors (AWS:EC2 instance, lambda and Azure: Azure virtual machines, Azure data
factory), Definition, Characteristics, Components, Cloud provider, SAAS, PAAS, IAAS
and other Organizational scenarios of clouds, benefits and limitations, Deploy
application over cloud. Comparison among SAAS, PAAS, IAAS, Cloud Products and
Solutions, Compute Products and Services, Elastic Cloud Compute, Dashboard,
deploy AI and analytics workloads in Cloud environments with sample mini project.
Python Programming: Python basics, If, If- else, Nested if-else, Looping, For, While,
Nested loops, Control Structure, Break, Continue, Pass, Strings and Tuples,
Accessing Strings, Basic Operations, String slices, Working with Lists, Accessing list,
Operations, Function and Methods, Files, Pickling, Modules, Dictionaries, Dictionary
Comprehension, Functions and Functional Programming, Declaring and calling
Functions, Declare, assign and retrieve values from Lists, Introducing Tuples,
Accessing tuples, Visualizing using Matplotlib, Seaborn, OOPs concept, Class and
object, Attributes, Inheritance, Overloading, Overriding, Data hiding, Generators,
Decorators, Operations Exception, Exception Handling, except clause, Try-finally
clause, User Defined Exceptions, Data wrangling, Data cleaning, Load images and
audio files using python libraries(pillow/scikit-learn), Creation of python virtual
environment, Logging in Python.
R Programming: Reading and Getting Data into R, Exporting Data from R, Data
Objects-Data Types & Data Structure. Viewing Named Objects, Structure of Data
Items, Manipulating and Processing Data in R (Creating, Accessing, sorting data
frames, Extracting, Combining, Merging, reshaping data frames), Control Structures,
Functions in R (numeric, character, statistical), working with objects, Viewing Objects
within Objects, Constructing Data Objects, Packages – Tidyverse, Dplyr, Tidyr etc.,
Queuing Theory, Case Study
Introduction to Java Virtual Machine,Data Types, Operators and Language, OOPs
Concepts, Constructs, Inner Classes and Inheritance, Interface and Package,
Exceptions, Collections, Threads, Java.lang,, Java.util, Lambda
Expressions, Introduction to Streams, Introduction of JDBC API.
Introduction to Business Analytics using some case studies, Summary Statistics,
Making Right Business Decisions based on data, Statistical Concepts, Descriptive
Statistics and its measures, Probability theory, Probability Distributions (Continuous
and discrete- Normal, Binomial and Poisson distribution) and Data, Sampling and
Estimation, Statistical Interfaces, Predictive modelling and analysis, Bayes’ Theorem,
Central Limit theorem,Statistical Inference Terminology (types of errors, tails of test,
confidence intervals etc.),Hypothesis Testing, Parametric Tests: ANOVA, t-test, Non
parametric Tests- chi-Square, U-Test Data Exploration & preparation, Concepts of
Correlation, Covariance, Outliers, Simulation and Risk Analysis, Optimization, Linear,
Integer, Overview of Factor Analysis, Directional Data Analytics, Functional Data
Analysis, Predictive Modelling (From Correlation To Supervised Segmentation):
Identifying Informative Attributes, Segmenting Data By Progressive Attributive,
Models, Induction And Prediction, Supervised Segmentation, Visualizing
Segmentations, Trees As Set Of Rules, Probability Estimation; Decision Analytics:
Evaluating Classifiers, Analytical Framework, Evaluation, Baseline, Performance And
Implications For Investments In Data; Evidence And Probabilities: Explicit Evidence
Combination With Bayes Rule, Probabilistic Reasoning; Business Strategy: Achieving
Competitive Advantages, Sustaining Competitive Advantages
Python Libraries – Pandas, Numpy, Scrapy,Plotly, Beautiful soup
Database Concepts (File System and DBMS), OLAP vs OLTP, Database Storage
Structures (Tablespace, Control files, Data files), Structured and Unstructured data,
SQL Commands (DDL, DML & DCL), Stored functions and procedures in SQL,
Conditional Constructs in SQL, data collection, Designing Database schema,Normal
Forms and ER Diagram, Relational Database modelling, Stored Procedures , Triggers.
The tools and how data can be gathered in a systematic fashion, Data ware Housing
concept, No-SQL, Data Models - XML, working with MongoDB, Cassandra- overview,
comparison with MongoDB, working with Cassendra, Connecting DB’s with Python,
Introduction to Data Driven Decisions, Enterprise Data Management, data preparation
and cleaning techniques
Understanding Data Lakes – concepts, architecture and components, Data Lake vs.
Data Warehouse vs. Lakehouse, data storage management, processing and
transformation, workflow orchestration, analytics in Data Lake, case study using Delta
Lake with analytics and AI.
