Online-Data-Analytics-Course

Online courses allow the students to set their own learning steps, and there’s the added flexibility of setting a schedule that fits everyone’s agenda. As a result, using an online educational program allows for a finer balance of work and studies, so there’s no need to give anything up. We at Knowledge Uplift provide the best online courses to our students under the supervision of our most qualified teachers. Our online data analytics course is one of the best courses we offer to our online students.

Key benefits of online learning with us:

You can work from anywhere, at any time as we know that you want to make balance with your duties. Since everything is available online by our teachers, accessing class materials and submitting work is very convenient.

Sometimes students need more time to think before sharing. In an online class, students can spend as much time as they want thinking about and sharpen their own ideas. This can lead the way to greater confidence and more refined discussions.

Our distance learning programs, however, foster virtual communication and allow students to work with team members via email, chat rooms, and other easy-to-use methods.

At colleges and universities, talking to a professor after class can be difficult. Yes, instructors have office hours, but it’s frequently only an hour or two each week, with too many students waiting for notice. While our professors who teach online may also have set hours for student interaction, web-based technologies make conversing with multiple students at once much easier. Among other courses, our online data analytics course makes our site one of the best teaching sites in the world. Top of form bottom of Form.

What is big data analytics?

It is the usage of modern analytic techniques against enormous, diverse big data sets that involve structured, semi-structured, and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

What is big data exactly?

It is described as data sets whose size or type is deeply the ability of traditional relational databases to capture, control, and process the data with low latency. Features of big data include high volume, high velocity, and high variety. Sources of data are becoming more composite than those for traditional data because they are being operated by artificial intelligence (AI), mobile devices, social media, and the Internet of Things (IoT). With big data analytics, you can eventually feel better and faster decision-making, modeling and predicting future end results, and increased business intelligence. However, Big Data analytics is the procedure of collecting, organizing, and analyzing large sets of data (called Big Data) to locate patterns and other handy information. Analysts working with Big Data generally want the mastery that comes from analyzing the data.

Why is our big data analytics course important?

Big data analytics aids organizations to tackle their data and use it to point out new opportunities. That guides to clever business moves, more productive operations, higher profits, and happier customers. Our best online data analytics courses enable you to master this skill. The benefits that big data analytics brings to the table, however, are speed and productivity. One or two years ago a business would have collected information, run analytics, and dig information that could be used for future decisions. Today, that business can identify insights for speedy decisions. The capability to work faster – and stay supple – gives organizations a ruthless edge they didn’t have before. That’s why we are offering data analyst certification online courses to make it more accessible for our students.

Details of our analytics courses online:

This specialized data analyst certification online course introduces you to cloud-based architecture and design patterns to build highly scalable big data solutions using Amazon Elastic Map Reduce (EMR), Amazon Redshift, Amazon Kinesis, Amazon Glue, Amazon Athena, Data Lake, Elastic Search, and the rest of the AWS big data program. Through these best online data analytics courses, you come to know how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue.

You acquire a deeper comprehension of how to create big data environments, work with Amazon Dynamo DB, Amazon Redshift, Amazon Quick Sight, Amazon Athena, and Amazon Kinesis. Hold best practices to plan big data environments for security and cost-effectiveness through these analytics courses online.

This online data analytics course is 60% workshop driven with hands-on labs for various big data platform services.

Module 1

Introduction to AWS Analytics

Overview of Big Data

Big data ingestion, processing, and analytics

Big data streaming and Amazon Kinesis

The Amazon EMR and Hadoop

Amazon Quick Sight

The Amazon EMR and Spark

AWS Glue

Amazon Redshift

AWS Lake Formation

Module 2

Deep dive into EMR

Day1-Material

Day2-Lab

Using Amazon Elastic Map Reduce.

Storing and Querying Data on Dynamo DB.

Hadoop Programming Frameworks.

Processing Server Logs with Hive on Amazon EMR.

Streamlining Your Amazon EMR Experience with Hue.

Running Pig Scripts in Hue on Amazon EMR.

Spark on Amazon EMR.

Altering New York Taxi dataset using Spark on Amazon EMR

Module 3

Deep dive – AWS Glue/Athena/Lake Formation

Day1-Material

Day2-Lab

Using AWS Glue to automate ETL workloads

Amazon Athena and Big Data

Visualizing and Orchestrating Big Data

Quick Sight- Visualizing

Managing Amazon EMR Costs

Securing Big Data solutions

Big Data Design Patterns

Amazon Lake Formation

Module 4

Date Warehousing Deep Dive

Day1-Material

Day2-Lab

Assess the connection between Amazon Redshift and other Big Data systems.

Assess use cases for data warehousing workloads and inspect the real-world implementation of AWS data and analytic services as part of a data warehousing solution.

Acknowledge which security features are suitable for Amazon Redshift, such as encryption, IAM permissions, and database permissions.

Plan the data warehouse to make productive use of compression, data distribution, and sort methods.

Review the Redshift spectrum and materialized views.

Fill and unload data and carry out data maintenance tasks.

Write questions and assess query plans to optimize query performance.

Construct the database to allocate resources such as memory to query and define criteria to route certain types of queries to your configured query queues for improved processing.

Audit, monitor, and receive event notifications about activities in the data warehouse by using features and services such as Amazon Redshift database audit logging, Amazon Cloud Trail, Amazon Cloud Watch, and Amazon Simple Notification Service (Amazon SNS)

Module 5

Streaming

Day1 – Workshop

Acknowledge how to question streaming data or build entire streaming applications using SQL. In this online data analytics course, we discuss how the service collects, processes, and analyzes streaming data in real-time. Review Amazon Kinesis firehose loading into S3 pattern.

Amazon Kinesis stream review.

Module 6

Elastic Search

Day1 – Workshop

Deep dive into Amazon Elastic search.

Components of Elastic search.

Review architecture and integration patterns.

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