Advanced Methods in Data Science and Big Data Analytics

Course code: DELLADMEDSBG

This course builds on skills developed in the Data Science and Big Data Analytics course. The main focus areas cover Hadoop (including Pig, Hive, and HBase), Natural Language Processing, Social Network Analysis, Simulation, Random Forests, Multinomial Logistic Regression, and Data Visualization. Taking an “Open” or technology-neutral approach, this course utilizes several open-source tools to address big data challenges. This training prepares the learner for Dell Technologies Proven Professional advanced analytics specialist-level certification exam (E20-065).

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

Develop and execute MapReduce functionality
Gain familiarity with NoSQL databases and Hadoop Ecosystem tools for
analyzing large-scale, unstructured data sets
Develop a working knowledge of Natural Language Processing, Social
Network Analysis, and Data Visualization concepts
Use advanced quantitative methods and apply one of them in a Hadoop
environment
Apply advanced techniques to real-world datasets in a final lab

2 250 EUR

2 723 EUR including VAT

The earliest date from 10.02.2025

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Course dates

Starting date: 10.02.2025

Type: Virtual

Course duration: 5 days

Language: en

Price without VAT: 2 250 EUR

Starting date: 17.02.2025

Type: Virtual

Course duration: 5 days

Language: en

Price without VAT: 2 250 EUR

Starting date: Upon request

Type: Virtual

Course duration: 5 days

Language: en

Price without VAT: 2 250 EUR

	Starting date	Type	Course duration	Language	Price without VAT
	10.02.2025	Virtual	5 days	en	2 250 EUR	Register

	17.02.2025	Virtual	5 days	en	2 250 EUR	Register

	Upon request	Virtual	5 days	en	2 250 EUR	Register
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Target group

This course is intended for aspiring Data Scientists, data analysts that have completed the associate level Data Science and Big Data Analytics course, and computer scientists wanting to learn MapReduce and methods for analyzing unstructured data such as text.

Course structure

The content of this course is designed to support the course objectives.

Module 1: MapReduce and Hadoop

Lesson 1: The MapReduce Framework
Lesson 2: Apache Hadoop
Lesson 3: Hadoop Distributed File System
Lesson 4: YARN

Module 2: Hadoop Ecosystem and NoSQL

Lesson 1: Hadoop Ecosystem
Lesson 2: Pig
Lesson 3: Hive
Lesson 4: NoSQL – Not Only SQL
Lesson 5: HBase
Lesson 6: Spark

Module 3: Natural Language Processing

Lesson 1: Introduction to NLP
Lesson 2: Text Preprocessing
Lesson 3: TFIDF
Lesson 4: Beyond Bag of Words
Lesson 5: Language Modeling
Lesson 6: POS Tagging and HMM
Lesson 7: Sentiment Analysis and Topic Modeling
Module 4: Social Network Analysis
Lesson 1: Introduction to SNA and Graph Theory
Lesson 2: Most Important Nodes
Lesson 3: Communities and Small World
Lesson 4: Network Problems and SNA Tools
Module 5: Data Science Theory and Methods
Lesson 1: Simulation
Lesson 2: Random Forests

Lesson 3: Multinomial Logistic Regression

Module 6: Data Visualization

Lesson 1: Perception and Visualization
Lesson 2: Visualization of Multivariate Data Module In addition t
Lecture and demonstrations, the classroom options include handson lab exercises designed to allow practical experience for the participant. The
on-demand course provides recordings of the lab exercises.