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Python Data Science with NumPy: Over 100 Exercises

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Python Data Science with NumPy: Over 100 Exercises

Level up Your Data Science Skills in Python - Unleash the Power of Numerical Computing and Analysis!

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64117 enrolled on this course

Last updated 11/2021

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₹ 2499.0

₹ 3999.0

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Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

Description

The course "Python Data Science with NumPy: Over 100 Exercises" is a practical, exercise-oriented program aimed at individuals who want to strengthen their Python data science skills, with a particular focus on the powerful NumPy library. It caters to learners eager to dive deep into the functionalities that NumPy offers for handling numerical data efficiently.
Each section of the course contains a set of carefully curated exercises designed to consolidate the learners' understanding of each concept. Participants will get to tackle real-life problems that simulate challenges faced by data scientists in their everyday roles. Each exercise is followed by a detailed solution, helping students understand not just the 'how' but also the 'why' of each solution.
The "Python Data Science with NumPy: Over 100 Exercises" course is suited for individuals at various stages of their data science journey - from beginners just starting out, to more experienced data scientists looking to refresh their knowledge or gain more practice working with NumPy. The primary prerequisite is a basic understanding of Python programming.
NumPy - Unleash the Power of Numerical Python!
NumPy, short for Numerical Python, is a fundamental library for scientific computing in Python. It provides support for arrays, matrices, and a host of mathematical functions to operate on these data structures. This course is structured into various sections, each targeting a specific feature of the NumPy library, including array creation, indexing, slicing, and manipulation, along with mathematical and statistical functions.
Who this course is for:
data analysts or data scientists who want to enhance their skills in data manipulation and numerical computing using the NumPy library in Python
students or individuals with a background in data analysis, statistics, or related fields who want to gain practical experience in using NumPy for data manipulation and analysis
programmers or software developers who are interested in data science and want to learn how to use the NumPy library to efficiently handle large datasets and perform numerical computations
professionals working with scientific or numeric data who want to leverage the power of NumPy to perform advanced calculations, data transformations, and statistical analysis
self-learners who are passionate about data science and want to develop proficiency in using NumPy for data manipulation, analysis, and numerical computations
researchers or scientists in fields such as physics, biology, or engineering who want to apply numerical methods and data analysis techniques using NumPy in Python

