Blockchain
Ethereum, Blockchains, Bitcoin, Hyperledger, Smart contracts, Fabric, Composer, Core Backend development ( Agile, MVC framework, API and Dynamic programming), Cryptography
Cyber Security
CIA Triad, Authentication & non-repudiation, Vulnerability, Threat, Risk, Assessment & Tools, Kali Linux & Tools, Nexpose, Qualys, Open VAS, Network Reconnaissance & Scanning, Port Scanning techniques (NMAP, ZENMAP), Enumeration, MIM, SQL Injection and Social Engineering Attacks, Metasploit, BURP SUITE and OWASP, Concepts of Cryptography, BCP (Business Continuity Planning) and Disaster Recovery, Security Auditing & Compliance'. Introduction to Cyber Security, Networking Concepts Overview , Security Management, Network Security, System and Application Security, and OS Security.
SAS
Introduction to SAS, overview of base SAS software, data management facility, how SAS works, reading raw data into SAS, writing your first SAS program, reading data from a dataset, SAS informats and formats, SAS functions Unix-SaS, an introduction to arrays and array processing, by - group processing, overview of methods for combining SAS data sets, SAS procedures, introduction to proc sql, an introduction to SAS macros, the output delivery system (ods), & introduction to diagnosing and avoiding errors.
Cloud Computing
Introduction to Cloud Computing,Adopting the Cloud, Exploiting Software as a Service ( SaaS ), Delivering Platform as a Service (PaaS), Deploying Infrastructure as a Service ( IaaS ), Building a Business Case & Migrating to the Cloud. Cloud computing models, look into the threat model and security issues related to data and computation outsourcing, and explore practical applications of secure cloud computing. Using the confidentiality, integrity, and availability of data (CIA) model we will examine the threats and security implications to befall poorly established and maintained cloud computing environment.
Artificial Intelligence
Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning, Constraint Satisfaction Problems, Classical Search, Automated Planning, Optimization Problems, Adversarial Search, Probabilistic Models.
Data Warehousing
Data Warehousing topics covered are Introduction To Dataware Housing, Core Java Language Environment, Java Fundamentals, Essentials of Object-Oriented Programming, Writing Java Classes ackages, Exception Handling, I/O Operations in Java, Multithreaded Programming, Developing Java Apps, Network Programming, Java Util Package / Collections Framework, Generics, Inner Classes, Abstract Window Toolkit , Swing Programming, JAVA Frameworks, & more.
Data Analytics
Data Analytics topics covered are Introduction to Data Analytics, Understanding Text analytics, Sentiment analysis and social network analysis using case studies and hands on exercises using tools, Big Data, Enterprise Master Data Management, Data Mining for Business, Hadoop Cluster, Hadoop Vs. Traditional systems, Using Big Data for decision making, innovation and increasing productivity across functions like marketing, supply chain and operations and finance, Understanding Impact of Big Data Analytics on business performance using case studies, Big Data solutions and challenges on Cloud, & Big Data Visualization using Tableau.
Big Data
Big Data Overview, Use Cases, Data Analytics Process, Data Preparation, Tools for Data Preparation, Using SQL and NoSql DB's, Usage of Tools, Data Analysis Introduction, Classification, Data Visualization using R & more.
MATLAB
Introduction to MATLAB, MATLAB Architecture, Scope of MATLAB, MATLAB Windows (Editor, Work space, Command history, Command Window), Naming and Checking Existence, MATLAB Graphics, Data and data flow in MATLAB, Editing and debugging M Files, Programming, Simulink, Control System Toolbox, Signal Processing Toolbox, Communication Toolbox, Image Processing Toolbox, Computer Vision Systems Toolbox, Fuzzy Logic Toolbox, Neural Network Toolbox, Analysis of C MEX files, Projects on Interfacing between Arduino, MATLAB and Simulink & more.
R, Python
This course will cover 'Introduction to Python Programming, Exploratory Data Analysis, Mathematics and Theory of Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Project'.
Power BI
With Power BI desktop you can shape and combine data with powerful, built-in tools. Introduces the tools that are available for preparing your data, and transforming it into a form ready for reporting.
Data Science with Python
Data Acquisition and Preprocessing, Data Visualization and Storytelling, Exploratory Data Analysis, Mathematics of Data Science, Python Programming, Modeling and Machine Learning,
Cloud Services with AWS
Cloud and Cloud Computing Preview, Cloud Computing Service Models, Cloud Computing Deployment Models, Amazon Web Services (AWS) Preview, AWS Global Infrastructure, AWS Regions and Replication of Data between the Regions, Availability in AWS and How to Measure Availability, High Availability and Availability Zones, AWS Edge Location, AWS Services, AWS CLI, SDKs And Management Console & more.
Machine Learning with Python
Python Programming, Exploratory Data Analysis, Mathematics and Theory of Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Capstone Project.