Gain insights into using Python for scientific and engineering applications. Delve into real-world scenarios, learning NumPy, Matplotlib, audio processing, and more through practical, hands-on projects.

Python for Scientists and Engineers_468 x 60 copy 2.png

jupter.tar.gz

notebook

obesity_plot_gender

print_gender_pandas

create_sine_wave

notebook_exercise

range

print_gender_pandas_remove_NaN

print_gender_year

print_data_age

print_data_age_with_year

print_data_age_remove_NaN

obesity_plot_age_simple

obesity_plot_age_clear

print_data_age_under_16

print_data_age_25_34

obesity_plot_children_vs_adults

print_movie_top_rated

print_movies_oldie_teen

print_movies_by_gender

print_movies_for_women

print_movies_by_gender_loc

print_movies_diff_gender

movies_diff_gender_plot

print_u_data

print_movies_oldie_index

print_movies_oldies

print_movies_teens

display_image_nouse

grey_image

blur_image

edge_detection_low

edge_detection_high

plot_sin_wave

create_wave_file

display_again

print_frequencies

count_objects

detect_faces

detect_faces_little_mix

count_exact_lines

webcam_detection

single_thread

find_correlation

webcam_face_detection

motion_detection

motion_detection_live

In this course, you will learn to use Python for real-world scientific/engineering applications. For each topic, there will be a real case scenario where you will build a quick solution in Python to solve the problem.

More specifically, you will cover topics such as creating a word counter, NumPy and Matplotlib, audio processing, and a lot more. Throughout each chapter, you will get hands-on experience in building solutions for complex problems that engineers and scientists face every day.

The Practical Guide to Python for Scientists and Engineers

In the `np.corrcoef()` function, what does a value of 1 mean?


In the <code>np.corrcoef()</code> function, what does a value of 1 mean?

Consider the code below:

```
import numpy as np

a = [1, 2, 3, 4, 5]
b = [1, 3, 9, 20, 22]
print(np.X(a,b)[1][0])
```
Which of the following functions will replace `X` if we want to find the correlation between `a` and `b`?

Consider the code below:
<pre><code>import numpy as np

a = [1, 2, 3, 4, 5]
b = [1, 3, 9, 20, 22]
print(np.X(a,b)[1][0])
</code></pre>
Which of the following functions will replace <code>X</code> if we want to find the correlation between <code>a</code> and <code>b</code>?



Given  array `a` and array `b`:
$$
	a\begin{bmatrix}  
	1 & 0.92
	\end{bmatrix}
	\quad
	$$
$$
	b\begin{bmatrix}  
	0.87 & -1
	\end{bmatrix}
	\quad
	$$


What value of correlation will we get if we run the line of code mentioned below?

```
print(np.corrcoef(a,b)[1][0])
```

Given array <code>a</code> and array <code>b</code>:
<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>a</mi><mrow><mo fence="true">[</mo><mtable rowspacing="0.16em" columnalign="center center" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>1</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>0.92</mn></mstyle></mtd></mtr></mtable><mo fence="true">]</mo></mrow><mspace width="1em"/></mrow><annotation encoding="application/x-tex">	a\begin{bmatrix} 
	1 &amp; 0.92
	\end{bmatrix}
	\quad
</annotation></semantics></math>a[1​0.92​]
<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>b</mi><mrow><mo fence="true">[</mo><mtable rowspacing="0.16em" columnalign="center center" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>0.87</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mo>−</mo><mn>1</mn></mrow></mstyle></mtd></mtr></mtable><mo fence="true">]</mo></mrow><mspace width="1em"/></mrow><annotation encoding="application/x-tex">	b\begin{bmatrix} 
	0.87 &amp; -1
	\end{bmatrix}
	\quad
</annotation></semantics></math>b[0.87​−1​]
What value of correlation will we get if we run the line of code mentioned below?
<pre><code>print(np.corrcoef(a,b)[1][0])
</code></pre>

There is no correlation between `a` and `b`.

There is a strong positive correlation between `a` and `b`.

There is a weak negative correlation between `a` and `b`.

There is a strong negative correlation between `a` and `b`.

Consider the code below:

```
import numpy as np

a = [1, 2, 3, 4]
c = [2000 , 1500, 1000, 500]
print(np.corrcoef(a,c)[1][0])
```

After running this code, we’ll get an output of `-1.0`

Which of the following options is correct given the above information?


Consider the code below:
<pre><code>import numpy as np

a = [1, 2, 3, 4]
c = [2000 , 1500, 1000, 500]
print(np.corrcoef(a,c)[1][0])
</code></pre>
After running this code, we’ll get an output of <code>-1.0</code>
Which of the following options is correct given the above information?

Which of the following functions will be able to read Python data structures (such as lists and dictionaries) in their original forms and not as a text file?

Which of the following functions will be able to read Python data structures (such as lists and dictionaries) in their original forms and not as a text file?

Which of the following functions allows us to generate and run Python code in real-time?

Which of the following functions allows us to generate and run Python code in real-time?

Introduction

Create a Word Counter in Python

An Introduction to NumPy and Matplotlib

Python pandas

Audio Processing

Image and Video Processing

MultiThreading vs. Multiprocessing in Python

Machine Learning with an Amazon-like Recommendation Engine

Conclusion

Quiz: Build Amazon Recommendation Engine