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3 Books to read this Summer to boost your Coding Skills and Career

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The following 3 books are meant for both newcomers and seniors in the fields of Software Engineering and Data Science.
They cover 3 different aspects of Coding:

I have personally read all of them and they have substantially helped me to be a better programmer and have a vision for my career.

1. The Coding Career Handbook

The book focuses on your Coding Career. It includes 40 chapters spanning over 450 pages.
You will read about:

2. Cracking the Coding Interview

Ace your next coding interview. Learn how to uncover the hints and hidden details in a question, discover how to break down a problem into manageable chunks, develop techniques to unstick yourself when stuck, learn core computer science concepts through 189 real interview questions.
You will 650+ pages read about:

3. Clean Code: A Handbook of Agile Software Craftsmanship

A book to make you a better programmer. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn’t have to be that way. Write clean code, be efficient, love your code.
You will read about:

Not sure what to read next? Here is one pick:

Keep in touch

➥Follow me on Medium for more content like this.
➥Let’s connect on LinkedIn.
➥Check my GitHub.

Data Scientist at Mathisys Technologies Hellas | Ph.D. Candidate at the Democritus University of Thrace. https://www.linkedin.com/in/dimitrios-effrosynidis/

Data Scientist at Mathisys Technologies Hellas | Ph.D. Candidate at the Democritus University of Thrace. https://www.linkedin.com/in/dimitrios-effrosynidis/

Image by Faye Cornish on Unsplash

Most of the content of this article is from my recent paper entitled:
“An Evaluation of Feature Selection Methods for Environmental Data”, available here for anyone interested.

The 2 approaches for Dimensionality Reduction

There are two ways to reduce the number of features, otherwise known as dimensionality reduction.

The first way is called feature extraction and it aims to transform the features and create entirely new ones based on combinations of the raw/given ones.
The most popular approaches are the Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Multidimensional Scaling. However, the new feature space can hardly provide us with useful information about the original features.
The…


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Achieve ×66 speedup read time, ×25 write time, and ×0.39 filesize on your daily I/O operations.

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Reading and writing files using Pandas and NumPy is an everyday task for Data Scientists and Engineers.

Let’s compare the most common functions that these libraries provide to write/read tabular data.

We can make our code much faster in these I/O operations, save time, and make our boss and ourselves happy.

We can also save serious amounts of disk space by choosing the appropriate save function.

First, let's create a DataFrame of 10,000,000 rows and 2 columns.

to_csv() / pd.read_csv()

The most common approach to save a Pandas DataFrame.


Keep up with the latest trends and stay at the top of your field.

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Podcasts are on the rise. They can be an alternative way for Data Scientists to learn and keep up with the latest news on the field. Immerse yourself in the industry and stay at the top of your field.

A podcast is a passive form of learning, so you can do other things at the same time. You can listen to podcasts when you take a walk, when exercising, when cleaning the house, or when relaxing.

I will recommend 5 active podcasts that post new episodes every week with durations ranging from 20 minutes to about an hour.

I recommend…


google

I rate all the movies I watch on IMDb and the website allows you to download a nice .csv with all your ratings. This .csv contains basic information about the movies. In order to perform topic modeling, I need the plots and/or summaries of the movies. I will grab this information from Wikipedia and use it to enrich the IMDb dataset. Then I will perform LDA for topic modeling on the plots+summaries of the movies to find 6 topics.

I will keep the article clean of code. The code is available here.

The purpose of this article is to:


Image by Dimitris Effrosynidis

I created a simple Web Application with Spotify API, Python Dash, and Flask. Spotify users can access the app giving permission to the app to use the data. A lot of cool statistics are displayed!

You can visit the app here

You need a Spotify Account to access it. Allow up to 20 seconds to load.

Code is available on GitHub

I am a Data Scientist, with an academic background in Electrical and Computer Engineering. After completing university in 2017, I immediately started a Ph.D. Through the Ph.D. journey, I discovered Data Science. Machine Learning and Data Science Books, Youtube Videos, Online Courses, Podcasts, Kaggle, all combined made me a self-taught…