COURSE OBJECTIVE:
• Immediately participate and contribute as a Data Science Team Member on big data and other analytics projects by: • Deploying the Data Analytics Lifecycle to address big data analytics projects • Reframing a business challenge as an analytics challenge • Applying appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results • Selecting appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences • Using tools such as: R and RStudio, MapReduce/Hadoop, in-database analytics, Window and MADlib functions • Explain how advanced analytics can be leveraged to create competitive advantage and how the data scientist role and skills differ from those of a traditional business intelligence analyst
TARGET AUDIENCE:
This course is intended for individuals seeking to develop an understanding of Data Science from the perspective of a practicing Data Scientist.
COURSE PREREQUISITES:
To complete this course successfully and gain the maximum benefits from it, a student should have the following knowledge and skill sets: • A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course • Experience with a scripting language, such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open source statistical tool and programming • Experience with SQL (some course examples useConsider the above as a list of specific prerequisite (or refresher) training and reading to be completed prior to enrolling for or attending this course. Having this requisite background will help ensure a positive experience in the class, and enable students to build on their expertise to learn many of the more advanced tools and analytical methods taught in the course.
COURSE CONTENT:
• Introduction and Course Agenda • Introduction to Big Data Analytics • Big Data Overview • State of the Practice in Analytics • The Data Scientist • Big Data Analytics in Industry Verticals • Data Analytics Lifecycle • Discovery • Data Preparation • Model Planning • Model Building • Communicating Results • Operationalizing • Review of Basic Data Analytic Methods Using R • Using R to Look at Data – Introduction to R • Analyzing and Exploring the Data • Statistics for Model Building and Evaluation • Advanced Analytics – Theory And Methods • K Means Clustering • Association Rules • Linear Regression • Logistic Regression • Naïve Bayesian Classifier • Decision Trees • Time Series Analysis • Text Analysis • Advanced Analytics – Technologies and Tools • Analytics for Unstructured Data – MapReduce and Hadoop • The Hadoop Ecosystem • In-database Analytics – SQL Essentials • Advanced SQL and MADlib for In-database Analytics • The Endgame, or Putting it All Together • Operationalizing an Analytics Project • Creating the Final Deliverables • Data Visualization Techniques • Final Lab Exercise on Big Data Analytics
FOLLOW ON COURSES:
Not available. Please contact.
