๐ฅ Data Science Master Program (Use Code “๐๐๐๐๐๐๐๐๐”): https://www.edureka.co/masters-program/data-scientist-certification

This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners tutorial video:

00:00 Agenda

2:44 Introduction to Data Science

9:55 Data Analysis at Walmart

13:20 What is Data Science?

14:39 Who is a Data Scientist?

16:50 Data Science Skill Set

21:51 Data Science Job Roles

26:58 Data Life Cycle

30:25 Statistics & Probability

34:31 Categories of Data

34:50 Qualitative Data

36:09 Quantitative Data

39:11 What is Statistics?

41:32 Basic Terminologies in Statistics

42:50 Sampling Techniques

45:31 Random Sampling

46:20 Systematic Sampling

46:50 Stratified Sampling

47:54 Types of Statistics

50:38 Descriptive Statistics

55:52 Measures of Spread

55:56 Range

56:44 Inter Quartile Range

58:58 Variance

59:36 Standard Deviation

1:14:25 Confusion Matrix

1:19:16 Probability

1:24:14 What is Probability?

1:27:13 Types of Events

1:27:58 Probability Distribution

1:28:15 Probability Density Function

1:30:02 Normal Distribution

1:30:51 Standard Deviation & Curve

1:31:19 Central Limit Theorem

1:33:12 Types of Probablity

1:33:34 Marginal Probablity

1:34:06 Joint Probablity

1:34:58 Conditional Probablity

1:35:56 Use-Case

1:39:46 Bayes Theorem

1:45:44 Inferential Statistics

1:56:40 Hypothesis Testing

2:00:34 Basics of Machine Learning

2:01:41 Need for Machine Learning

2:07:03 What is Machine Learning?

2:09:21 Machine Learning Definitions

2:!1:48 Machine Learning Process

2:18:31 Supervised Learning Algorithm

2:19:54 What is Regression?

2:21:23 Linear vs Logistic Regression

2:33:51 Linear Regression

2:25:27 Where is Linear Regression used?

2:27:11 Understanding Linear Regression

2:37:00 What is R-Square?

2:46:35 Logistic Regression

2:51:22 Logistic Regression Curve

2:53:02 Logistic Regression Equation

2:56:21 Logistic Regression Use-Cases

2:58:23 Demo

3:00:57 Implement Logistic Regression

3:02:33 Import Libraries

3:05:28 Analyzing Data

3:11:52 Data Wrangling

3:23:54 Train & Test Data

3:20:44 Implement Logistic Regression

3:31:04 SUV Data Analysis

3:38:44 Decision Trees

3:39:50 What is Classification?

3:42:27 Types of Classification

3:42:27 Decision Tree

3:43:51 Random Forest

3:45:06 Naive Bayes

3:47:12 KNN

3:49:02 What is Decision Tree?

3:55:15 Decision Tree Terminologies

3:56:51 CART Algorithm

3:58:50 Entropy

4:00:15 What is Entropy?

4:23:52 Random Forest

4:27:29 Types of Classifier

4:31:17 Why Random Forest?

4:39:14 What is Random Forest?

4:51:26 How Random Forest Works?

4:51:36 Random Forest Algorithm

5:04:23 K Nearest Neighbour

5:05:33 What is KNN Algorithm?

5:08:50 KNN Algorithm Working

5:14:55 kNN Example

5:24:30 What is Naive Bayes?

5:25:13 Bayes Theorem

5:27:48 Bayes Theorem Proof

5:29:43 Naive Bayes Working

5:39:06 Types of Naive Bayes

5:53:37 Support Vector Machine

5:57:40 What is SVM?

5:59:46 How does SVM work?

6:03:00 Introduction to Non-Linear SVM

6:04:48 SVM Example

6:06:12 Unsupervised Learning Algorithms – KMeans

6:06:18 What is Unsupervised Learning?

6:06:45 Unsupervised Learning: Process Flow

6:07:17 What is Clustering?

