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

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.

[lachmantewary]

So far the best source available on web to brush up stats concepts for Data Science.
Cheers to Edureka & Zulaikha.

[priyankarsinha]

Great Explanation on the IG and Entropy part. Thanks for that!

[goutamnayak]

Excellent explanation, thank you for making such a video which explains the concepts so clearly

[abhishekkhatana]

Amazing coverage of statistics.
Please make a video over mathematics required in data science. I am very confused like what to cover and how much to cover in mathematics.

[flyeagle]

Thank you so much for making video on complete probability, statistics in one video

[anafashraf]

It is humble request if you will share pdf of this video ?

You people have done a great, amazing work
Thanks

[Supri_d]

thank you so much for sharing this video😇😇

[dipanshusingh]

The way she is teaching 👌
Awesome, it clear my all doubt🤟

[lalitpandey]

I had been trying to understand IG and entropy but never got such a simple explanation. Thanks very much Edureka team :)🙂🙂

[ygproduction]

Your voice is really good. It is so sweet just like needed for the students to capture their attention on the subject matter

[RAJKUMAR-ellj]

Mam I am new to learn data science what is the sequence of concept which I have to follow and is any prerequisite required before this
Or if I randomly learning anything for this is good or bad ???
Please mam ????

[narenspirit]

concise and informative...thank u so much...Edureka team.

[souhardyaganguly]

This was really helpful!! Thanks for uploading!!

[RohitChouhan-djse]

THANKS FOR THE QUALITY VIDEO AND MINE OF INFORMATION 👍🏼

[basunnaidu]

INTRESTING & KNOWLEDGEFUL and stepping stone to SUCCESS.

[ajith.studyingmtech.atbits]

Superb session, Thanks to Edureka and trainer, crisp precise and well modulated - where can I get a copy of this presentation for reference?

[JustWindSurf]

Amazing Video! I really tried answering the question for Bayes Theorem but couldnt figure it out... Could you help me out please?

[zilpahnamutali]

thanks a lot, i learned a lot i did not know

[sangrambahadur]

Good Evening ma'am thank you for awesome video and Your voice like Madhuri Dixit. lovely voice.

[ShivamGupta-ylir]

Thanks a lot.. Mam and Edureka team.. Very good content.. And.. Nice explaination by mam..