Why Do 87% of Data Science Projects Never Make It into Production?

The Dataquest Download

Project Walkthrough

3 Real-World Data Projects for Your Portfolio

In our next webinar, in collaboration with our partner Omdena, we will go in-depth with three data science case studies to see how companies leverage data science to make better decisions.  Our panel of experts will showcase the following projects:

• “Building an AI Model to Detect Forest Wood Fire through Sensor Data Analysis”—Rosana de Oliveira Gomes
• “Developing Species Identifications for Mosquitos Using AI”—Srirupa Guha
• “Using NLP and Machine Learning to Build Sustainability Benchmarking System Globally”—Saisreenath Srinivasan Govindarajan

Learning Tips

10 Great Portfolio Projects for Business Analysis (2022)

If you want to get a job as a business analyst, you need a portfolio of relevant projects. Why? It's not only an excellent way to practice your skills with an end-to-end project but also an essential element for your job search. In this article, we’ll share 10 great projects that you can add to your portfolio to help hone your skills and land your next interview. 

Learning Tips

How to Become a Data Scientist (Step-by-Step) in 2022

Data scientists are in extreme demand, and for good reason — data scientists are doing everything from creating self-driving cars to automatically captioning images. In this article, Dataquest Founder and CEO, Vik Paruchuri shares the necessary steps to become a data scientist and fast-track your career. 

What We're Watching

Why Do 87% of Data Science Projects Never Make It into Production?

Using AI, fast companies are outperforming slow companies, regardless of their size. And tiny, no-name companies are actually stealing market share from the giants. But if AI offers a competitive edge, why do only 13% of data science projects (or just one out of every 10) actually make it into production?

Tutorial

Introduction to Data Types in Python

Python implements built-in data types, so programmers need to know their options — and when to use each one. In this article, we’ll go over the most commonly used built-in data types in Python, as well as some of their methods and use cases.

Community Spotlights

Top Community Projects of the Week

@jesmaxavier shared his personal project on E-mail Spam Detection that stands out for its creative title and subheadings, cool additional libraries, easy-to-navigate structure, informative explanations, curious insights, helpful function documentation, and numerous references to useful resources.

@oniabdulazeez00 built his personal project on Visualization of Life Expectancy and GDP Variation Over Time in Africa, wherein he demonstrated genuine curiosity about the argument by creating awesome visualizations that tell a meaningful, number-focused story about Africa by year and region.

In his project on Predicting Housing Prices, @erzinrost dug deep into the data, applied plenty of machine learning models and approaches, made insightful plots, provided detailed storytelling and informative conclusions, summarized the main points in the abstract, and created perfectly documented functions.

@casandra.data.analys shared two projects: Exploring Hacker News and Profitable App Profiles for the App Store and Google Play Markets. They are a great read for their well-structured data analysis workflow, concisely stated project goals, efficiently outlined project steps, well-commented code, valid recommendations, and plenty of room for improvement and potential next steps.

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