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

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** Data Science Certification using R: **
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

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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.

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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

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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 or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information. .

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