watching now
3 Students
44 Lessons
Beginner

#### What Will You Learn?

• How to get into data science and apply your skills in real-life business cases
• How the mathematics and statistics behind machine learning work
• How to pre-process data and carry out cluster and factor analysis
• How to improve machine learning algorithms and unfold the power of deep neural networks

#### Curriculum

34h 9m
07:01
09:25
##### Section 3: Practical Scenario
36:59
Practical Scenario
36:59
##### Section 4: Data Science Roles
12:50
Data Science Roles
12:50
##### Section 5: An Insight on Data Science
21:18
Understanding Data Science
21:18
##### Section 6: Terminologies and Statistical Methods in Data Science
09:01
Terminologies and Statistical Methods in Data Science
09:01
30:21
Random Variables
30:21
##### Section 8: Descriptive Statistics
10:31
Descriptive Statistics
10:31
##### Section 9: Understanding Percentile
50:52
Understanding Percentile
50:52
59:35
Probability
59:35
##### Section 11: Probability: Continued
1:00:21
Probability: Continued
1:00:21
##### Section 12: Descriptive Statistics: Part 1
1:01:54
Descriptive Statistics: Part 1
1:01:54
##### Section 14: Degrees of freedom and mathematical operations
2:12:44
Descriptive stats continued
57:37
Learn the degrees of freedom, mathematical operations
1:15:07
1:11:10
Random Variables
1:11:10
##### Section 16: Random variables contd
1:05:44
Random Variables Contd
1:05:44
##### Section 17: Properties of E(x)
1:09:01
Properties of E(x)
1:09:01
##### Section 18: Data Visualization
1:11:36
Data Visualization
1:11:36
##### Section 19: Histogram and Boxplot
1:06:35
Histogram and Boxplot
1:06:35
##### Section 20: Boxplot Contd and Scatter Plot
1:13:08
Boxplot examples and Scatter Plot
1:13:08
##### Section 21: Covariance and Correlation
1:02:08
Covariance and Correlation
1:02:08
##### Section 22: R Programming
7:32:24
Installation of R
R Programming Part 1
02:48
R Programming Part 2
11:40
R Programming Part 3
06:12
R Programming Part 4
09:27
R Programming Part 5
24:01
R Programming Part 6
54:17
R Programming Part 7
59:29
R Programming Part 8
56:47
R Programming Part 9
57:57
R Programming Part 10
59:23
R Programming Part 11
56:05
R Programming Part 12
54:18
##### Section 23: Data Science Refresher
57:32
Data Science Refresher
57:32
58:07
Day 28
58:07
56:02
Day 29
56:02
1:02:27
Day 30
1:02:27
58:06
Day 31
58:06
1:04:34
Day 32
1:04:34
1:04:45
Day 33
1:04:45
55:00
Day 34
55:00
1:03:10
Day 35
1:03:10
1:15:06
Day 36
1:15:06

#### Requirements

• An interest in becoming a data scientist

#### Sai Acuity

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