Opinion

Kaggle datasets for practicing data science and machine learning algorithms

Photo by Austin Distel on Unsplash [1].

Table of Contents

  1. Introduction
  2. TOP 50 Cryptocurrencies Historical Prices
  3. Top 10 Cryptocurrencies Historical Dataset
  4. Historical data on the trading of cryptocurrencies
  5. Summary
  6. References

Introduction

The purpose of this article is to discuss information around three datasets regarding cryptocurrency, so that you can practice data science with a more relevant topic and its data. Of…

Opinion

To help you to decide on your career

Photo by Nick Fewings on Unsplash [1].

Table of Contents

  1. Introduction
  2. Product Stakeholder Collaboration
  3. Preference for Python over SQL
  4. Experimentation Oriented
  5. Summary
  6. References

Introduction

The long story short, is that of course, I prefer to be a data scientist over a data engineer, but why? Perhaps these reasons I will share from my personal experience can relate to what you are…

Opinion

… along with use-case examples for easier stakeholder collaboration

Photo by path digital on Unsplash [1].

Table of Contents

  1. Introduction
  2. Bucketed Unit Ranges
  3. Change in Impact
  4. Summary
  5. References

Introduction

While there are a ton of data science or specific algorithm metrics like MAE and RMSE that are useful to know, there are other metrics that can mean more to stakeholders and your business as a whole. It is often not…

Tutorial for beta version - easily perform forecasting

Photo by Icons8 Team on Unsplash [1].

Table of Contents

  1. Introduction
  2. Setting Up, Comparing, Tuning, and Blending Models
  3. Productionalize Model
  4. Summary
  5. References

Introduction

To be frank, even though it seems simple, time-series algorithms are usually difficult to deal with. PyCaret [2] alleviates a lot of monotonous and confusing work by automating the setup and comparison of models processes of time series…

Opinion

Showcase your skills strategically to draw employers’ attention

Photo by Kote Puerto on Unsplash [1].

Table of Contents

  1. Introduction
  2. LSTM
  3. XGBoost
  4. CatBoost
  5. Summary
  6. References

Introduction

There are several machine learning algorithms that you can either study in academia or employ in the workplace, but some might be more useful than others to practice and apply. It can ultimately depend on a few factors, like the problem you are solving…

Opinion

… what to avoid from my experience as an interviewer

Photo by Randy Laybourne on Unsplash [1].

Table of Contents

  1. Introduction
  2. Pretend Knowing the Answer
  3. Going on Long Tangents
  4. Centering Data Science Around You
  5. Summary
  6. References

Introduction

From my participation in data science interviews, I have experienced a variety of applicants who have exhibited some things that they should have and some that they should not have. For this article, we…

Opinion

for Data Scientists and ML Engineers

Photo by Ludemeula Fernandes on Unsplash [1].

Table of Contents

  1. Introduction
  2. Categorical Features are More Powerful
  3. Integrated Plotting Features
  4. Efficient Processing
  5. Summary
  6. References

Introduction

Out with the old, and in with the new. More specifically, CatBoost [2] may be replacing XGBoost for many data scientists and ML engineers moving forward. Not only is this a great algorithm for data science competitions…

Opinion

A few insights based on my experience as an interviewer

Photo by Christina @ wocintechchat.com on Unsplash [1].

Table of Contents

  1. Introduction
  2. Stakeholder Collaboration
  3. Key Performance Indicators
  4. Culture Fit
  5. Summary
  6. References

Introduction

There are many guides to data science interviews out there on the web, but few stress the importance of interactions with colleagues and the effects of data science on the business, rather than only focusing on machine learning algorithm concepts…

Matt Przybyla

Sr/MS Data Scientist. Top Writer in Artificial Intelligence, Technology, & Education. Towards Data Science. Subscribe: https://datascience2.medium.com/subscribe

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