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Data Science Ecare Technologies Institute

₹ 15,000   ( ₹ 18,000  | 15% Off)
No. 24, 1st Floor, 2nd Main Cross Road, Vinayaka Layout,, ,
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Data Science has risen in the recent past to become the number 1 skill to have in the industry.  There is significant and growing need for data scientist in almost all the business domains. However, the talent in this niche skill is still scarce resulting in high salaries for data engineers, data scientists, statisticians, and data analysts.

McKinsey Global Institute recently reported, “a shortage of the analytical and managerial talent necessary to make the most of Big Data is a significant and pressing challenge (for the U.S.).”  The report estimates that there will be 4-5 million jobs in the U.S. requiring data analysis skills by 2018, and that large numbers of positions will only be filled through training or re-training.

Learning Objectives: At the end of Data Science Training course, participants will be able to:

  • Work Basic statistics on R
  • Understand Difference between Supervise and Unsupervised Machine learning
  • Fully grasp all the concepts of clustering, classification and recommendation systems
  • Integration of R and Hadoop
  • Basics of statistical modeling and experiment of design
  • Data visualization

Module 1: Introduction

Topics covered on Introduction
  • Examples,
  • Data science articulated
  • History and context
  • Technology landscape

Module 2: Basic & Advanced Data Manipulation, Basic Graphs and Statistic, using R

Topics covered on R Programming
  • Manipulating dates and missing values
  • Understanding data type conversions
  • Creating and recoding variables
  • Selecting and dropping variables.
  • Mathematical and statistical functions
  • Character functions
  • Looping and conditional execution
  • User-written functions
  • Ways to aggregate and reshape data
  • Bar, box, and dot plots
  • Pie and fan charts
  • Histograms and kernel density plots
  • Descriptive statistics
  • Frequency and contingency tables
  • Correlations and covariances
  • t-tests
  • Nonparametric statistics

Module 3: Machine learning algorithm

Topics covered on Machine Learning Algorithm
  • Machine Learning Overview
  • ML Common Use Cases
  • Understanding Supervised
  • Unsupervised Learning Techniques


Supervised Learning

  • Classifying with k-Nearest Neighbors
  • Splitting datasets one feature at a time: decision trees
  • Classifying with probability theory: naïve Bayes
  • Logistic regression
  • Support vector machines


Unsupervised Learning

  • Grouping unlabeled items using k-means clustering

Module 4: Clustering, Classification and Recommendation

  • Intro to clustering
  • Common Clustering Algorithms
  • K-means
  • Fuzzy K-means, Mean Shift etc.
  • Representing data
  • Feature Selection
  • Vectorization
  • Representing Vectors
  • Intro to classification
  • Examples
  • Basics
  • Common Algorithms
  • Mahout on Hadoop
  • Apache Mahout
  • Intro to Classification
  • Common Classification Algorithms
Recommendation Systems
  • Intro to recommendation systems
  • Content Based
  • Collaborative filtering
  • User based
  • Nearest N Users
  • Threshold
  • Item based
  • Mahout Optimizations
  • An overview of a recommendation platform
  • Distance & Similarity measures
  • Evaluating Recommendation engines – Online/ Offline

Module 5: Integration of R & HADOOP

Topics covered on R and hadoop
  • Hadoop – Architecture, Hive, Pig, Data Analysis
  • Integrating R with Hadoop using RHadoop and RMR package, Exploring RHIPE (R Hadoop Integrated Programming Environment), Writing Map Reduce Jobs in R and executing them on Hadoop.

Module 6: Analytics

Topics covered on Analytics
  • Basic statistical modeling, experiment design, introduction to machine learning, over fitting
  • Supervised learning: overview, simple nearest neighbor, decision trees/forests, regression
  • Unsupervised learning: k-means, multi-dimensional scaling
  • Graph Analytics: PageRank, community detection, recursive queries, iterative processing
  • Text Analytics: latent semantic analysis
  • Collaborative Filtering: slope-one

Module 7: Communicating Results

Topics covered on Communicating Results
  • Visualization, data products, visual data analytics
  • Provenance, privacy, ethics, governance

Module 8: Case Studies and Projects – Financial, Retail, Social Media etc..,

Case Studies and Projects
  • Project Discussion, Problem Statement and Analysis, Various approaches to solve a Data Science Problem, Pros and Cons of different approaches and algorithms
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data science ecare technologies bangalore course
Price: ₹ 15,000
Start-End Dates: 15 Oct 16 - 14 Nov 16
Course Duration: 30 days
Discount: 25%
Instructional Level: Appropriate for All
Live Projects
Doubt Clearing Sessions
Reading Material
EMI Option
Online Support
Post completion course access
Practice Exams
Placement assistance
Refund Policy
Post completion support

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