Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 2: Contextual Knowledge
Data Analytics Part II
This program is designed to help Developers, Data Analysts, and Data Engineers design, build, secure, and maintain analytics solutions. The digital training included in this program will expose you to the fastest way to get answers from all your data to all your users. This program can also help prepare you for the AWS Certified Data Analytics - Specialty certification exam.
This is the second part of the program, the first part is in Data Analytics Part I. If you are interested in additional resources you can explore the Ramp-Up Guide: Data Analytics.
This is the second part of the program, the first part is in Data Analytics Part I. If you are interested in additional resources you can explore the Ramp-Up Guide: Data Analytics.
Courses in this program
11) Data Analytics Fundamentals
In this self-paced course, you learn about the process for planning data analysis solutions and the various data analytic processes that are involved. This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data. This includes learning basic architectures, value propositions, and potential use cases. The course introduces you to the AWS services and solutions to help you build and enhance data analysis solutions.
In this course, you will learn how to:
In this course, you will learn how to:
- Identify the characteristics of data analysis solutions and the characteristics that indicate such a solution may be required.
- Define types of data including structured, semistructured, and unstructured data.
- Define data storage types such as data lakes, AWS Lake Formation, data warehouses, and the Amazon Simple Storage Service (Amazon S3).
- Analyze the characteristics of and differences in batch and stream processing.
- Define how Amazon Kinesis is used to process streaming data.
- Analyze the characteristics of different storage systems for source data.
- Analyze the characteristics of online transaction processing (OLTP) and online analytical processing (OLAP) systems and their impact on the organization of data within these systems.
- Analyze the differences of row-based and columnar data storage methods.
- Define how Amazon EMR, AWS Glue, and Amazon Redshift each work to process, cleanse, and transform data within a data analysis solution.
- Analyze the concept of atomicity, consistency, isolation, and durability (ACID) compliance as well as basic availability, soft state, eventual consistency (BASE) compliance and how an extract, transform, load (ETL) process can help to ensure compliance.
- Explore the concept of data schemas and understand how they define data and how this information is stored in metastores.
- Analyze the concept of data versus information.
- Recognize the ways to analyze data to produce information for reports using tools such as Amazon QuickSight and Amazon Athena.
- Define how AWS services work together to visualize data.
Estimated effort 4 hours
English language
12) AWS Hadoop Fundamentals
AWS Hadoop Fundamentals introduces you to the basics of big data and how Hadoop as a framework handles it. This course discusses Hadoop architectures and how large sets of data are stored and processed. The course explains several tools used in the process: MapReduce, Hive, and Pig. The course also examines Hadoop as part of the AWS big data ecosystem.
In this course, you will learn how to:
In this course, you will learn how to:
- Describe the Hadoop framework and tools used.
- Explain what MapReduce is and how it processes data.
- Explain how the Hive data warehouse system is leveraged with Hadoop.
- Identify the components of Hive and Pig.
- Describe the Pig Latin query language.
- Recognize how Hadoop fits into the AWS big data ecosystem.
13) Deep Dive into Concepts and Tools for Analyzing Streaming Data on AWS
In this course, we will introduce basic stream processing concepts and discuss strategies that are commonly used to address the challenges that arise from querying of streaming data. We will discuss different time semantics, processing guarantees, and elaborate how to deal with reordering and late arriving of events. We will also address the benefits of managing state inside a stream processing engine and highlight how it changes the way we build architectures in a streaming domain.
14) Introduction to Amazon Kinesis Data Analytics for Java Applications
The new support for Java programming in Amazon Kinesis Data Analytics helps you solve challenges, and this course will show you how. You’ll also learn how the SDKs are supported through Apache Flink libraries and see how it works in real-world use cases.
15) Serverless Analytics
Customer data comes in all shapes and forms and from every direction. It’s more critical than ever to connect and process all of that data, so that you can enable more data-driven decisions. In this course, Glenn Gillen will show you how to synthesize all of that disparate data using the power of tools like AWS IoT Analytics, Amazon Cognito, AWS Lambda, and Amazon SageMaker, to name a few. You will learn how to aggregate, process, store, and deliver actionable data in new and powerful ways.
16) Why Analytics for Games
This course addresses the use of analytics in gaming use cases. Learners will explore the benefit of analytics and how insights can be used to improve game design, increase efficiency of game operations, and inform financial and strategic decisions. Learners will see different sources and types of game data to use for business intelligence and how an analytics pipeline can be used to translate game data to answers.
This course is designed to teach you how to:
This course is designed to teach you how to:
- Describe the business case for analytics in the games industry.
- Identify and describe business questions about games and data sources to provide answers.
- Identify and describe types of data to provide answers to business questions.
- Describe the key components of an analytics pipeline.
17) Exam Readiness: AWS Certified Data Analytics - Specialty
The AWS Certified Data Analytics – Specialty exam validates technical skills and experience in designing and implementation AWS services to derive value from data. This course helps you prepare for the exam by exploring the exam’s topic areas and familiarizing you with the question style and exam approach. The course reviews sample exam questions in each topic area and teaches you how to interpret the concepts being tested so you can more easily eliminate incorrect responses.
The course addresses each of the exam’s content domains:
The course addresses each of the exam’s content domains:
- Data collection systems.
- Storage and data management concerns.
- Data processing solutions.
- Analysis and visualization of analytical data.
- Security of the data analysis system.