Big Data and Data Analytics
Big Data and Data Analytics is a change agent that challenges how organizational leaders have traditionally made decisions.
Across all lines of business, sharp and timely data insights are needed to keep an organization competitive in this digital era.
Used effectively, Big Data and Data Analytics provide 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.
This course goes beyond the hype of what Big Data is, and will set the foundation for your journey to advanced decision-making and business benefits by giving you the tools to leverage Big Data.
In the current era, modern organization managers or specialists are expected to decipher insights from an organization-wide IT system and be able to provide precise analysis and recommendations for senior managers/executives.
To do this effectively means parsing through tonnes of data to recognize and analyze patterns using the best tools for the job. This course will enable you to use cutting-edge techniques to identify trends and explore what those trends mean in turn for your organization.
This hands-on interactive learning experience will provide you with a rich toolset for data analysis to help you make better decisions and recommendations, thus building your capability and confidence in using Big Data analysis as part of your job role.
Participants 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.
You might be interested in other IT programs as a next step.
- COURSE TYPE Practitioner
- COURSE NUMBER
- DURATION 3 days
- COURSE ACCREDITED BY Local Certificate
YOU WILL LEARN HOW TO
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.
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 significant 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 systematic and straightforward analytical approach for generating business value
IMPORTANT COURSE INFORMATION
Participants who fully attend this course and complete the test on the last day will receive a Strategic Axis Professional Certificate (SAPC). SAPC certificates are regionally recognized and can be quite valuable when applying for more senior roles within the organization or outside.
Module 1: 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
- How big data projects benefit businesses and industries
- The Hadoop ecosystem and architecture
- Other technologies in the big data paradigm
Module 2: Big data project planning
- Beyond the Hadoop ecosystem
- Other popular projects by MapR
- Commercial distributions of Hadoop
- Security within Hadoop
- Data engineering
- Useful programming languages
- The 4-step big data planning process
- Staying competitive as a big data professional
Module 3: Advanced analytical methods for problem-solving
- The nature of data science and analytics
- Fraud prevention in real-time using machine learning
- Online sales improvement through recommendation engines
- Customer churn prediction and reduction through logistic regression
- Best option selection using multi-criteria decision making
- Stock price predictions using Markov Chains
- Analyzing how price changes impact sales volumes using simple linear regression
Module 4: Data science mechanics
- The benefits of object-oriented programming
- Programming Python
- R programming for data science
- Where is your data coming from?
- The traditional relational database management system (RDBMS – DSFD) source
- Structured Query Language (SQL) in analytics and data science
- Making value of location data with Geographic Information System (GIS)
- Machine learning
- Popular machine learning algorithms (ML)
Module 5: Resources to analyze data and communicate findings
- Applications for data science and analytics
- Context and benchmarking using free and open data
- Scraping the web for market data
- The different types of data visualization
- Three simple steps to design for your audience
- Data graphics
- Design styles to convey powerful messages
- Design data analytics dashboards
In The Classroom
Private Team Training
Indiviual Private Session
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