Free Online & Classroom Demo On Data Science

LEARNING OUTCOMES – DATA SCIENCE TRAINING PROGRAMUnderstand about the landscape of the various data generation sources
Perform text mining to generate the sentiment analysis of customers
Learn about the tools and techniques used in the space of analyzing both structured and unstructured data
To understand the differences between descriptive analytics and predictive analytics
To understand the data driven machine learning approaches in taking critical business decisions
To understand on how to build prediction models for day-to-day applicability
To understand on how to perform forecasting to take proactive business decisions
Learn about representing data in most representable format using data visualization concepts
WHO SHOULD ATTEND DATA SCIENCE CERTIFICATIONCandidates aspiring to be Data Scientist, Big Data Analyst, Analytics Manager / Professional, Business Analyst, Developer in Data Analytics
Graduates who are looking to build a career in Data Science, Machine Learning, Forecasting, Business Intelligence, etc.
Employees of organizations, who are planning to shift to Big Data tools
Academicians and researchers with keen eye to data-driven decision making
Finally, students who are aiming to work in IT industry on emerging technologies
METHODOLOGY
Lecture with a blend of theoretical and practical exposure
Hands-on exercise to reiterate the learning
Quizzes to test the understanding
Discussions and final exam to attain certificate
Internship opportunity for eligible candidates
Live project opportunity to engage and explore Data Science Learning with USA based Analytics Consulting firm - Innodatatics
MODULES - DATA SCIENCE PROGRAM
Introduction to Data Science, Data Sources, Opportunities
Application of Data Science across various Industries, Sectors, Domains
Project Management Overview of Handling Data Science Projects
Data Preparation, Data Cleansing, Exploratory Data Analysis
Feature Engineering, Feature Extraction, Feature Selection
Hypothesis Testing, Regression Techniques
Predictive Modelling using Advanced Regression Techniques
Data Mining Unsupervised - Clustering, Association Rules, etc.
Text Mining, Natural Language Processing
Data Mining Supervised / Machine Learning Algorithms
Black Box Techniques - Neural Networks, SVM, etc.
Time Series Analysis / Forecasting

Others Details

If you want more information please contact : 9989994319