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Introduction to Data Science for Big Data Analytics

COURSE TYPE

Advanced

Course Number

1253

Duration

5 Days

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Whether you are tracking the efficiency of a warehouse or predicting how and when to modify staffing levels in a call center, this training course equips you with the essential knowledge and skills required to reach the next level of decision-making maturity. You will learn to derive value from vast amounts of untapped data and apply data analytics techniques for smart, data-driven decision-making.

You Will Learn How To

  • 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

Course Outline

  • 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
  • Building a model from existing data for future predictions
  • 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, up–selling 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
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Course Schedule

Attend this live, instructor-led course In-Class or Online via AnyWare.

Hassle-Free Enrollment: No advance payment required.
Tuition due 30 days after your course.

Jun 19 - 23 Toronto/AnyWare Enroll Now

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In-Class

Jul 31 - Aug 4 Herndon, VA/AnyWare Enroll Now

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Sep 11 - 15 Ottawa/AnyWare Enroll Now

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Sep 18 - 22 New York/AnyWare Enroll Now

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In-Class

Oct 16 - 20 Toronto/AnyWare Enroll Now

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In-Class

Oct 23 - 27 Washington, DC Enroll Now

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In-Class

Oct 23 - 27 Alexandria, VA/AnyWare Enroll Now

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In-Class

Oct 30 - Nov 3 Rockville, MD/AnyWare Enroll Now

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Nov 27 - Dec 1 Herndon, VA/AnyWare Enroll Now

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Jan 8 - 12 AnyWare Enroll Now

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Feb 26 - Mar 2 Ottawa/AnyWare Enroll Now

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Mar 12 - 16 Herndon, VA/AnyWare Enroll Now

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Mar 19 - 23 New York/AnyWare Enroll Now

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Apr 9 - 13 Washington, DC/AnyWare Enroll Now

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Apr 16 - 20 Toronto/AnyWare Enroll Now

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Apr 30 - May 4 Rockville, MD/AnyWare Enroll Now

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Bring this Course to Your Organization and Train Your Entire Team
For more information, call 1-888-843-8733 or click here

Tuition

Standard

$3190

Government

$2833

Course Tuition Includes:

After-Course Instructor Coaching
When you return to work, you are entitled to schedule a free coaching session with your instructor for help and guidance as you apply your new skills.

After-Course Computing Sandbox
You'll be given remote access to a preconfigured virtual machine for you to redo your hands-on exercises, develop/test new code, and experiment with the same software used in your course.

Free Course Exam
You can take your course exam on the last day of your course and receive a Certificate of Achievement with the designation "Awarded with Distinction."

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Questions

Call 1-888-843-8733 or click here »

An experienced training advisor will happily answer any questions you may have and alert you to any tuition savings to
which you or your organization may be entitled.

Training Hours

Standard Course Hours: 9:00 am – 4:30 pm
*Informal discussion with instructor about your projects or areas of special interest: 4:30 pm – 5:30 pm

FREE Online Course Exam (if applicable) – Last Day: 3:30 pm – 4:30 pm
By successfully completing your FREE online course exam, you will:

  • Have a record of your growth and learning results.
  • Bring proof of your progress back to your organization
  • Earn credits toward industry certifications (if applicable)

Enhance Your Credentials with Professional Certification

Learning Tree's comprehensive training and exam preparation guarantees that you will gain the knowledge and confidence to achieve professional certification and advance your career.

Earn 29 Credits from NASBA

This course qualifies for 29 CPE credits from the National Association of State Boards of Accountancy CPE program. Read more ...

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