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 to consume.
Data Procurement: A developer can store enormous amounts of information. They also gather information from different websites etc. In order to use that data in a snap when necessary a well-guided system must be in place.
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, other ways to segment customers, market segmentation, product segmentation, etc.
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.
It offers accurate measurement.
It helps in decision making.
It allows the researchers to visualize data
It helps in modelling
It helps to predict future outcomes
It helps to save cost and time
It helps to understand market conditions
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 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).
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 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 - 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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