Python for Data Science

Introduction to Programming in Python. Includes Variables, Expressions, Statements, Functions, Conditionals, Recursion, Iteration, Arrays, Strings, Lists, Tuples, Dictionaries, Files and Exceptions, Classes, Objects, Inheritance. Manipulating, processing and cleaning data in Python.  Includes basics of Python libraries such as NumPy, Matplotlib, Pandas. Includes data loading, wrangling, visualization, aggregation, Advanced Numpy.

NLP using Python

This course teaches you how to manipulate and analyze language data using key concepts from NLP, various NLP algorithms and the NLTK toolkit. Accessing text corpora and lexical resources, processing raw text, regular expressions, tokenization, POS tagging, N-grams, classifying text, decision trees, naive Bayes, MaxEnt, information extraction, NER, CFG, syntactic parsing, semantic parsing, statistical parsing, dependency grammars, NLTK..

Deep Learning using Python

Neural Networks, Introduction, Activation Functions, Network Architectures, CNN, RNN, LSTMs. Applications in Computer Vision, Text Processing. Deep Learning with Keras and Google Colab.

Reinforcement Learning

Multi-armed bandits, Finite Markov Decision Processes, Optimal policies and value functions, Dynamic Programming, Policy evaluation, Policy improvement, Monte-Carlo methods, Temporal difference learning, N-step bootstrapping, Planning and learning with tabular methods, Approximate solution methods.

C Programming Fundamentals

 Introduction; Constants and Variables; Types, Operators, Expressions; Control flow and Iteration Statements; Functions and Program Structure; Pointers and Arrays; I/O; Files ,Structures and Unions, Bitwise operations.

C++ Programming Fundamentals

Introduction, Classes and Members, Objects, Overloaded Functions, Friend Functions, Overloaded Operators, References, Constants, Type Casting, Constructors, Destructors, Static Members, Inheritance, Templates, Multiple Inheritance, Implementation of  Data Structures: Arrays, Queues, Stacks, Linked Lists.

Hundred Algorithms

The focus will be on algorithms, data structures and problem solving. This will be a tutorial style course with emphasis on problem solving, rather than mere description of usual textbook algorithms and data structures. The course will involve solving more than 100 problems in class.

Cloud Computing with AWS & Azure

IT Platform for a cloud based service. HDInsight, SageMaker, Data Factory, Functionalities, Architecture, Integration, Streaming: Event and Analytics. Kafka and Spark architecture, SDLC, Integration with CloudConfluent, Azure Databricks, Data Services, No-SQL, Data Lakes. NFR-Security, Performance, Scalability, Case Studies.


Applied Machine Learning Using Python

This course is for those  who wish to perform various machine learning tasks using open source libraries. Covers classification, linear and logistic regression, clustering, k-means, Naive Bayes, decision trees, kNN, association rules, dimensionality reduction. Applications include topic modeling, sentiment analysis, recommender systems using Python libraries such as NumPy, SciPy and Scikit -learn.

Web Development With Python

Prerequisites: Knowledge of Python Programming Fundamentals. In this course, you will learn how to build robust web applications with Python. You will go through the basics of Web Design with HTML, CSS, Javascript and MySQL. You will learn two Python Frameworks, Django and Flask. At the end of this course, you should be able to build a web application from scratch.

Algorithms and Data Structures Fundamentals

Arrays, Linked Lists, Trees, Heaps, Binary Search Trees, Graphs, Analysis, Asymptotic Notations, Sorting, Lower Bounds, Binary Search and variants, Binary Search Trees, Divide and Conquer, Dynamic Programming, Greedy Method, Amortized Analysis, Disjoint Sets, Graph Algorithms Studying relative hardness of problems, NP-Hard and NP-complete Problems. Reduction Proofs. Coping with NP-Completeness, Approximation algorithms, Branch and Bound Algorithms. 

API Microservices

Architectures, Patterns, REST,  API Integration, API Security, API Performance, API monitoring and deployment, API monitoring and deployment, Tools for API Gateway, Postman, Prometheus, Grafana, Newman, Jenkins Integration.

R Programming Fundamentals

Introduction, R UI, RStudio, Functions, Arguments, Scripts. Objects: Vectors, Attributes, Matrices, Arrays, Classes, Factors, Lists, Frames. Operations: Selection, Modification, Logical subsetting. Complexity, Loops, Efficiency Issues.

Algorithms for VLSI Physical Design

Algorithmic Foundations. CAD Tools flow. Partitioning.  Floorplanning. Placement.  Routing. Compaction. Layout Analysis and Verification. Design Rule Checking, Connectivity extraction, Device and parameter extraction, LVS..

Big Data with Apache Spark

Basics of Streaming; Introduction to Spark; Spark Architecture; Installation of Oracle V-Box on windows and configuration for Ubuntu-Linux VM; Spark Installation, configuration; Spark API; RDD; SQL and Data Frames; GraphX; MLib; Structured Streaming; Integration of DataBricks-MLFlow (open-source ML library); Clustering: Spark Examples for API demonstrations with Python; Case Study.

Data Science using R

Covers Data Transformation, Exploratory Data Analysis, Data Import, Data Organization – Tidy Data, Handling Multiple Tables, Strings, Factors, Building Models using R packages such as ggplot2, dplyr, readr, tidyr, stringr, forcats, lubridate, magrittr, modelr, purrr.

Summer Training

Bundled course offerings for Interview preparation in June-July 2024.