Java
and Python languages
taught right from the basics

Introduction to Java Development environments  | Program with Graphical User Interface | Advanced coding techniques. 

Bigdata / Data Science / Oracle / Python & Java

Python Syllabus

Introduction to Python 3
Introduction to Datatypes
Programming Concepts
Functions & Modules
Object Oriented Programming
Files and Exception Handling
GUI Development using Tkinter
Introduction to Pyqt5
Basic Widgets & Advanced Widgets
OS Module, Network Programming
SQL & SQL Constrains
Database Handling with SQL Lite3
Introduction to web Designing
Introduction to Client-Side Scripting
Documentation Object Model (DOM)
Basic Study of Django Framework
Django Templates and Form Details
Django Admin Customization
Reset APIS & User Authentication
Deploying Django Framework

Java Syllabus

Introduction to Java
Control Structure & Looping Statements
OOPS Introduction and Basics
OOPS Basics – Classes & Objects
OOPS Concepts – Inheritancs
OOPS Concepts – Abstraction
OOPS Concepts – Polymorphis
OOPS Concepts – Encapsulation Along with Packages
Exception Handling
Java Updated Features and OOPS Misc
IO Packages
Database
JDBC
Multithreading
Util Package and Collections
Generics in Collections Framework
AWT, Swing and Event Handling
J2EE Introduction & Basics of HTM, CSS & Java Script
Servlet Introduction & Creation
Servlet Usage
Servlet Filter and Listener
Java Server Pages (JSP)
JSP Capabilities
Spring Framework
Hibernate Framework

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Data Science Syllabus

➢ Life Cycle of Data Science
➢ Skills required for Data Science
➢ Careers Path in Data Science
➢ Applications of Data Science

➢ Relationship between Statistics and Data Science
➢ Introduction to Data
➢ Descriptive Statistics
➢ Inferential Statistics
➢ Random Sampling and Probability Distribution

➢ Python programming

➢ Python for Exploratory Data Analysis

➢ Introduction to RDBMS
➢ Retrieving
➢ Updating
➢ Inserting
➢ Deleting
➢ Sorting AND Filtering
➢ Summarizing AND Grouping
➢ Using Subqueries
➢ Joining Tables
➢ Views
➢ Stored Procedure
➢ Python Database ConnectionAPI

➢ Introduction To Machine Learning

➢  Text preprocessing using Bag of words technique
➢ TF(Term Frequency)
➢ IDF(Inverse Document Frequency)
➢ Normalization
➢ Vectorization
➢ NLP with Python

Big Data Syllabus

  • Linux (Ubuntu/Centos) – Tips and Tricks
  • Basic(core) Java Programming Concepts – OOPS
  • Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.
  • Topics:
    • Introduction to Big Data & Big Data Challenges
    • Limitations & Solutions of Big Data Architecture
    • Hadoop & its Features
    • Hadoop Ecosystem
    • Hadoop 2.x Core Components
    • Hadoop Storage: HDFS (Hadoop Distributed File System)
    • Hadoop Processing: MapReduce Framework
    • Different Hadoop Distributions

Understanding the Cluster

  • Hadoop 2.x Architecture
  • Typical workflow
  • HDFS Commands
  • Writing files to HDFS
  • Reading files from HDFS
  • Rack awareness
  • Hadoop daemons
  • Before MapReduce
  • MapReduce overview
  • Word count problem
  • Word count flow and solution
  • MapReduce flow
  • Data Types
  • File Formats
  • Explain the Driver, Mapper and Reducer code
  • Configuring development environment – Eclipse
  • Writing unit test
  • Running locally
  • Running on cluster
  • Hands on exercises
  • Anatomy of MapReduce job run
  • Job submission
  • Job initialization
  • Task assignment
  • Job completion
  • Job scheduling
  • Job failures
  • Shuffle and sort
  • Hands on exercises

MapReduce Types and Formats

  • File Formats – Sequence Files
  • Compression Techniques
  • Input Formats – Input splits & records, text input, binary input
  • Output Formats – text output, binary output, lazy output
  • Hands on exercises

