[Download] The Data Science Course 2019: Complete Data Science Bootcamp

The Data Science Course 2019: Complete Data Science Bootcamp download free udemy courses

The Data Science Course 2019: Complete Data Science Bootcamp download free udemy courses

4.5 (39,113 ratings)
179,040 students enrolled
Created by 365 Careers, 365 Careers Team
Last updated 12/2019
English

The Data Science Course 2019 download udemy videos free

This course includes
28.5 hours on-demand video
89 articles
147 downloadable resources
Full lifetime access
Access on mobile and TV
Certificate of Completion

 

Get any paid udemy course you want for free Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning download udemy paid courses for free

Impress interviewers by showing an understanding of the data science field
Learn how to pre-process data
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
Start coding in Python and learn how to use it for statistical analysis
Perform linear and logistic regressions in Python
Carry out cluster and factor analysis
Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
Apply your skills to real-life business cases
Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
Unfold the power of deep neural networks

 

Complete Data Science Bootcamp udemy courses for free download

Part 1: Introduction
3 lectures
19:19

The Field of Data Science – The Various Data Science Disciplines
5 lectures
31:11

The Field of Data Science – Connecting the Data Science Disciplines
1 lecture
07:19

The Field of Data Science – The Benefits of Each Discipline
1 lecture
04:44

The Field of Data Science – Popular Data Science Techniques
11 lectures
53:34

The Field of Data Science – Popular Data Science Tools
1 lecture
05:51

The Field of Data Science – Careers in Data Science
1 lecture
03:29

The Field of Data Science – Debunking Common Misconceptions
1 lecture
04:10

Part 2: Probability
4 lectures
23:04

Probability – Combinatorics
11 lectures
42:56

Probability – Bayesian Inference
12 lectures
54:38

Probability – Distributions
15 lectures
01:17:12

Probability – Probability in Other Fields
3 lectures
18:51

Part 3: Statistics
1 lecture
04:02

Statistics – Descriptive Statistics
22 lectures
48:11

Statistics – Practical Example: Descriptive Statistics
2 lectures
16:18

Statistics – Inferential Statistics Fundamentals
8 lectures
21:53

Statistics – Inferential Statistics: Confidence Intervals
15 lectures
44:25

Statistics – Practical Example: Inferential Statistics
2 lectures
10:08

Statistics – Hypothesis Testing
15 lectures
48:24

Statistics – Practical Example: Hypothesis Testing
2 lectures
07:19

Part 4: Introduction to Python
7 lectures
32:49

Python – Variables and Data Types
3 lectures
19:17

Python – Basic Python Syntax
7 lectures
15:13

Python – Other Python Operators
2 lectures
07:45

Python – Conditional Statements
4 lectures
27:44

Python – Python Functions
7 lectures
29:26

Python – Sequences
5 lectures
34:49

Python – Iterations
6 lectures
32:30

Python – Advanced Python Tools
4 lectures
12:56

Part 5: Advanced Statistical Methods in Python
1 lecture
01:27

Advanced Statistical Methods – Linear regression with StatsModels
11 lectures
40:55

Advanced Statistical Methods – Multiple Linear Regression with StatsModels
13 lectures
42:18

Advanced Statistical Methods – Linear Regression with sklearn
19 lectures
54:29

Advanced Statistical Methods – Practical Example: Linear Regression
9 lectures
38:01

Advanced Statistical Methods – Logistic Regression
16 lectures
40:49

Advanced Statistical Methods – Cluster Analysis
4 lectures
14:03

Advanced Statistical Methods – K-Means Clustering
15 lectures
49:01

Advanced Statistical Methods – Other Types of Clustering
3 lectures
13:34

Part 6: Mathematics
11 lectures
51:01

Part 7: Deep Learning
1 lecture
03:07

Deep Learning – Introduction to Neural Networks
12 lectures
42:38

Deep Learning – How to Build a Neural Network from Scratch with NumPy
5 lectures
20:35

Deep Learning – TensorFlow 2.0: Introduction
9 lectures
28:10

Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
9 lectures
25:44

Deep Learning – Overfitting
6 lectures
19:36

Deep Learning – Initialization
3 lectures
08:04

Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
7 lectures
20:40

Deep Learning – Preprocessing
5 lectures
14:33

Deep Learning – Classifying on the MNIST Dataset
12 lectures
36:34

Deep Learning – Business Case Example
12 lectures
39:19

Deep Learning – Conclusion
6 lectures
17:30

Appendix: Deep Learning – TensorFlow 1: Introduction
10 lectures
28:56

Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset
11 lectures
39:31

Appendix: Deep Learning – TensorFlow 1: Business Case
12 lectures
50:58

Software Integration
5 lectures
29:38

Case Study – What’s Next in the Course?
3 lectures
10:14

Case Study – Preprocessing the ‘Absenteeism_data’
33 lectures
01:29:43

Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
16 lectures
01:07:05

Case Study – Loading the ‘absenteeism_module’
4 lectures
11:00

Case Study – Analyzing the Predicted Outputs in Tableau
6 lectures
23:29

The Data Science Course 2019: Complete Data Science Bootcamp free tutorials udemy. Mathematics, Statistics, Python download udemy paid courses for free 2018

Amazing course that teaches from statistics to deepl learning. Very thorough. The instructors explain in a very easy way to understand even for the people that have no previous knowledge on the subjects. I loved it.

It took a while to complete this course, but I think it was worth the effort. Very well framed and delivered course. Thanks a lot.

The Mathematics, Statistics, and Advanced Statistics were fantastic and absolutely awesome! I would recommend those sections to anyone who likes to thoroughly learn Statistics from scratch! The Python part could still be more comprehensive. The ML and DL sections were also good. Thanks so much for this course the 365 Careers Team!

Early on – very informative and styled like a subject that I want to learn and apply. I am now about more than half-way through. Wow! to explain and to be understand is a gift – thank guys. I am actually excited about what I am learning. Real-world opportunities to make sense of data through traditional and big data look promising and my goal.

Generally good as an introduction. However I would need to pass through the course at least once more, if not twice.

The course overall has been a great match for me. There is a suitable balance between various levels of difficulty in the material. The only criticism is that perhaps the duplicate material between TensorFlow 1 and 2 lectures could have been omitted. Other than that minor point, the overall course was great as an introduction to data science. Much appreciated!

Download Udemy Course File 15.05 GB ↓

Download File

LEAVE A REPLY

Please enter your comment!
Please enter your name here