Virtual ClassroomLearning Style
5 DaysCourse Duration
About Individual Course:
About this course:
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.
The average salary for Big Data Analyst is $65,470 per year.
After completing this course, students will be able 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
This course 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.
- Students should already be familiar with quantitative methods at an introductory level, up to linear regression analysis. Familiarity with computer programming or database structures is a benefit, but not formally required.
Suggested prerequisites courses:
Virtual Instructed-Led 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
|Learning Style||Virtual Classroom|
|Course Duration||5 Days|
Frequently Asked Questions About Virtual Instructor-Led Courses
I can't connect to my class, what are my options?
The link to the class is available upon logging in to your dashboard. If you are unable to see it, please contact our support team at 1-855-800-8240 and they will be happy to provide you the direct link via email or the dial in number.
I can't make it to attend to class. Can I reschedule?
Yes, you can reschedule your class. Please contact your Sales representative and they will arrange this for you. If you forgot his/her name, feel free to contact our support team at email@example.com or 1-855-800-8240.
Will I get my certificate upon completion?
Yes. Upon completion of the course, it will be available on your course as a Trophy Icon for you to download. If you do not see this, you will need to contact firstname.lastname@example.org with the following details so they can email you the certificate: Class Name, Class Date, Account Rep, and Your Email.
I cannot connect to my lab. Help!
Your Lab is accessible on the bottom part of your course. You will see a button that says "LAB". Just click it to launch the lab. Please note that some classes don’t need/require a LAB. You can verify with our support team by calling them at 1-855-800-8240 or by email at email@example.com. You can also check with your Instructor or the Associate Instructor if your class includes one.
What is my access code for Skillpipe?
A. Not all of the classes have or require Skillpipe. If your class includes one, please check your email as you should have received one from firstname.lastname@example.org. In case you do not find it in your inbox, please check the Spam / Junk folder. For any further assistance, you can call the support at 1-855-800-8240 or contact them via email at email@example.com.
I don't have audio. I can't hear the instructor.
Make sure you are using a compatible headset for your laptop or computer. If you don’t have a headset, you can use the built-in speaker of your laptop. Otherwise, you can use the dial in option by calling the dial in number provided in the class joining email. You may also contact support team for the dial in numbers associated for your training at 1-855-800-8240 or contact them via email at firstname.lastname@example.org.
How can I reach student support?
Support can be reach via phone at 1855-800-8240; via email at email@example.com or via chat support through the chat button on our website. Please note that support office hours will be from 8am-5pm CST Monday to Friday. Any concerns after office hours will be attended the following business day.
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