The Introduction to Data Science class will survey the foundational topics in data science, namely:
- Data Manipulation
- Data Analysis with Statistics and Machine Learning
- Data Communication with Information Visualization
- Data at Scale -- Working with Big Data
- Introduction to R
The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science.
- Any Graduates(B.A,B.Com,B.Sc)
- Engineering Students(B.Tech, B.E, M.Tech)
- Any Diploma Holder
- Any Working Professionals
- Data Warehouse Administrators
- Database Administrators
- Software Tester
- Project Manager
- MIS Support
Data Science Overview
Accelerate your career in data science by mastering concepts of Data Management, Statistics, Machine Learning and Big Data from most influential Data Science leaders.
Learn. Experience. Master.
- For the Industry, By the Industry
- Domain Specialization
- Career Guidance
Data Science – A Definition
Data Science is the science which uses computer science, statistics and machine learning, visualization and human- computer interactions to collect, clean, integrate, analyze, visualize, and interact with data to create data products.
What is a data scientist to do?
These data scientists, who have expertise in computer applications, data modeling, statistical analysis, and more. Not only analyzing and resolving informational problems of the organization, their role in providing information security is very important.
Thanks to the digital revolution that is sweeping the world and India in particular, data scientists are now the most sought-after professionals by big corporations as well as startups. And companies across industries are rewarding good data analysts and scientists with desirable career growth and salaries.
Learn by Application
Learn from our comprehensive collection of case-studies, hand-picked by industry experts, to give you an in-depth understanding of how data science moves industries like telecom, transportation, e-commerce & more.
Statistics with R
- Understanding R and its data structures
- Descriptive statistics
- Data Distribution
- Hypothesis testing
- Graphs and plots
- Summary statistics etc
- Linear Regression and case study
- Logistic Regression and case study
- Case studies in Regression
Time Series Analysis
- Stationary Process
- Augmented Reality Process
- Moving Average Process
- Auto Regressive Integrated Moving Average Model
- ARIMA Modelling using R with examples
Classification Learning Objectives
- Meaning and types of classification
- Decision tree classifier
- K-nearest neighbors
- Neural Network
- Support vector machines
- Naive Bayes
- Probabilistic methods
- Random forest classifier
- What is Clustering ?
- k-means Clustering?
- Hierarchical clustering
Market Basket Analysis
- What is Market Basket
- What is Market Basket Model
- Association Rule mining and case study
- Credit Risk evaluation using R
- Hands on Training
- Learners are taught to understand business intelligence and business and data analytics.
- To understand the business data analysis through the powerful tools of data application.
- Learn how to apply Tableau, MapReduce, and get introduced in to R and R+.
- Understand the methods of data mining and creation of decision tree.
- Explore different aspects of Big Data Technologies.
- Learn the concepts of loop functions and debugging tools.