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Data Science for Big Data Analytics

( Duration: 2 Days )

Big Data Analytics allow organizations to build competitive strategies around data-driven insights and derive value from vast amounts of untapped data. Whether you are tracking the efficiency of a warehouse or predicting how and when to modify staffing levels in a call center, this Data Science for Big Data Analytics training course provides the knowledge and skills required to reach the next level of decision-making maturity.

By attending Data Science for Big Data Analytics workshop, delegates will learn to:

  • Harness data mining methods to answer crucial business questions from internal and external data sources
  • Create competitive advantage from both structured and unstructured data
  • Predict outcomes with supervised machine learning techniques
  • Unearth patterns in customer behavior with unsupervised techniques
  • Work with R and RHadoop to analyze structured, unstructured and Big Data

This Data Science for Big Data Analytics class is intended for managers, data and business analysts, database professionals and others involved in forecasting and trends management. Programming and a background in statistics is helpful, but not required.



Introduction to R

  • Exploratory Data Analysis with R
    • Loading, querying and manipulating data in R
    • Cleaning raw data for modeling
    • Reducing dimensions with Principal Component Analysis
    • Extending R with user-defined packages
  • Facilitating good analytical thinking with data visualization
    • Investigating characteristics of a data set through visualization
    • Charting data distributions with boxplots, histograms and density plots
    • Identifying outliers in data

Working with Unstructured and Large Data Sets

  • Mining unstructured data for business applications
    • Preprocessing unstructured data in preparation for deeper analysis
    • Describing a corpus of documents with a term-document matrix
  • Coping with the additional complexities of Big Data
    • Examining the MapReduce and Hadoop architectures
    • Integrating R and Hadoop with RHadoop

Predicting Outcomes with Regression Techniques

  • Estimating future values with linear and logistic regression
    • Modeling the relationship between an output variable and several input variables
    • Correctly interpreting coefficients of continuous and categorical data
  • Regression techniques for dealing with Big Data
    • Overcoming issues of volume with RHadoop
    • Creating regression modules for RHadoop

Categorizing Data with Classification Techniques

  • Automating the labeling of new data items
    • Predicting target values using Decision Trees
    • Applying probabilistic methods to predict outcomes with Naive Bayes
    • Combining tree predictors with random forests in RHadoop
  • Assessing model performance
    • Visualizing model performance with a ROC curve
    • Evaluating classifiers with confusion matrices

Detecting Patterns in Complex Data with Clustering and Link Analysis

  • Identifying previously unknown groupings within a data set
    • Segmenting the customer market with the K-Means algorithm
    • Defining similarity with appropriate distance measures
    • Constructing tree-like clusters with hierarchical clustering
    • Clustering text documents and tweets to aid understanding
  • Discovering connections with Link Analysis
    • Capturing important connections with Social Network Analysis
    • Exploring how social networks results are used in marketing

Leveraging Transaction Data to Yield Recommendations and Association Rules

  • Building and evaluating association rules
    • Capturing true customer preferences in transaction data to enhance customer experience
    • Calculating support, confidence and lift to distinguish “good” rules from “bad” rules
    • Differentiating actionable, trivial and inexplicable rules
    • Meeting the challenge of large data sets when searching for rules with RHadoop
  • Constructing recommendation engines
    • Cross-selling, upselling and substitution as motivations
    • Leveraging recommendations based on collaborative filtering

Implementing Analytics within Your Organization

  • Expanding analytic capabilities
    • Breaking down Big Data Analytics into manageable steps
    • Integrating analytics into current business processes
    • Reviewing Spark, MLib and Mahout for machine learning
  • Dissemination and Big Data policies
    • Examining ethical questions of privacy in Big Data
    • Disseminating results to different types of stakeholders

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

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Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.