Data Science and Big Data Analytics (EN)

studium

Základní info

Description

This course provides practical foundation level training that enables immediate and effective participation in Big Data and other analytics projects. It includes an introduction to Big Data and the data analytics lifecycle to address business challenges that leverage Big Data.

Training is provided by authorized distributor DNS a.s.

The course provides grounding in basic and advanced analytic methods and an introduction to Big Data analytics technology and tools, including MapReduce and Hadoop. Labs offer opportunities for students to understand how these methods and tools may be applied to real world business challenges by a practicing data scientist.

The course takes an “open”, or technology-neutral approach and includes a final lab which addresses 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 Dell EMC Proven™ Professional Data Scientist Associate (EMCDSA) certification exam.

Goals

Upon successful completion of this course, participants should be able to:

Immediately participate as a data science team member
Work with large data sets and generate insights
Build predictive and classification models
Manage a data analytics project through the entire lifecycle

Target

This course is intended for individuals seeking to develop an understanding of Data Science from the perspective of a practicing Data Scientist, including:

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 seeking to take advantage of the EMC Proven™ Professional Data Scientist Associate (EMCDSA) certification

Module 1 - Introduction to Big Data analytics

Big Data and its characteristics Lesson
Business value from Big Data
Data scientist

Module 2 – Data Analytics Lifecycle

Data analytics lifecycle overview
Discovery phase
Data preparation phase
Model planning phase
Model building phase
Communicate results phase
Operationalize phase

Module 3 – Basic data analytics methods using R

Introduction to the R programming language
Analyzing and exploring data
Statistics for model building and evaluation

Module 4– Advanced analytics theory and methods

Introduction to advanced analytics—theory and methods. It includes an introduction to Big Data and the data analytics lifecycle to address business challenges that leverage Big Data.
K-means clustering
Association rules
Linear regression
Logistic regression
Text analysis
Naïve Bayes
Decision trees
Time series analysis

Module 5: Advanced analytics—technology and tools

Introduction to advanced analytics—technology and tools
Hadoop ecosystem
In-database analytics SQL essentials
Advanced SQL and MADlib

Module 6: Putting it all together

Preparing to operationalize
Preparing project presentations
Data visualization techniques

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

Study Materials

Materials are in electronic form from Dell Technologies.

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