Big Data Analytics Provides you with the effective use of statistical analysis which includes detailed and predictable Data Modeling. The Predicted Data Modeling is based on current fact-based data Analytics which drive you to make your decision perfect. So that it's closely related to Management Science. The Big Data Analytics will be applied for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, online analytical processing (OLAP).
Big Data Analytics helps organizations to use their data in an effective way to find new opportunities in the market. The Data Analytics leads to move your business into smarter, more streamlined operations, greater profits with satisfied customers. 
2. Faster, better decision making - The combination of High speed of Hadoop and in-memory analytics, we can analyze new sources of data easily. Also, we can make decisions based on what we have learned to analyze information immediately.
3. New products and services - As per the customer needs and satisfaction through analytics leads to the power to give customers what they want.
1. Cost reduction - Big data technologies such as Hadoop and cloud-based analytics makes significant cost advantages when it comes to storing large amounts of data. Also, they can find more productive ways of doing business.
2. Faster, better decision making - The combination of High speed of Hadoop and in-memory analytics, we can analyze new sources of data easily. Also, we can make decisions based on what we have learned to analyze information immediately.
3. New products and services - As per the customer needs and satisfaction through analytics leads to the power to give customers what they want.
- Bachelor’s/Diploma in any branch of Engineering or Technology from a recognized Institution
- Bachelor’s degree (with mathematics at least up to the PUC level or equivalent) from a recognized University or
- Institution
- Candidate shall possess a minimum of one year’s working experience in IT/ITeS, Industrial, Commercial or ScientificOrganizations
Faculty : Trained Statistics Professional from ISI (Indian Statistical Institute), Bangalore Course Duration: 40 Hours
Who can learn Big Data Analytics
This course is intended for individuals seeking to develop an understanding of Data Science from the perspective of a practising Data Scientist, including:
- Managers of teams of business intelligence, analytics, and big data professionals
- Current Business and Data Analysts looking to add big data analytics to their skills.
- Data and database professionals looking to exploit their analytic skills in a big data environment
- Recent college graduates and graduate students with academic experience in a related discipline looking to move into the world of data science and big data
- Experience with a scripting language, such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open-source statistical tool and programming
- Experience with SQL (some course examples use Consider the above as a list of specific prerequisite (or refresher) training and reading to be completed prior to enrolling for or attending this course. Having this requisite background will help ensure a positive experience in the class, and enable students to build on their expertize to learn many of the more advanced tools and analytical methods taught in the course.
Course Features
- Duration60 hours
- Skill levelAll level
- Batch Strength15
- AssessmentsYes
- Mock InterviewsYes
- Resume BuildingYes
- PlacementsYes
- Flexible TimingYes
- Fee InstallmentsYes
- LanguageTamil/English
Chapter 1: Introduction to Big Data Analytics
- Big Data Overview
- State of the Practice in Analytics
- The Data Scientist
- Big Data Analytics in Industry Verticals
Chapter 2: Data Analytics Lifecycle
- Discovery
- Data Preparation
- Model Planning
- Model Building
- Communicating Results
- Operationalizing
Chapter 3: Review of Basic Data Analytic Methods
- Using R
- Using R to Look at Data
Chapter 4: Introduction to R
- Analyzing and Exploring the Data
- Statistics for Model Building and Evaluation
Chapter 5: Advanced Big Data Analytics
- Theory and Methods
- K Means Clustering
- Association Rules
- Linear Regression
- Logistic Regression
- Naïve Bayesian Classifier
- Decision Trees
- Time Series Analysis
- Text Analysis
Chapter 6: Advanced Big Data Ecosystem Analytics
- Technologies and Tools
- Analytics for Unstructured Data
- MapReduce and Hadoop
- The Hadoop Ecosystem
- In-database Analytics
- SQL Essentials
- Advanced SQL and MADlib for In-database Analytics
Putting It All Together
- Operationalizing an Analytics Project
- Creating the Final Deliverables
- Data Visualization Techniques
- Final Lab Exercise on Big Data Analytics
You will be going through detailed 2 to 3 months of Big Data Analytics Hands-on training
- Detailed instructor led sessions to help you become a proficient Expert in Big Data Analytics.
- Build a Big Data Analytics professional portfolio by working on hands on assignments and projects.
- Personalised mentorship from professionals working in leading companies.
- Lifetime access to downloadable Big Data Analytics course materials, interview questions and project resources.
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