Big Data Analytics Training
CREDO SYSTEMZ provides the best Big Data Analytics Training in Chennai. Business Analytics training by experienced professionals with top placements and 100% certification track record.
Big Data Analytics Training in Chennai – Business Analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.
Business Analytics makes extensive use of statistical analysis, including explanatory and predictive modeling and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, online analytical processing (OLAP).
Overview
This course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It includes an introduction to big data and the Data Analytics Lifecycle to address business challenges that leverage big data. The course provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. Labs offer opportunities for students to understand how these methods and tools may be applied to real-world business challenges as a practicing data scientist. The course takes an “Open”, or technology-neutral approach, and includes a final lab in which students address a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle.
Audience
This course is intended for individuals seeking to develop an understanding of Data Science from the perspective of a practicing 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 expertise to learn many of the more advanced tools and analytical methods taught in the course.
Course Objectives
Upon successful completion of this course, participants should be able to:
- Immediately participate and contribute as a Data Science Team Member on big data and other analytics projects by:
- Deploying the Data Analytics Lifecycle to address big data analytics projects o Reframing a business challenge as an analytics challenge
- Applying appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
- Selecting appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences
- Using tools such as: R and RStudio, MapReduce/Hadoop, in-database analytics, Window and MADlib functions
- Explain how advanced analytics can be leveraged to create competitive advantage and how the data scientist role and skills differ from those of a traditional business intelligence analyst
The topics covered in the course are,
BIG DATA ANALYTICS TRAINING COURSE MODULES
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
Minimum educational qualifications required:
- 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
KEY FEATURES
- Professional approach towards training using latest techniques
- Minimal batch strength to give individual attention to all
- Unlimited lab / practice environment access provided to all candidates
- Flexible batch schedule – Attend missed sessions with next batch
- We provide free placement assistance because we care about your career
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