Certificate in Big Data and Data Analytics

Why Attend

Across all lines of business, sharp and timely data insights are needed to keep an organization
competitive in this digital era. Big data is a change agent that challenges the ways in which
organizational leaders have traditionally made decisions. Used effectively, it provides accurate
business models and forecasts to support better decision-making across all facets of an
organization.

This course provides participants with the data literacy they need to remain efficient,
effective, and ahead of the curve. Participants will learn why, where and how to generate business
value by deploying analytical methodologies. They will gain the knowledge and skills they need to
assemble and manage a large-scale big data analytics project. Lastly, participants will get a
conceptual introduction to the sophisticated predictive algorithms that are used in data science.

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Overview
Course Outline

Course Methodology
Participants will be led through a series of hands-on exercises and workshops, where they will have
the chance to apply and test the methods and practical approaches that they are learning throughout
the course. Students will work to identify areas of their organization that can be improved through
big data-driven implementations, and the types of improvements that can be made through these
analytical measures. As part of this course, participants will produce an actionable big data plan that
can be used as a blueprint for enterprise-wide big data deployments.

Course Objectives
By the end of the course, participants will be able to:
 Weigh-in on the benefits, functionality, and ecosystem that are related to big data
 Manage a big data initiative within their organization
 Identify how big data technologies and analytical methods can generate value for their organization.
 Assemble well-rounded big data analytics teams by identifying the essential data professional roles and responsibilities
 Deploy a simple and systematic analytical approach for generating business value

Target Audience
This course is designed for high-level technical professionals who want to use enterprise data to
achieve better, more efficient business results and/or to make improved decisions through
predictive analytics. This includes experienced data professionals, such as database administrators,
system administrators, business analysts or business intelligence specialists, as well as less
technically-inclined management and administrative professionals. Recommended pre-knowledge
includes experience analyzing data in Excel, as well as a basic understanding of correlation and how
to use Excel pivot tables. Participants should have prior experience working with data that is stored
in traditional relational database systems.

Target Competencies
Big Data Project Planning and Management
Data Presentation and Communication
Data-Informed Decision-Making
Analytical and Statistical Methods for Decision-Support

Course Outline

 The big data landscape overview
 What is Big Data?
 Big data vs. its predecessors
 How big data relates to data analytics and data science
 The big data paradigm
 Big data professional roles
 Overview of ways big data projects benefit businesses and industries
 The Hadoop ecosystem and architecture
 Overview of Hadoop, MapReduce YARN & Spark
 Other technologies in the big data paradigm
 Overview of MPP, In-memory appliances, Apache Spark (redo), NoSQL, Apache Lucene,Hive / Pig, HBASE, Cassandra, Kafka.Sqoop, Oozie, RDBMSs

 Big data project planning
 Conceptualizing how a big data project can meet organizational needs
 Considering relevant use cases
 NetFlix, LinkedIn, Experian, Shell Oil, Facebook, Google for Education, ETL off-loading, Enterprise search, Orbitz, Dell, SecureWorks
 Best practices in metrics selection
 Assessing the current state of your organization
 Assembling data teams
 Finalizing your implementation plan
 Implementing a data-driven solution

 Analytical methods for problem-solving
 Data-Driven Approach to Drive Improvements Across Business Workshop
 Pinpointing the problem
 Assessing the problem
 Analyzing alternative solutions
 Implementing your solution
 Getting to know data science and analytics roles and objectives
 Introduction to data analytics
 Basic math and statistics for data science
 Statistical algorithms in data science
 Making value of location data with Geographic Information System (GIS)
 Free analytics applications

 Basic data science mechanics
 The benefits of object-oriented programming
 Programming Python
 Structured Query Language (SQL) in analytics and data science
 Data presentation workshop

 Introduction to machine learning
 Getting to know machine learning
 Classification algorithms
 Regression algorithms
 Clustering algorithms
 Linear algebra algorithms
 Mathematical methods: MCDM
 Recommendation systems
 The ethics of artificial intelligence