Training with Iverson classes
Training is not a commodity – all training centres are not the same. Iverson Associates Sdn Bhd is the most established, the most reputable, and the top professional IT training provider in Malaysia. With a large pool of experienced and certified trainers, state-of-the-art facilities, and well-designed courseware, Iverson offers superior training, a more impactful learning experience and highly effective results.
At Iverson, our focus is on providing high-quality IT training to corporate customers, meeting their learning needs and helping them to achieve their training objectives. Iverson has the flexibility to provide training solutions whether for a single individual or the largest corporation in a well-paced or accelerated training programme.
Our courses continue to evolve along with the fast-changing technological advances. Our instructor-led training services are available on a public and a private (in-company) basis. Some of our courses are also available as online, on demand, and hybrid training.
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
Additional Info
- Certification Course & Certificate
- Course Code EMCADSA
- Price RM12250
- Exam Price Exclude
- Exam Code E20-065
- Duration 5 Days
- CertificationInfo EMC Data Scientist Specialist
- Principals Dell-EMC
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Schedule
4-8 Mar 2024
24-28 Jun 2024
23-27 Sep 2024
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Audience
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
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Prerequisities
- Completion of the Data Science and Big Data Analytics course
- Proficiency in at least one programming language such as Java or Python
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At Course Completion
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
- Module 1 Title MapReduce and Hadoop
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Module 1 Content
Lesson 1: The MapReduce Framework
Lesson 2: Apache Hadoop
Lesson 3: Hadoop Distributed File System
Lesson 4: YARN
- Module 2 Title Hadoop Ecosystem and NoSQL
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Module 2 Content
Lesson 1: Hadoop Ecosystem
Lesson 2: Pig
Lesson 3: Hive
Lesson 4: NoSQL - Not Only SQL
Lesson 5: HBase
Lesson 6: Spark
- Module 3 Title Natural Language Processing
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Module 3 Content
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 Title Social Network Analysis
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Module 4 Content
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 Title Data Science Theory and Methods
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Module 5 Content
Lesson 1: Simulation
Lesson 2: Random Forests
Lesson 3: Multinomial Logistic Regression
- Module 6 Title Data Visualization
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Module 6 Content
Lesson 1: Perception and Visualization
Lesson 2: Visualization of Multivariate Data
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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.
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 (DCA-DS) certification exam.
Additional Info
- Certification Course & Certificate
- Course Code EMCDSA
- Price RM12250
- Exam Price Exclude
- Exam Code DEA-7TT2
- Duration 5 Days
- CertificationInfo EMC Data Scientist Associate
- Principals Dell-EMC
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Schedule
22-26 Jan 2024
26 Feb 2024 - 1 Mar 2024
11-15 Mar 2024 (Penang Date)
3-7 Jun 2024
9-13 Sep 2024
14-18 Oct 2024 (Penang Date)
16-20 Dec 2024
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Audience
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
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Prerequisities
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
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At Course Completion
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
- Module 1 Title Introduction to Big Data analytics
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Module 1 Content
- Big Data and its characteristics Lesson
- Business value from Big Data
- Data scientist
- Module 2 Title Data Analytics Lifecycle
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Module 2 Content
- Data analytics lifecycle overview
- Discovery phase
- Data preparation phase
- Model planning phase
- Model building phase
- Communicate results phase
- Operationalize phase
- Module 3 Title Basic data analytics methods using R
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Module 3 Content
- Introduction to the R programming language
- Analyzing and exploring data
- Statistics for model building and evaluation
- Module 4 Title Advanced analytics theory and methods
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Module 4 Content
- Introduction to advanced analytics—theory and methods
- K-means clustering
- Association rules
- Linear regression
- Logistic regression
- Text analysis
- Naïve Bayes
- Decision trees
- Time series analysis
- Module 5 Title Advanced analytics—technology and tools
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Module 5 Content
- Introduction to advanced analytics—technology and tools
- Hadoop ecosystem
- In-database analytics SQL essentials
- Advanced SQL and MADlib
- Module 6 Title Putting it all together
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Module 6 Content
- Preparing to operationalize
- Preparing project presentations
- Data visualization techniques
In addition to lecture and demonstrations, this course includes labs designed to allow practical experience for the participant.
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