Big Data Analytics Course And Certification

What is Big Data Analytics?  

Big Data Analytics is the complex method of examining large and diverse data sets to reveal relevant information including hidden patterns, unknown correlations, market trends and client preferences that can assist organizations make informed business decisions. 

Big Data Analytics includes mainly gathering data from various sources, mungling it in such a manner that it becomes accessible to analysts and users to consume. 

Advantages of Big Data Analytics 

The benefits of Big Data Analytics include: 

Data Procurement: A data analyst can gather and store enormous amounts of information from different sources such as webpages, directories etc. In order to use these data in a snap as at when needed, a well-guided system must be in place. Big Data Analytics helps in data procurement.

Data Segmentation: There are times when a real estate agency wishes to distribute her information on the basis of various parameters such as, Gender, Age, Income Group, Location, Budget, segment customers, market segmentation, product segmentation, etc. Big Data Analytics helps in proper data segmentation.

Checks Variability: Data flows may be extremely incompatible with regular peaks in relation to growing information speeds and data variants. So Big Data Analytics helps to check this variability.

Checks Complexity: The information today comes from various sources, making it hard to connect, match, clean and convert information across systems. However, relationships, hierarchies and various information linkages need to be connected and correlated or your information can spiral out of control rapidly. Big Data Analytics helps in checking data complexity.

Business Intelligence: This is a technology-based process for analyzing data and presenting actionable information to help executives and managers, including corporate end users make informed business decisions. 

Identify New Opportunities: It helps organization to harness their data and use it to identify new opportunities.

Decision Making: It helps organization to make better, informed and faster decisions.

Market Predictions: It helps organizations to be able to predict market outcomes and how it will affect organizational goals and objectives.

Competitive Edge: It gives the organization competitive edge over others. 

Accurate Measurement: It offers accurate measurement of data.

Data Visualization: It allows the researchers to be able to visualize data.

Saves Cost/Time: It helps to save cost and time.

Market Conditions: It helps to understand market conditions and directions in order to plan ahead.

Main Features of Big Data Analytics 

Predictive Applications-Identity Management: (or Identity and Access Management) is the method of managing organizations that have access to your information. 

Real-time Reporting: collects information minute by minute, typically in an intuitive dashboard format, and relays it to you.

Security Features: It is essential for a successful company to keep your system secure.

Analytics Features: The provision of tools for users with a multitude of analytics packages and modules.

Data Processing Features: This involves collecting and organizing raw information in order to generate significance.

Technologies Support: It supports variety of technologies and tasks that may be useful to you.

Types of Big Data Analytics: 

Types of Big Data Analytics include:

1. Descriptive Analytics (past)

2. Predictive Analytics (future)

3. Prescriptive Analytics (environmental Analytics) and

4. Diagnostic Analytics (failure and success rate of an event).

Big Data Analytics Tools: 

The Tools used in Big Data Analytics are:

1. Hadoop: Data Processing and Storage.

2. Kafka: Data Warehousing.

3. Apache H – Base: No – SQL Database.

4. Splunks: Log Analytics Platform.

5. Talend: Software Integration. 

6. Apache Spark: Real–time Processing.

Big Data Analytics Skillset: 

Big Data Analytics Skillset include:

1. Basic Programming

2. Data Visualization

3. Statistical and Quantitative Analysis

4. Specific Business Knowledge

5. Computational Frameworks e.g. Hadoop.

6. Data Warehousing e.g SQL and NO SQL.

In the Full Course, you will learn everything you need to know about Big Data Analytics with Certification upon successful completion of exams. 

Big Data Analytics Course Outline: 

Big Data Analytics - Introduction/Overview

Big Data Analytics - Data Life Cycle

Big Data Analytics - Methodology

Big Data Analytics - Core Deliverables

Big Data Analytics - Key Stakeholders

Big Data Analytics - Data Analyst

Big Data Analytics - Data Scientist

Big Data Analytics - Problem Definition

Big Data Analytics - Data Collection

Big Data Analytics - Cleansing data

Big Data Analytics - Summarizing

Big Data Analytics - Data Exploration

Big Data Analytics - Data Visualization

Big Data Analytics - Introduction to R

Big Data Analytics - Introduction to SQL

Big Data Analytics - Charts & Graphs

Big Data Analytics - Data Tools

Big Data Analytics - Statistical Methods

Big Data Analytics - Machine Learning for Data Analytics

Big Data Analytics - Naive Bayes Classifier

Big Data Analytics - K-Means Clustering

Big Data Analytics - Association Rules

Big Data Analytics - Decision Trees

Big Data Analytics - Logistic Regression

Big Data Analytics - Time Series

Big Data Analytics - Text Analytics

Big Data Analytics - Online Learning

Big Data Analytics - Exams and Certification


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