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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
Section 2: Curriculum of Data Science
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
Section 7: Confusion Matrix, Random Variables
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
Section 10: Probability : An Introduction
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
Section 15: Random Variables
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
Section 24: Day 28
58:07
Day 28
58:07
Section 25: Day 29
56:02
Day 29
56:02
Section 26: Day 30
1:02:27
Day 30
1:02:27
Section 27: Day 31
58:06
Day 31
58:06
Section 28: Day 32
1:04:34
Day 32
1:04:34
Section 29: Day 33
1:04:45
Day 33
1:04:45
Section 30: Day 34
55:00
Day 34
55:00
Section 31: Day 35
1:03:10
Day 35
1:03:10
Section 32: Day 36
1:15:06
Day 36
1:15:06

Description

Requirements

  • An interest in becoming a data scientist
  • A wish to learn more about the field

About the Instructor

100% of students rated this instructor as excellent!
Reviews 0
Students 169
Courses 3

I care for knowledge transfer because knowledge is power, it helps get food on the table. Hence I work with my partners and friends who work in the IT Industry and come up with learning programs and courses which can help quickly build competency levels which can help a student or a professional get a job!
Hence I focus more on tool-based learning which helps to get hands-on knowledge thus accelerating the chances to get placed in a corporate!
Presently my focus is on Data Science and Cyber Security Domains! A warm welcome to those who are interested to build their practical knowledge using our courses on Bitdegree!

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