Developing a skill is much easier if we are able to streamline the process of practicing it. For me, improving my data science skills involves weekly and sometimes daily practice of the different aspects of doing data science work: data collection, data cleaning and preprocessing, data visualization, modelling and more.
In this post, I will share my basic template for practicing data science with a practical example.
One good way to simplify the cycle of practicing your data science skills is to clarify what are the steps that take you from data to insight. …
When you first get to your laptop/desktop in the morning you probably open quite a few applications. Doing this every day can get tiresome, so I wrote a script to automate the process of starting my day on my desktop, and in this tutorial I will show you how it works.
This script streamlines my daily routine right after booting my computer in the morning, handling a lot of the the basic manual tasks I have to get through before I can actually start working.
This routine involves processes like opening specific browser tabs, showing routine reminders, starting tracking scripts…
I am periodically looking to improve my pipeline of knowledge management, and, upon testing multiple systems for note taking, I realized the underappreciated value of tags for notes. So, in this post I will be sharing my tagging system to optimize your notes.
Tags are like micro bridges that allow you to give your overall knowledge storage a language to communicate high level organizational aspects of your notes.
For me, tags can be…
Writing fast code is a trademark of our perception of what constitutes an efficient programmer. Even though the bottleneck for good programming performance is most definitely not how fast you type, it can have a very positive effect in the way you think and solve problems.
Today I want to share an approach to optimizing your coding productivity by using keyboard shortcuts with AutoHotkey to write a variety of repetitive python snippets of code.
For those who program in Python, we get used to a certain degree of readability. We get “spoiled” by the ease of dynamic typing and all the cool aesthetic features that Python offers.
That is all wonderful until we want to learn a more low-level language like C or C++, which do not offer the same kind of readability levels that Python does.
This can be a pain to get through, so I decided to write this post as way to help you get through the super basics of C++ in a quick and simple manner.
In this article I…
In my daily routine I have to deal with a lot of the same situations from loading csv files to visualizing data. So, to help streamline my process I created the habit of storing snippets of code that are helpful in different situations from loading csv files to visualizing data.
In this post I will share 15 snippets of code to help with different aspects of your data analysis pipeline
import pandas as pd
csv_files = glob.glob("path/to/folder/with/csvs/*.csv")
dfs = [pd.read_csv(filename) for filename in csv_files]
import pandas as pd
df = pd.read_csv("path/to/csv/file.csv")
df["Item_Identifier"].unique()array(['FDA15', 'DRC01', 'FDN15', ..., …
Typing is a part of our modern routine, therefore, learning how to type optimally can have a huge benefit for people that, like me, work mostly on their computers.
In this post I will share my daily typing practice routine with the scripts and packages I use to log and visualize my performance.
When access to the GPT-3 API was released to researchers and engineers (upon request) I immediately requested it so I could see what kind of interesting tools one could write and what kind of interesting research questions could be asked.
Upon trying the API and the awesome tools that come with it, I realized that one of my favorite applications of GPT-3 was for paper summarization, so I decided to test it out.
Today I will show you how to build a simple python tool to summarize papers directly from their arxiv address.
I love Python snippets. I just love sitting down in from of an ipython shell and write simple and useful code. So, today I want to share some of the many Python snippets I wrote, gathered or found throughout my pythonic explorations.
from itertools import combinationslista = [1,2,3,4,5]print(list(combinations(lista,2)))
[(1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5), (4, 5)]
import win32apidef printInfo():
device = win32api.EnumDisplayDevices()
settings = win32api.EnumDisplaySettings(device.DeviceName, -1)
for varName in ['Color', 'BitsPerPel', 'DisplayFrequency']:
print("%s: %s"%(varName, getattr(settings, varName)))
import numpy as np from…
The objective of this quick tutorial is to implement a simple variation of a dynamic programming solution to the classic combinatorial optimization problem: “knapsack” using the swift programming language.
Senior ML Engineer at K1 Digital. AI | Computer Vision| Data Science| Productivity | Learning