Linux Programming and Cloud Computing: 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 Cloud Computing: Cloud Computing Basics, Understanding Cloud Vendors (AWS:EC2 instance, lambda and Heroku: Heroku platform, Heroku Data services), Definition, Characteristics, Components, Cloud provider, SAAS, PAAS, IAAS and other Organizational scenarios of clouds, Administering & Monitoring cloud services, benefits and limitations, Deploy application over cloud. Comparison among SAAS, PAAS, IAAS, Cloud Products and Solutions, Cloud Pricing, Compute Products and Services, Elastic Cloud Compute, Dashboard.

  

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, Window function, Case statements. 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, architecture, comparison with MongoDB, working with Cassendra, Connecting DB’s with Python, Introduction to Data Driven Decisions, Enterprise Data Management, data preparation and cleaning techniques

  

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, Modules, Dictionaries, 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, 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  

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

  

Oops Concepts, Data Types, Operators and Language, Constructs, Inner Classes and Inheritance, Interface and Package, Exceptions, Collections, Threads, Java.lang, Java.util, Java Virtual Machine, Reflection in JVM, JVM’s architecture, Lambda Expressions, Functional Programming and Interfaces, Introduction to Streams, Introduction of JDBC API

  

  

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 Power BI, Modern Data Analytic Tools, Visualization Techniques.

  

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 modeling 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

  

Supervised and Unsupervised Learning , Uses of Machine learning , Clustering, K means, Hierarchical Clustering, Decision Trees, Classification problems, Bayesian analysis and Naïve Bayes classifier, Random forest, Gradient boosting Machines, Association rules learning, PCA, Apriori, Support vector Machines, Linear and Non liner classification,  ARIMA, XG Boost, CAT Boost, Neural Networks and its application, Tensorflow 2.x framework, Deep learning algorithms, KNN, NLP, Bert in NLP,NLP transformers, NLTK, Introduction to Pytorch framework, AI and its application