Data Science and Big Data Analytics

Target group

• Managers of teams of business intelligence, analytics, and big data professionals
• Current business and data analysts looking to add big data analytics to their skills
• Data and database professionals looking to exploit their analytic skills in a big data environment
• Recent college graduates and graduate students with academic experience in a related discipline looking to move into the world of Data Science and big data
• Individuals looking to take the EMC Proven Professional Data Scientist Associate (EMCDSA) certification

Skills Gained

• Deploy the Data Analytics Lifecycle to address big data analytics projects
• Reframe a business challenge as an analytics challenge
• Apply appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
• Select appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences
• Use R and RStudio, MapReduce/Hadoop, in-database analytics, Windows, and MADlib functions
• Use advanced analytics create competitive advantage
• Data scientist role and skills vs. traditional business intelligence analyst

Struktura kurzu66

1. Big Data Analytics

• Big Data
• State of the Practice in Analytics
• Data Scientist
• Big Data Analytics in Industry Verticals

2. Data Analytics Lifecycle

• Discovery
• Data Preparation
• Model Planning
• Model Building
• Communicating Results
• Operationalizing

3. Basic Data Analytic Methods Using R

• Using R to Look at Data
• Analyzing and Exploring the Data
• Statistics for Model Building and Evaluation

4. Advanced Analytics: Theory and Methods

• K Means Clustering
• Association Rules
• Linear Regression
• Logistic Regression
• Nave Bayesian Classifier
• Decision Trees
• Time Series Analysis
• Text Analysis

5. Advanced Analytics: Technologies and Tools

• Analytics for Unstructured Data
  • MapReduce and Hadoop
  • Hadoop Ecosystem
• In-Database Analytics: SQL Essentials
  • Advanced SQL and MADlib for In-Database Analytics

6. Putting it All Together

• Operationalizing an Analytics Project
• Creating the Final Deliverables
• Data Visualization Techniques
• Final Lab Exercise on Big Data Analytics