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Statistics And Probability Tutorial | Statistics And Probability for Data Science | Edureka

🔥 Data Science Certification using R (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/data-science This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language. The following topics are covered in this session: 3:23 What Is Data? 4:17 Categories Of Data 9:01 What Is Statistics? 11:20 Basic Terminologies In Statistics 12:35 Sampling Techniques 17:46 Types Of Statistics 20:22 Descriptive Statistics 21:25 Measures Of Centre 25:40 Measures Of Spread 32:06 Information Gain & Entropy 44:13 Confusion Matrix 49:00 Descriptive Statistics Demo 53:09 Probability 55:33 Terminologies In Probability 57:46 Probability Distribution 1:03:00 Types Of Probability 1:10:00 Bayes' Theorem 1:15:34 Inferential Statistics 1:16:09 Point Estimation 1:19:05 Interval Estimation 1:22:23 Margin Of Error 1:22:57 Estimating Level Of Confidence 1:26:25 Hypothesis Testing 1:30:25 Inferential Statistics Demo Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram:   / edureka_learning   Facebook:   / edurekain   Twitter:   / edurekain   LinkedIn:   / edureka   - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyze Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyze data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies. For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.