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Data Science at Mytectra Institute





  (  | 15% Off)
IWWA Building NO (10)P 2nd Floor, 7th Main Road BTM 2nd Stage, ,
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734

Data administration and management being the biggest challenges of the information explosion happening these days, this data science course gets the deeper and yet knowledgeable course for the data analytics professionals. The course allows one to bring up their basic data base knowledge and make it apply to the more advanced level of data science which is a very much typically needed mindset for the current data analysis of IT field.

Who should go for this course?

  • Managers
  • Data analysts
  • Business analysts
  • Operators
  • Job Seekers
  • End users
  • Developers
  • Freshers/Graduates
  • IT professionals

Why learn Data Science?

To manage whopping amount of the data, data scientists are needed who are the most enthusiastic people. These can handle large volumes of data and literally play with it, to give inferences and create spot trends upon the data. It is the undoubtedly emerging field in data analysis which has great link with the upcoming data software that are being prepared for the improvisation of data management.

What are the pre-requisites for this Course?

There are many Data Science training Institutes in Bangalore that offer courses in Data science, but this institute gives the best out of all existing institutes. To attend this highly evolved data science course, most of the Information technology professionals having analytical thinking are needed. Those who want to move from basic trending and analyzing till the typical analytical thinking about the data. People having the more quantitative skills, technical background complementarily can attend the course, as either of them have the need to fill their gaps of knowledge to move further.

How will I execute the Practicals?

To be able to execute one needs to get the latest server software installed in their systems which comes with most of the current using software to accommodate the software accordingly. Internet is needed for better support and help from the side of the software. Anyhow basic remote desktop support will be provided by our side to clear any doubts in the mind before going to actual real time execution of the Data science.

The Course goes with the aim to understand key concepts about:

  • To know about the business intelligence and business analysis
  • To understand the descriptive statistics of Data analysis
  • To excel working with Tableau
  • To get introduced to R and data exploration to R
  • Understand to create Decision trees.
  • To understand Data collection and data mining
  • Know the importance of big data technologies
  • To get the prior idea about the Loop functions and debugging tools
  • To learn running non-parametric tests

Module 1: Getting Started With Data Science And Recommender Systems

Topics covered on Introduction

Data Science Overview
Reasons to use Data Science
Project Lifecycle
Data Acquirement
Evaluation of Input Data
Transforming Data
Statistical and analytical methods to work with data
Machine Learning basics
Introduction to Recommender systems
Apache Mahout Overview

Module 2: Introduction to Data Science

Topics covered on R Programming

What is Data Science?
What Kind of Problems can you solve?
Data Science Project Life Cycle
Data Science-Basic Principles
Data Acquisition
Data Collection
Understanding Data- Attributes in a Data, Different types of Variables
Build the Variable type Hierarchy
Two Dimensional Problem
Co-relation b/w the Variables- explain using Paint Tool
Outliers, Outlier Treatment
Boxplot, How to Draw a Boxplot

Module 3: Machine Learning In Data Science

Topics covered on Machine Learning Algorithm

Discussion about Boxplot and Outlier
Goal: Increase Profits of a Store
Areas of increasing the efficiency
Data Request
Business Problem: To maximize shop Profits
What are Interlinked variables
What is Strategy
Interaction b/w the Variables
Univariate analysis
Multivariate analysis
Bivariate analysis
Relation b/w Variables
Standardize Variables
What is Hypothesis?
Interpret the Correlation
Negative Correlation4.16
Machine Learning

Module 4: Statistical And Analytical Methods Dealing With Data, Implementation Of Recommenders Using Apache Mahout And Transforming Data

Clustering

Correlation b/w Nominal Variables
Contingency Table
What is Expected Value?
What is Mean?
How Expected Value is differ from Mean
Experiment – Controlled Experiment, Uncontrolled Experiment
Dependency b/w Nominal Variable & Continuous Variable
Linear Regression
Extrapolation and Interpolation
Univariate Analysis for Linear Regression
Building Model for Linear Regression
Pattern of Data means?
Data Processing Operation
What is sampling?
Sampling Distribution
Stratified Sampling Technique
Disproportionate Sampling Technique
Balanced Allocation-part of Disproportionate Sampling
Systematic Sampling
Cluster Sampling

Module 5: Integration of R & HADOOP

Module 6: Business Algorithms, Simple Approaches To Prediction, Building Model, Model Deployment

Topics covered on Analytics

Machine Learning
Importance of Algorithms
Supervised and Unsupervised Learning
Various Algorithms on Business
Simple approaches to Prediction
Predict Algorithms
Population data
sampling
Disproportionate Sampling
Steps in Model Building
Sample the data
What is K?
Training Data
Test Data
Validation data
Model Building
Find the accuracy
Rules
Iteration
Deploy the model
Linear regression

Module 7: Getting Started With Segmentation Of Prediction And Analysis

Topics covered on Communicating Results

Clustering
Cluster and Clustering with Example
Data Points, Grouping Data Points
Manual Profiling
Horizontal & Vertical Slicing
Clustering Algorithm
Criteria for take into Consideration before doing Clustering
Graphical Example
Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy Clustering
Simple Approaches to Prediction
Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
Clustering Algorithm in Mahout
Probabilistic Clustering
Pattern Learning
Nearest Neighbor Prediction

Module 8: Integration Of R And Hadoop

Case Studies and Projects

R introduction
How R is typically used
Features of R
Introduction to Big data
R+Hadoop
Ways to connect with R and Hadoop
Products
Case Study
Architecture
Steps for Installing RIMPALA
How to create IMPALA packages

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Start-End Dates: 24 Jun 16 - 23 Jul 16
Course Duration: 30 days
Discount: 25%
Instructional Level: Appropriate for All
Certification
Quizzes
Live Projects
Doubt Clearing Sessions
Reading Material
EMI Option
Online Support
Post completion course access
Practice Exams
Placement assistance
Refund Policy
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