Data Preprocessing with NumPy

with Viktor Mehandzhiyski

This course will guide you through one of Python’s most notable packages – NumPy. We’ll explain why it’s so popular and discuss the numerous applications of its crown jewel – the ndarray class.

8 hours 68 lessons
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68 High Quality Lessons
6 Practical Tasks
8 Hours of Content
Certificate of Achievement

Course Overview

This course is designed to show you how to work with one of Python’s fundamental packages – NumPy. You will learn what a “package” is and see how to install, upgrade and import it. By the time you finish the course, you’ll be comfortable with NumPy’ ndarray class, how to slice and reduce the dimensions of its instances, as well as how to quickly refer to the documentation. Furthermore, you’ll be ready to take advantage of NumPy’s various built-in functions and methods, which we’ll use to generate random and non-random data, import and export data to and from Python, find statistical values for a dataset, and clean and preprocess ndarrays.

Topics covered

data analysisData processingProgrammingPython

What You'll Learn

Do you already know how to use Python? Improve your skills by adding another tool to your arsenal – one of the most notable packages, NumPy!

Create arrays with NumPy 
Use basic NumPy syntax 
Generate data with NumPy 
Implement statistical functions 
Substitute missing values in Ndarrays 
Explore ways to clean and preprocess data in NumPy 


Student feedback


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Long module, full of notions and formulas without much points for reflection and context. It would have been useful to add more exercises and practice questions to test knowledge and increase engagement. It might also be a good idea to split it in two if adding new content makes it overly long. Also, a number of incorrect formulas/solutions in the practice exams, which although flagged a long while ago have not been fixed, or even replied to. It looks like this course has been released and then not looked after anymore, which is not a good impression, especially compared to much better modules in 365. The practice case study at the end is useful.
This last section with the practical example is the cherry of the cake. It helps widen the conceptualization taught in the course, beyond common grounds. People have different way of learning and the more approach you offer, the better. I have had a short journey in Data Science, yet it is evident that practice plays a huge part on it. Such activity takes you from 0 to 100 and greatly increases your confidence in the subject. Food for thought: I don't know how plausible it is, but if the same could be applied for the career track courses, where we could have all subjects wrapped up into a big work, that would be exceptional.
Overall the course is good for beginners. Problems could be of different types. Random generator section has achieved the prize of worst section of the entire course. I barely could understand the section. The instructor just uses poisson, logistics etc. distributions and said we should have statistical knowledge for this. But he could just explain the trials, events, occurrences etc. to us rather than just showing some code.
Many of the questions asked in Q&A sections are not having a reply from the instructor for months Also, some of the videos have outdated information like that in section 5 [Generating Data with NumPy]
Organized very well and the templates are incredibly helpful to be engaged and follow along with the videos. I would love to see this done for the Pandas course & all the others I have seen.
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Viktor Mehandzhiyski

“A large portion of a data analyst’s work is dedicated to preprocessing datasets. Unquestionably, this involves tons of mathematical and statistical techniques that NumPy is renowned for. NumPy can be described as a computationally stable state-of-the-art Python instrument that provides flexibility and can take your analysis to the next level.”

Viktor Mehandzhiyski

Content Creator at 365 Data Science

Data Preprocessing with NumPy

with Viktor Mehandzhiyski

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