6:09:15 Types of Clustering

6:10:15 K-Means Clustering

6:10:40 K-Means Algorithm Working

6:16:17 K-Means Algorithm

6:19:16 Fuzzy C-Means Clustering

6:21:22 Hierarchical Clustering

6:22:53 Association Clustering

6:24:57 Association Rule Mining

6:30:35 Apriori Algorithm

6:37:45 Apriori Demo

6:40:49 What is Reinforcement Learning?

6:42:48 Reinforcement Learning Process

6:51:10 Markov Decision Process

6:54:53 Understanding Q – Learning

7:13:12 Q-Learning Demo

7:25:34 The Bellman Equation

7:48:39 What is Deep Learning?

7:52:53 Why we need Artificial Neuron?

7:54:33 Perceptron Learning Algorithm

7:57:57 Activation Function

8:03:14 Single Layer Perceptron

8:04:04 What is Tensorflow?

8:07:25 Demo

8:21:03 What is a Computational Graph?

8:49:18 Limitations of Single Layer Perceptron

8:50:08 Multi-Layer Perceptron

8:51:24 What is Backpropagation?

8:52:26 Backpropagation Learning Algorithm

8:59:31 Multi-layer Perceptron Demo

9:01:23 Data Science Interview Questions

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Data Science Masters Certification Curriculum, Visit our Website: http://bit.ly/3sw3tJj (Use Code "๐๐๐๐๐๐๐๐๐")

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Thank you edureka for explaining each concept so nicely. Would it be possible for you to share the pdf or any document of the course in order to brush up the concepts quickly

Where can i find ppt of this tutorial ?

Mam please provide this ppt

Heyy, Can commerce student do this course? If Anyone has any idea please share โค๏ธ

Can we get whole course pdf?

Does anyone know where I can find the Jupyter Notebook code files for this youtube video ? I really enjoyed seeing the code but if i had the file I could practice a bit more on my own.

i am a commerecde student and want to learn data science skill for mt Mba will it be helpful or should I go in depth for more ?

2:44 Introduction to Data Science

9:55 Data Analysis at Walmart

13:20 What is Data Science?

14:39 Who is a Data Scientist?

16:50 Data Science Skill Set

21:51 Data Science Job Roles

26:58 Data Life Cycle

30:25 Statistics & Probability

34:31 Categories of Data

34:50 Qualitative Data

36:09 Quantitative Data

39:11 What is Statistics?

41:32 Basic Terminologies in Statistics

42:50 Sampling Techniques

45:31 Random Sampling

46:20 Systematic Sampling

46:50 Stratified Sampling

47:54 Types of Statistics

50:38 Descriptive Statistics

55:52 Measures of Spread

55:56 Range

56:44 Inter Quartile Range

58:58 Variance

59:36 Standard Deviation

1:14:25 Confusion Matrix

1:19:16 Probability

1:24:14 What is Probability?

1:27:13 Types of Events

1:27:58 Probability Distribution

1:28:15 Probability Density Function

1:30:02 Normal Distribution

1:30:51 Standard Deviation & Curve

1:31:19 Central Limit Theorem

1:33:12 Types of Probablity

1:33:34 Marginal Probablity

1:34:06 Joint Probablity

1:34:58 Conditional Probablity

1:35:56 Use-Case

1:39:46 Bayes Theorem

1:45:44 Inferential Statistics

1:56:40 Hypothesis Testing

2:00:34 Basics of Machine Learning

2:01:41 Need for Machine Learning

2:07:03 What is Machine Learning?

2:09:21 Machine Learning Definitions

2:11:48 Machine Learning Process

2:18:31 Supervised Learning Algorithm

2:19:54 What is Regression?

2:21:23 Linear vs Logistic Regression

2:33:51 Linear Regression

2:25:27 Where is Linear Regression used?

2:27:11 Understanding Linear Regression

2:37:00 What is R-Square?

2:46:35 Logistic Regression

2:51:22 Logistic Regression Curve

2:53:02 Logistic Regression Equation

2:56:21 Logistic Regression Use-Cases

2:58:23 Demo

3:00:57 Implement Logistic Regression

3:02:33 Import Libraries

3:05:28 Analyzing Data

3:11:52 Data Wrangling

3:23:54 Train & Test Data

3:20:44 Implement Logistic Regression

3:31:04 SUV Data Analysis

3:38:44 Decision Trees

3:39:50 What is Classification?