Introduction to Big Data-Big Data - Beyond The Hype, Big Data Skills And Sources
Of Big Data, Big Data Adoption, Research And Changing Nature Of Data Repositories,
Data Sharing And Reuse Practices And Their Implications For Repository Data
Curation,
Hadoop: Introduction of Big Data Programming-Hadoop, The ecosystem and stack,
The Hadoop Distributed File System (HDFS), Components of Hadoop, Design of
HDFS, Java interfaces to HDFS, Architecture overview, Development Environment,
Hadoop distribution and basic commands, Eclipse development, The HDFS command
line and web interfaces, The HDFS Java API, Analyzing the Data with Hadoop, Scaling
Out, Hadoop event stream processing, complex event processing, MapReduce
Introduction, Developing a Map Reduce Application, How Map Reduce Works, The
MapReduce Anatomy of a Map Reduce Job run, Failures, Job Scheduling, Shuffle and
Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features, Real-
World MapReduce,
Hadoop Environment: Setting up a Hadoop Cluster, Cluster specification, Cluster
Setup and Installation, Hadoop Configuration, Security in Hadoop, Administering
Hadoop, HDFS – Monitoring & Maintenance, Hadoop benchmarks,
Apache Airflow/ETL Informatica: Introduction to Data warehousing and Data lakes,
Designing Data warehousing for an ETL Data Pipeline, Designing Data Lakes for ETL
Data Pipeline, ETL vs ELT
Introduction to HIVE: Programming with Hive: Data warehouse system for Hadoop,
Optimizing with Combiners and Practitioners, Bucketing, more common algorithms:
sorting, indexing and searching, Relational manipulation: map-side and reduce-side
joins, evolution, purpose and use, Case Studies on Ingestion and warehousing
HBase: Overview, comparison and architecture, java client API, CRUD operations and
security
Apache Spark: Overview, APIs for large-scale data processing, Linking with Spark,
Initializing Spark, Resilient Distributed Datasets (RDDs), External Datasets, RDD Operations,
Passing Functions to Spark, Job optimization, Working with Key-Value Pairs, Shuffle
operations, RDD Persistence, Removing Data, Shared Variables, EDA using PySpark,
Deploying to a Cluster Spark Streaming, Spark MLlib and ML APIs, Spark Data Frames/Spark
SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka
Connect API, Map reduce, Connecting DB’s with Spark
Business Intelligence- requirements, content and managements, information
Visualization, Data analytics Life Cycle, Analytic Processes and Tools, Analysis vs.
Reporting, MS Excel: Functions, Formula, charts, Pivots and Lookups, Data Analysis
Tool pack: Descriptive Summaries, Correlation, Regression, Introduction to Tableau,
Data sources in Tableau, Taxonomy of data visualization, Numeric, String, Date
Calculations, LOD (Level of Detail) Expressions, Modern Data Analytic Tools,
Visualization Techniques.
Machine Learning:
Introduction to machine learning, Formal learning model – PAC learning, Bias
complexity trade off, The VC Dimension, Non-uniform learnability (Structural risk
minimization and Occam’s Razor and No Free Lunch Theorem), Regularization and
Stability, Model Selection and Validation, Machine Learning taxonomy – Supervised,
Unsupervised and Semi-supervised Learning, practical use cases of Machine
learning, Unsupervised Learning – Clustering (K-Means and its variants), Hierarchical
Clustering, Dimension Reduction (PCA, Kernel PCA, LDA, Random Projections),
Fundamentals of information theory, Supervised Learning with simple and ensemble
learning – Classification and Regression (KNN, Decision Trees, Bayesian analysis and
Naïve Bayes classifier, Random forest, Gradient boosting Machines, SVM, XGBoost,
CatBoost, Linear and Non-linear regression), Time series Forecasting.
Deep Learning:
Introduction to neural networks (Neurons, construction of networks, backpropagation)
, Introducing Modern Practical Deep Networks (Deep Feedforward
Networks, Regularization for Deep Learning, Optimization for Training Deep Models),
Convolutional Neural Networks, Sequence modelling using recurrent neural networks,
Transfer Learning, Autoencoders, Object Detection, Object Segmentation and
Tracking, Concepts of NLP.
Generative AI:
Introduction to transformers, Difference between encoder, decoder and encoder-
decoder architectures, Attention Mechanisms, Overview of BERT, Application of
transformers, Introduction to LARGE LANGUAGE MODELS, Understanding and
handling TEXT DATA, Understand the concept of fine-tuning pre-trained model,
Reward Models and Alignment Strategies, Practical case studies using SLMs and
LLMs, Deployment of LLMs.
