Introduction to Data Science for Big Data Analytics
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
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Will I get my certificate upon completion?
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