What you'll learn

solve over 100 exercises in NumPy

deal with real programming problems in data science

work with documentation and Stack Overflow

guaranteed instructor support

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Tips

1.1-A few words from the author

1.2-Configuration

1.3-Tip

2-Starter

2.1-Exercise 0

2.2-Solution 0

2.3-NumPy - Intro

3-Exercises 1-10

3.1-Exercise 1

3.2-Solution 1

3.3-Exercise 2

3.4-Solution 2

3.5-Exercise 3

3.6-Solution 3

3.7-Exercise 4

3.8-Solution 4

3.9-Exercise 5

3.10-Solution 5

3.11-Exercise 6

3.12-Solution 6

3.13-Exercise 7

3.14-Solution 7

3.15-Exercise 8

3.16-Solution 8

3.17-Exercise 9

3.18-Solution 9

3.19-Exercise 10

3.20-Solution 10

4-Exercises 11-20

4.1-Exercise 11

4.2-Solution 11

4.3-Exercise 12

4.4-Solution 12

4.5-Exercise 13

4.6-Solution 13

4.7-Exercise 14

4.8-Solution 14

4.9-Exercise 15

4.10-Solution 15

4.11-Exercise 16

4.12-Solution 16

4.13-Exercise 17

4.14-Solution 17

4.15-Exercise 18

4.16-Solution 18

4.17-Exercise 19

4.18-Solution 19

4.19-Exercise 20

4.20-Solution 20

5-Exercises 21-30

5.1-Exercise 21

5.2-Solution 21

5.3-Exercise 22

5.4-Solution 22

5.5-Exercise 23

5.6-Solution 23

5.7-Exercise 24

5.8-Solution 24

5.9-Exercise 25

5.10-Solution 25

5.11-Exercise 26

5.12-Solution 26

5.13-Exercise 27

5.14-Solution 27

5.15-Exercise 28

5.16-Solution 28

5.17-Exercise 29

5.18-Solution 29

5.19-Exercise 30

5.20-Solution 30

6-Exercises 31-40

6.1-Exercise 31

6.2-Solution 31

6.3-Exercise 32

6.4-Solution 32

6.5-Exercise 33

6.6-Solution 33

6.7-Exercise 34

6.8-Solution 34

6.9-Exercise 35

6.10-Solution 35

6.11-Exercise 36

6.12-Solution 36

6.13-Exercise 37

6.14-Solution 37

6.15-Exercise 38

6.16-Solution 38

6.17-Exercise 39

6.18-Solution 39

6.19-Exercise 40

6.20-Solution 40

7-Exercises 41-50

7.1-Exercise 41

7.2-Solution 41

7.3-Exercise 42

7.4-Solution 42

7.5-Exercise 43

7.6-Solution 43

7.7-Exercise 44

7.8-Solution 44

7.9-Exercise 45

7.10-Solution 45

7.11-Exercise 46

7.12-Solution 46

7.13-Exercise 47

7.14-Solution 47

7.15-Exercise 48

7.16-Solution 48

7.17-Exercise 49

7.18-Solution 49

7.19-Exercise 50

7.20-Solution 50

8-Exercises 51-60

8.1-Exercise 51

8.2-Solution 51

8.3-Exercise 52

8.4-Solution 52

8.5-Exercise 53

8.6-Solution 53

8.7-Exercise 54

8.8-Solution 54

8.9-Exercise 55

8.10-Solution 55

8.11-Exercise 56

8.12-Solution 56

8.13-Exercise 57

8.14-Solution 57

8.15-Exercise 58

8.16-Solution 58

8.17-Exercise 59

8.18-Solution 59

8.19-Exercise 60

8.20-Solution 60

9-Exercises 61-70

9.1-Exercise 61

9.2-Solution 61

9.3-Exercise 62

9.4-Solution 62

9.5-Exercise 63

9.6-Solution 63

9.7-Exercise 64

9.8-Solution 64

9.9-Exercise 65

9.10-Solution 65

9.11-Exercise 66

9.12-Solution 66

9.13-Exercise 67

9.14-Solution 67

9.15-Exercise 68

9.16-Solution 68

9.17-Exercise 69

9.18-Solution 69

9.19-Exercise 70

9.20-Solution 70

10-Exercises 71-80

10.1-Exercise 71

10.2-Solution 71

10.3-Exercise 72

10.4-Solution 72

10.5-Exercise 73

10.6-Solution 73

10.7-Exercise 74

10.8-Solution 74

10.9-Exercise 75

10.10-Solution 75

10.11-Exercise 76

10.12-Solution 76

10.13-Exercise 77

10.14-Solution 77

10.15-Exercise 78

10.16-Solution 78

10.17-Exercise 79

10.18-Solution 79

10.19-Exercise 80

10.20-Solution 80

11-Exercises 81-90

11.1-Exercise 81

11.2-Solution 81

11.3-Exercise 82

11.4-Solution 82

11.5-Exercise 83

11.6-Solution 83

11.7-Exercise 84

11.8-Solution 84

11.9-Exercise 85

11.10-Solution 85

11.11-Exercise 86

11.12-Solution 86

11.13-Exercise 87

11.14-Solution 87

11.15-Exercise 88

11.16-Solution 88

11.17-Exercise 89

11.18-Solution 89

11.19-Exercise 90

11.20-Solution 90

12-Exercises 91-100

12.1-Exercise 91

12.2-Solution 91

12.3-Exercise 92

12.4-Solution 92

12.5-Exercise 93

12.6-Solution 93

12.7-Exercise 94

12.8-Solution 94

12.9-Exercise 95

12.10-Solution 95

12.11-Exercise 96

12.12-Solution 96

12.13-Exercise 97

12.14-Solution 97

12.15-Exercise 98

12.16-Solution 98

12.17-Exercise 99

12.18-Solution 99

12.19-Exercise 100

12.20-Solution 100

13-Exercises 100+

13.1-Exercise 101

13.2-Solution 101

13.3-Exercise 102

13.4-Solution 102

14-Configuration (optional)

14.1-Info

14.2-Requirements

14.3-Google Colab + Google Drive

14.4-Google Colab + GitHub

14.5-Google Colab - Intro

14.6-Anaconda installation - Windows 10

14.7-Introduction to Spyder

14.8-Anaconda installation - Linux

15-Bonus

15.1-Bonus

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