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Greetings, I’m Rayno. I’m a student living in Bengaluru, India. I am a fan of web development, technology, and design. You can visit my company website with a click on the button above.

I am working for IQ Stream technologies - a software training company located at Bangalore BTM layout area. IQST also offers top quality data science training in Bangalore with 100% placements.

Data science courses help you to estimate the specific return oneach of your marketing strategies if you are from a marketing industry. Moreover, data science can help you to analyze your business better.

Data science course includes theory and programming labs. Execution of various periods of examination activities, for example, data control, statistics etc will be covered.

You will be learning the fundamentals of Data Science, like data measurement, charts, and graphs, measures of central tendency and shapes, deep knowledge of statistics, computer science, mathematical functions and data analysis etc.

Visualization Goals, Data Types, and Statistical Graphs also covered in the data science training course provided by IQ Stream Technologies at Bangalore center. Theory and practicals of R Programming also included in the course. After completing this course, tou will be able to gain in–depth insight into your marketing goals, strategies and success.

Enroll now!

Course details: https://datascience.columbia.edu/course-inventory Prerequisites: The pre-requisite for this course includes working knowledge in high school math and understanding of scientific research. Prior programming experience is NOT required. Description: Data Science is a dynamic and fast growing field at the interface of Statistics and Computer Science. The emergence of massive datasets containing millions or even billions of observations provides the primary impetus for the field. Such datasets arise, for instance, in large-scale retailing, telecommunications, astronomy, and Internet social media. It is essential for non-datascientists such as users of data-driven solutions and policy makers to understand the principles, methods and technologies used in data science and how the data science approach is being used to revolutionize decision process in both private and public sectors. This course will provide a broad overview of data science’s different areas from statistics, machine learning to data engineering and many data science applications. This course is designed to provide anon-technical introduction to the data science approach. It is intended to students from non-quantitative fields. It shall not be count towards degree requirements for quantitative graduate programs such as Statistics, Computer Science, Operations Research, or Data Science.Students should inquire with their respective programs to determine eligibility of this course to count towards minimum degree requirements.Course organization. The class meets weekly, following a flipped classroom approach. Well-designed pre-recorded lectures, readings and practices will be assigned before each meeting for students to complete on their own outside the classroom. During the class meeting time, we will go over in detail some hands-on exercises, discuss the lecture and reading materials and answer questions. Tutorials on R and other easy-to-use data science softwares will also be given.Below is a tentative schedule we will follow.Week 1 (9/6): What is Data Science?Tutorial: Intro to RWeek 2 (9/13): Intro to statistical thinking (I)Tutorial: Basic data processing using RWeek 3 (9/20): Intro to statistical thinking (II)Tutorial: Basic data analysis using RWeek 4 (9/27): Introduction to Bayesian modelingTutorial: TBDWeek 5 (10/4): Exploratory Data Analysis and Visualization (I)Tutorial: Introduction to TableauWeek 6 (10/11): Exploratory Data Analysis and Visualization (II)Tutorial: Easy interactive graphicsWeek 7 (10/18): AlgorithmsTutorial: Write an R functionWeek 8 (10/25): Machine learning (I)Tutorial: Machine learning using RWeek 9 (11/1): Machine learning (II)TUtorial: Machine learning using RWeek 10 (11/15): Privacy and data securityProject-related tutorials: knitr Week 11 (11/22): Data EngineeringProject-related tutorials: shiny appWeek 12 (11/29): Internet of Things Project-related tutorials: ggplot2Week 13 (12/6): Project presentations