Best 5 Free Courses for Data Science

Best 5 Free Courses for Data Science

1) Simplilearn

Course duration (video) – 10 hours

Link: https://www.youtube.com/watch?v=7WRlYJFG7YI

Content:

0. Introduction (0:00)

1. Data Science basics (01:28)

2. What is Data Science (05:51)

3. Need for Data Science (06:38)

4. Business intelligence vs Data Science (17:30)

5. Prerequisites for Data Science (22:31)

6. What does a Data Scientist do? (30:23)

7. Demand for Data Scientist (53:03)

8. Linear regression (2:30:10)

9. Decision trees (2:53:39)

10. Logistic regression in R (3:09:12)

11. What is a decision tree? (3:27:04)

12. What is clustering? (4:35:40)

13. Divisive clustering (4:51:14)

14. Support vector machine (5:17:21)

15. K-means clustering 96:44:13)

16. Time series analysis (7:33:05)

17. How to Become a Data Scientist (8:26:54)

18. Job roles in Data Science (8:30:59)

19. Simplilearn certifications in Data Science (8:33:50)

20. Who is a Data Science engineer? (8:34:34)

21. Data Science engineer resume (9:00:04)

22. Data Science interview questions and answers (9:04:42)

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2) Edureka

Course duration (video) – 10 hours

Link: https://www.youtube.com/watch?v=-ETQ97mXXF0

Content:

00:00 Data Science Full Course 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 Probability

1:33:34 Marginal Probability

1:34:06 Joint Probability

1:34:58 Conditional Probability

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

5:24:30 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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3) Intellipaat

Course duration (video) – 11 hours

Link: https://www.youtube.com/watch?v=xut5xpewcrE

Content:

00:00:00 – Introduction to Data Science Course

00:02:35 – What is Data Science?

00:06:39 – Introduction to Data Science and Machine Learning

00:13:43 – Types of Data

00:24:08 – Components of Data Science Projects

00:36:46 – Data Science vs ML vs AI

00:48:57 – Heart Disease Prediction

01:14:39 – Types Of Machine Learning

01:40:26 – Linear Regression

02:46:13 – Hypothesis Functions

03:11:45 – Gradient Descent

04:43:49 – Advantages of Liner Regression

05:10:42 – Regression Model Evaluation Metrics

05:35:54 – Linear Regression for Classification

07:17:17 – Logistic Regression

08:14:09 – Decision Tree and Random Forest

10:20:50 – Project

11:01:43 – Data Science Interview Questions

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4) WsCube Tech

Course duration (video) – 29 hours

Link: https://www.youtube.com/watch?v=VaSjiJMrq24&t=203s

Content:

00:00:00 – 02:15:26: Python Basics

02:40:22 – 06:36:18: Python – Loops and Lists

06:47:43 – 07:36:16: Tuples, Dictionaries, and Sets in Python

08:16:10 – 09:14:20: Python Functions and Modules

09:15:59 – 15:57:26: Numpy and Data Manipulation

12:01:26 – 13:45:36: Pandas for Data Analysis

13:43:51 – 15:57:26: Matplotlib for Data Visualization

18:38:09 – 21:02:52: MySQL for Data Analytics

21:14:31 – 24:45:52: Excel for Data Analytics

25:38:35 – 29:09:56: Power BI Essentials

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5) Great Learning

Course duration (video) – 11 hours

Link: https://www.youtube.com/watch?v=u2zsY-2uZiE

Content:

• Introduction – 00:00:00

• Statistics vs Machine Learning – 00:02:15

• Types of Statistics – 00:08:55

• Types of Data – 01:50:35

• Correlation – 02:45:50

• Covariance – 02:52:23

• Basics of Python – 04:24:36

• Python Data Structures – 04:43:58

• Flow Control Statements in Python – 04:55:58

• Numpy – 05:32:48

• Pandas – 05:51:30

• Matplolib – 06:14:28

• Linear Regression – 06:38:14

• Logistic Regression – 09:54:34

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