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 Certificate in Big Data and Data Analytics 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.

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 preknowledge 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 and 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 forEducation,
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-solvin
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

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 Certificate in Big Data and Data Analytics 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.

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 preknowledge 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 and 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 forEducation,
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-solvin
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
Mathematical methods: MCDM
Recommendation systems
The ethics of artificial intelligence

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