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Diploma in Data Science


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Prerequisite:

Modules & Certifications:

  • Statistical Analysis with R
  • Programming with Python
  • Fundamentals of Linux
  • Programming Java with IoT Developer
  • Cloud Computing & HPC Applications
  • Data Collection & SQL
  • Big Data & Hadoop Technologies
  • Data Visualization
  • Advance Analytics
  • Practical Machine Learning


Certifications :

  • SQL 12c (1Z0-071)
  • RHEL 7 (RHCSA)
  • Python (PCAP)
  • CCP Data Engineer


Duration: 12 Months


Job Deployment assured post certification.

Statistical Analysis with R

R logo

Course Content:

PYTHON


What is Python?


Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.

Some of Python's notable features:

  • Uses an elegant syntax, making the programs you write easier to read.
  • Is an easy-to-use language that makes it simple to get your program working. This makes Python ideal for prototype development and other ad-hoc programming tasks, without compromising maintainability.
  • Comes with a large standard library that supports many common programming tasks such as connecting to web servers, searching text with regular expressions, reading and modifying files.
  • Python's interactive mode makes it easy to test short snippets of code. There's also a bundled development environment called IDLE.
  • Is easily extended by adding new modules implemented in a compiled language such as C or C++.
  • Can also be embedded into an application to provide a programmable interface.
  • Runs anywhere, including Mac OS X, Windows, Linux, and Unix.

  • Is free software in two senses. It doesn't cost anything to download or use Python, or to include it in your application. Python can also be freely modified and re-distributed, because while the language is copyrighted it's available under an open source license.

Some programming-language features of Python are:

  • A variety of basic data types are available: numbers (floating point, complex, and unlimited-length long integers), strings (both ASCII and Unicode), lists, and dictionaries.
  • Python supports object-oriented programming with classes and multiple inheritance.
  • Code can be grouped into modules and packages.
  • The language supports raising and catching exceptions, resulting in cleaner error handling.
  • Data types are strongly and dynamically typed. Mixing incompatible types (e.g. attempting to add a string and a number) causes an exception to be raised, so errors are caught sooner.
  • Python contains advanced programming features such as generators and list comprehensions.
  • Python's automatic memory management frees you from having to manually allocate and free memory in your code.
See the SimplePrograms collection of short programs, gradually increasing in length, which show off Python's syntax and readability.

Course Content:

Fundamentals of LINUX


Administration I - RH124

Overview

Red Hat System Administration I (RH124) is entry level course for those who want to make a career in full-time red hat system administrators by introducing key command line concepts and other enterprise-level tools. After taking this course student will be able to do tasks such as lab-based knowledge checks, facilitative discussions to ensure maximum skill transfer and retention, installation, establish network connectivity, manage physical storage and perform basic security administration.

Audience

This course is designed for IT professionals who are new to Linux Administration and require core Red Hat Enterprise Linux skills to perform administration tasks that will be encountered in the workplace.

Course Content:

Programming JAVA with IoT Developer


Course Content:

Cloud Computing & HPC Applications



Course Content:

Oracle Database SQL - Exam Code : 1Z0-071


Overview

Become an Oracle Database SQL Certified Associate and demonstrate understanding of fundamental SQL concepts needed to undertake any database project. Passing the exam illustrates depth of knowledge of SQL and its use when working with the Oracle Database server. Gain a working knowledge of queries , insert, update and delete SQL statements as well as some Data Definition language and Data Control Language, the optimizer, tales and indexes, data modeling and normalization. By passing this exam, a certified individual proves fluency in and a solid understanding of SQL language . data modeling and using SQL to create amd manipulate tables in an Oracle Database.

Qualified candidates have knowledge of general computing concepts, knowledge of command line interfaces and experience working in command line.


Exam Details

Exam Title: Oracle Database SQL
Exam Number: 1Z0-071
Exam Price: ₹10,265.00 More on exam pricing
Format: Multiple Choice
Duration: 100 minutes
Number of Questions: 73
Passing Score: 63%
Validated Against:

This exam was validated against 11g Release 2 version 11.2.0.1.0 and up to 12c Release 1 version 12.1.0.1.0.

Course Content / Exam(s)

Course Content:

Examination Score Report & Certificate


Big Data & Hadoop Technologies


Pre –Requisites for Course

  • OOPS Concepts (Polymorphism, Inheritance, encapsulation etc)
  • Java Basics like Interfaces, Classes and Abstract Classes etc.
  • Collections
  • File I/O
  • SQL
  • Linux Basic Commands

Course Content:

Data Visualization – Analysis and Reporting

  • Information Visualization
  • Data Analytics Life Cycle
  • Analytic Processes and Tools
  • Analysis Vs. Reporting
  • Modern Data Analytic Tools
  • Visualization Techniques
  • Visual Encoding
  • Visualization Algorithms
  • Data Collection and binding
  • Cognitive Issues
  • Interactive Visualization
  • Visualizing big data – structured Vs. unstructured
  • Visual Analytics
  • Geo mapping
  • Dashboard Design

Advanced Analytics

  • Introduction to Business Analytics using some case studies
  • Making right Business Decisions based on data
  • Exploratory Data Analysis – Visualization and Exploring Data
  • Descriptive Statistical Measures
  • Probability Distribution and Data
  • Sampling and Estimation
  • Statistical Interfaces
  • Predictive modeling and analysis
  • Regression Analysis
  • Forecasting Techniques
  • Simulation and Risk Analysis
  • Optimization
  • Linear, Non Linear & Integer
  • Decision Analysis
  • Strategy and Analytics
  • Overview of Factor Analysis
  • Directional Data Analytics
  • Functional Data Analytics

Practical Machine Learning

  • Supervised and Unsupervised Learning
  • Uses of Machine Learning
  • Clustering
  • K means
  • Hierarchical Clustering
  • Decision Trees
  • Oblique Trees
  • Classification Problems
  • Bayesian Analysis and Naïve bayes classifier
  • Random Forest
  • Gradient boosting Machines
  • Association rules learning
  • Apriori and FP-growth Algorithms
  • Support Vector Machines
  • Linear and Non Linear classification
  • ARIMA
  • ML in real time
  • Neural Networks and its application

Examination Score Report & Certificate





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