MapReduce Features

  • Counters
  • Side data distribution
  • MapReduce combiner
  • MapReduce partitioner
  • MapReduce distributed cache
  • Hands exercises
  • Hive Architecture
  • Types of Metastore
  • Hive Data Types
  • HiveQL
  • File Formats – Parquet, ORC, Sequence and Avro Files Comparison
  • Partitioning & Bucketing
  • Hive JDBC Client
  • Hive UDFs
  • Hive Serdes
  • Hive on Tez
  • Hands-on exercises
  • Integration with Tableau

Apache Flume

  • Flume Architecture
  • Flume Agent Setup
  • Types of sources, channels, sinks Multi Agent Flow
  • Hands-on exercises

Apache Pig

  • Introduction to Apache Pig
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset

Apache HBase

  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model
  • Zookeeper Service
  • HBase Bulk Loading
  • Getting and Inserting Data
  • HBase Filters

Apache Sqoop

  • Sqoop Architecture
  • Sqoop Import Command Arguments, Incremental Import
  • Sqoop Export
  • Sqoop Jobs
  • Hands-on exercises

Apache Spark

  • Spark Basics
  • What is Apache Spark?
  • Spark Installation
  • Spark Configuration
  • Spark Context
  • Using Spark Shell
  • Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism
  • Functional Programming with Spark
  • ark Basics
  • What is Apache Spark?
  • Spark Installation
  • Spark Configuration
  • Spark Context
  • Using Spark Shell
  • Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism
  • Functional Programming with Spark

Apache Oozie

  • Oozie
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Talend Integration

 

  • Log File Analysis covering Flume, HDFS, MR/Pig, Hive, Tableau
  • Crime Data Analysis Covering Oozie, Sqoop, HDFS, Hive, HBase, RestFul Client.
  • Hadoop Use Cases in Insurance Domain
  • Hadoop Use Cases in Retail Domain
  •  

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Bigdata / Data Science / Oracle / Python & Java

Oracle Syllabus

• Identify the connection between an ERD and a Relational
Database
• Explain the relationship between a database and SQL
• Describe the purpose of DDL
• Describe the purpose of  DML
• Build a SELECT statement to retrieve data from an Oracle
Database table

• Use the ORDER BY clause to sort SQL query results
• Limit the rows that are retrieved by a query
• Use ampersand substitution to restrict and sort output at
runtime
• Use SQL row limiting clause

• Use various types of functions available in SQL
• Use character, number, and date and analytical
(PERCENTILE_CONT, STDDEV, LAG, LEAD) functions
in SELECT statements

• Describe various types of conversion functions that are
available in SQL
• Use the TO_CHAR, TO_NUMBER, and TO_DATE
conversion functions
• Apply general functions and conditional expressions in a
SELECT statement

• Describe the use of group functions
• Group data by using the GROUP BY clause
• Include or exclude grouped rows by using the HAVING
clause

• Describe the different types of joins and their features
• Use SELECT statements to access data from more than one
table using equijoins and nonequijoins
• Join a table to itself by using a self-join
• View data that generally does not meet a join condition by
using outer joins

• Define subqueries
• Describe the types of problems subqueries can solve
• Describe the types of subqueries
• Query data using correlated subqueries
• Update and delete rows using correlated subqueries
• Use the EXISTS and NOT EXISTS operators
• Use the WITH clause
• Use single-row and multiple-row subqueries

Using the Set Operators

• Describe set operators
• Use a set operator to combine multiple queries into a single
query
• Control the order of rows returned

• Truncate data
• Insert rows into a table
• Update rows in a table
• Delete rows from a table
• Control transactions

• Describe data types that are available for columns
• Create a simple table
• Create constraints for tables
• Drop columns and set column UNUSED
• Create and use external tables

• Query various data dictionary views

• Differentiate system privileges from object privileges
• Grant privileges on tables and on a user
• Distinguish between privileges and roles

• Describe how schema objects work
• Create simple and complex views with visible/invisible
columns
• Create, maintain and use sequences
• Create and maintain indexes including invisible indexes and
multiple indexes on the same columns
• Perform flashback operations

• Describe the features of multitable INSERTs
• Merge rows in a table

Bigdata / Data Science / Oracle / Python & Java
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