Preferred method of contact:

Introduction to Data Science for Big Data Analytics

COURSE TYPE

Foundation

Course Number

1253

Duration

5 Days

PDF Add to WishList

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
Show complete outline
Show Less

Convenient Ways to Attend This Instructor-Led Course

Hassle-Free Enrollment: No advance payment required to reserve your seat.
Tuition due 30 days after you attend your course.

In the Classroom

Live, Online

Private Team Training

In the Classroom — OR — Live, Online

Tuition — Standard: $3190   Government: $2833

Jul 31 - Aug 4 (5 Days)
9:00 AM - 4:30 PM EDT
Reston, VA / Online (AnyWare) Reston, VA / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Sep 11 - 15 (5 Days)
9:00 AM - 4:30 PM EDT
Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Sep 18 - 22 (5 Days)
9:00 AM - 4:30 PM EDT
New York / Online (AnyWare) New York / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Oct 16 - 20 (5 Days)
9:00 AM - 4:30 PM EDT
Toronto / Online (AnyWare) Toronto / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Oct 23 - 27 (5 Days)
9:00 AM - 4:30 PM EDT
Washington, DC Washington, DC Reserve Your Seat

How would you like to attend?

In-Class

Oct 23 - 27 (5 Days)
9:00 AM - 4:30 PM EDT
Alexandria, VA / Online (AnyWare) Alexandria, VA / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Oct 30 - Nov 3 (5 Days)
9:00 AM - 4:30 PM EDT
Rockville, MD / Online (AnyWare) Rockville, MD / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Nov 27 - Dec 1 (5 Days)
9:00 AM - 4:30 PM EST
Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Jan 8 - 12 (5 Days)
9:00 AM - 4:30 PM EST
Online (AnyWare) Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online

Feb 26 - Mar 2 (5 Days)
9:00 AM - 4:30 PM EST
Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Mar 12 - 16 (5 Days)
9:00 AM - 4:30 PM EDT
Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Mar 19 - 23 (5 Days)
9:00 AM - 4:30 PM EDT
New York / Online (AnyWare) New York / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Apr 9 - 13 (5 Days)
9:00 AM - 4:30 PM EDT
Washington, DC / Online (AnyWare) Washington, DC / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Apr 16 - 20 (5 Days)
9:00 AM - 4:30 PM EDT
Toronto / Online (AnyWare) Toronto / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Apr 30 - May 4 (5 Days)
9:00 AM - 4:30 PM EDT
Rockville, MD / Online (AnyWare) Rockville, MD / Online (AnyWare) Reserve Your Seat

How would you like to attend?

Live, Online
In-Class

Guaranteed to Run

Show all dates
Show fewer dates

Private Team Training

Enrolling at least 3 people in this course? Consider bringing this (or any course that can be custom designed) to your preferred location as a private team training.

For details, call 1-888-843-8733 or Click Here »

Tuition

Standard

Government

In Classroom or
Online

Standard

$3190

Government

$2833

Private Team Training

Contact Us »

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."

Prev
Next

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 ...

- ,

Prev
Next
Chat Now

Please Choose a Language

Canada - English

Canada - Français