3:42:27 Types of Classification

3:42:27 Decision Tree

3:43:51 Random Forest

3:45:06 Naive Bayes

3:47:12 KNN

3:49:02 What is Decision Tree?

3:55:15 Decision Tree Terminologies

3:56:51 CART Algorithm

3:58:50 Entropy

4:00:15 What is Entropy?

4:23:52 Random Forest

4:27:29 Types of Classifier

4:31:17 Why Random Forest?

4:39:14 What is Random Forest?

4:51:26 How Random Forest Works?

4:51:36 Random Forest Algorithm

5:04:23 K Nearest Neighbour

5:05:33 What is KNN Algorithm?

5:08:50 KNN Algorithm Working

5:14:55 kNN Example

5:24:30 What is Naive Bayes?

5:25:13 Bayes Theorem

5:27:48 Bayes Theorem Proof

5:29:43 Naive Bayes Working

5:39:06 Types of Naive Bayes

5:53:37 Support Vector Machine

5:57:40 What is SVM?

5:59:46 How does SVM work?

6:03:00 Introduction to Non-Linear SVM

6:04:48 SVM Example

6:06:12 Unsupervised Learning Algorithms – KMeans

6:06:18 What is Unsupervised Learning?

6:06:45 Unsupervised Learning: Process Flow

6:07:17 What is Clustering?

6:09:15 Types of Clustering

6:10:15 K-Means Clustering

6:10:40 K-Means Algorithm Working

6:16:17 K-Means Algorithm

6:19:16 Fuzzy C-Means Clustering

6:21:22 Hierarchical Clustering

6:22:53 Association Clustering

6:24:57 Association Rule Mining

6:30:35 Apriori Algorithm

6:37:45 Apriori Demo

6:40:49 What is Reinforcement Learning?

6:42:48 Reinforcement Learning Process

6:51:10 Markov Decision Process

6:54:53 Understanding Q – Learning

7:13:12 Q-Learning Demo

7:25:34 The Bellman Equation

7:48:39 What is Deep Learning?

7:52:53 Why we need Artificial Neuron?

7:54:33 Perceptron Learning Algorithm

7:57:57 Activation Function

8:03:14 Single Layer Perceptron

8:04:04 What is Tensorflow?

8:07:25 Demo

8:21:03 What is a Computational Graph?

8:49:18 Limitations of Single Layer Perceptron

8:50:08 Multi-Layer Perceptron

8:51:24 What is Backpropagation?

8:52:26 Backpropagation Learning Algorithm

8:59:31 Multi-layer Perceptron Demo

9:01:23 Data Science Interview Questions

Thanks for such a rich content on DataScience… Can you please share datasets with me used in video tutorial!!

Great course. Well done!

Thank you Edureka for this wonderful course. From where can we get the datasets used in this course? It would be great if you can share

thank you for the beautiful course. is there document with the dataset used or answers to questions given here?

And this kind of contents are for free ๐ฑ Thank you from the bottom of the heart ๐โค๏ธ

Nice class

your teaching style is very simple and effective.Can you please share pdf of it for practice.Thanks

Can you share PowerPoint, Dataset for practice. Thanks in advance

What if I use Minitab rather than R-Studio bcs I already used Minitab

i have diploma engineering in Electronics . can i go in this field ? how to start in data science

0.45 answer of bayes' theorem question

When starting to learn Data Science, which programming language should I start with before embarking on Data Science?

plz provide ppt of the lecture .

What a great contribution. Thank you Edureka for helping me understand the basics of Data science.

Great job and nice explanation

It is difficult to make notes can you provide us the whole content in pdf or any other format ?

Thanks for detailed explanation.Could you please share the slides used for this presentation

super channel

Thank you Edureka for this wonderful course. From where can we get the datasets used in this course? It would be great if you could share them through a link.

Thank you Edureka for this wonderful course. Can you share the course contents – slides, python code notebooks and dataset

Thank you Edureka for this wonderful course. Can you share the course contents – slides, python code notebooks and datasets ?