# Data Science Full Course – Learn Data Science in 10 Hours | Data Science For Beginners | Edureka

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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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1. 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 "๐๐๐๐๐๐๐๐๐")
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2. 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

3. 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.

4. 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

5. 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.