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This Edureka tutorial on “Statistics for Data Science” talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
0:58 Introduction to statistics
1:42 Basic Terminology
2:53 Categories in Statistics
5:12 Descriptive Statistics
11:17 Reasons for moving to R
12:50 Descriptive Statistics in R Studio
14:16 Inferential Statistics
16:53 Inferential Statistics using R Studio
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1. This is a 30-hour Instructor-led Online Course.
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3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!
About the Course
Edureka’s Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.
Who should go for this course?
The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
Developers aspiring to be a ‘Data Scientist’
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
‘R’ professionals who wish to work Big Data
Analysts wanting to understand Data Science methodologies
Why learn Data Science?
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.
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