Data Science & Big Data Analytics Course Description
Data Science Big Data Analytics course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It establishes a baseline of skills that can be further enhanced with additional training and real-world experience. The course provides an introduction to big data and a Data Analytics Lifecycle Process to address business challenges that leverage big data. It provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools including MapReduce and Hadoop.
The course has extensive labs throughout to provide practical opportunities to apply these methods and tools to real-world business challenges and includes a final lab in which students address a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle. The course prepares the student for the Proven\u2122 Professional Data Scientist Associate EMCDSA) certification exam.
Data Science & Big Data Analytics Course Learning Outcomes;
- Immediately participate and contribute as a Data Science Team Member on big data and other analytics projects.
- 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 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.
Data Science Big Data Analytics Training – Suggested Audience
This training is aimed at business intelligence professionals. Suggested attendees based on our past programs are:
- Managers of business intelligence
- Analytics Professionals
- Big data professionals
- Data and database professionals
Data Science & Big Data Analytics Training – Prerequisites
- A strong quantitative background with a solid understanding of basic statistics.
- Experience with a scripting language, such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (actually RStudio), which is an open source statistical tool and programming language
Experience with SQL
Data Science & Big Data Analytics In-house/Corporate Training
If you have a group of 5-6 participants, apply for in-house training. For commercials please send us an email with group size to firstname.lastname@example.org
|1. Introduction to Big Data Analytics|
|Big Data Overview Details||00:00:00|
|State of the Practice in Analytics Details||00:00:00|
|The Data Scientist Details||00:00:00|
|Big Data Analytics in Industry Verticals Details||00:00:00|
|2. Data Analytics Lifecycle|
|Data Preparation Details||00:00:00|
|Model Planning Details||00:00:00|
|Model Building Details||00:00:00|
|Communicating Results Details||00:00:00|
|3. Review of Basic Data Analytic Methods Using R|
|Using R to Look at Data – Introduction to R Details||00:00:00|
|Analyzing and Exploring the Data Details||00:00:00|
|Statistics for Model Building and Evaluation Details||00:00:00|
|4. Advanced Analytics - Theory and Methods|
|K Means Clustering Details||00:00:00|
|Association Rules Details||00:00:00|
|Linear and Logistic Regression Details||00:00:00|
|Naïve Bayesian Classifier Details||00:00:00|
|Decision Trees Details||00:00:00|
|Time Series Analysis Details||00:00:00|
|Text Analysis Details||00:00:00|
|5. Advanced Analytics - Technologies and Tools|
|Analytics for Unstructured Data – MapReduce and Hadoop Details||00:00:00|
|The Hadoop Ecosystem:In-database Analytics – SQL Essentials Details||00:00:00|
|Advanced SQL and MADlib for In-database Analytics Details||00:00:00|
|6. The Endgame, or Putting it All Together|
|Operationalizing an Analytics Project Details||00:00:00|
|Creating the Final Deliverable Details||00:00:00|
|Data Visualization Techniques Details||00:00:00|
|Final Lab Exercise on Big Data Analytics Details||00:00:00|
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