Big Data and Analytics
Overview:
Introduction:
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
Course Objectives:
At the end of this course the participants will be able to:
- Understand the Big Data Platform and its Use cases
- Provide an overview of Apache Hadoop
- Provide HDFS Concepts and Interfacing with HDFS
- Unstructured Data
- Exposure to Big Data Analytics
- Provide hands-on Hadoop Eco System
Targeted Audience:
- Essentially, big data allows companies to know their customers in order to improve their marketing strategies and the customer experience
- Big data provides crucial insight into your customers, including demographics, geographic location, and how they interact with your company in real time.
Course Outlines:
Unit 1:
- Enables organizations to separate storage from computing requirements
- Provides cost-effective elasticity for data-centric workloads
- Can easily be deployed and leveraged both on-premises and in the cloud
Unit 2:
- Data Models, Facts, and Dimensions Data
- Structured Data
- Unstructured Data
- Semi-Structured Data.
Unit 3:
- Big Data analyst wears multiple hats, frequently switching gears
- A Big Data analyst wears multiple hats, frequently switching gears from conducting research to mining data for information to presenting findings.
- Collecting, analyzing, visualizing, and communicating this data to help guide these future decisions.
- Social media, cloud applications, and machine sensor data
Unit 4:
- A Data Analyst role is better suited for those who want to start their career in analytics
- A Data Scientist role is recommended for those who want to create advanced machine learning models and use deep learning techniques to ease human tasks.
- Skilled data analysts are some of the most sought-after professionals in the world
Unit 5:
- Big data refers to any large and complex collection of data.
- Data analytics is the process of extracting meaningful information from data
- Data science is a multidisciplinary field that aims to produce broader insights.
- Data analysts rely on skills like programming in R or Python, querying databases with SQL, and performing statistical analysis.