5 Concrete Real-World Projects to Build Up Your Data Science Portfolio

5 Concrete Real-World Projects to Build Up Your Data Science Portfolio

The market currently gets tougher. So, you must be mentally prepared for a long hiring journey and many rejections. I assume that you have already read that a data science portfolio is crucial and how to build it up. Most of the time, you will do data crunching and wrangling and not applying fancy models.

Do you want to enter the data science world? Congratulations! That’s (still) the right choice.

The market currently gets tougher. So, you must be mentally prepared for a long hiring journey and many rejections. I assume that you have already read that a data science portfolio is crucial and how to build it up. Most of the time, you will do data crunching and wrangling and not applying fancy models.

One question that I am asked on and on is about concrete data sources for cool data and project opportunities to build such a portfolio.

I give you the following five ideas for your data science portfolio and a few hints on developing uniqueness.

Five Concrete Ideas for Data Science Projects

1. Customer analytics for a local non-profit organization

An essential task of a non-profit organization is to find the right person, at the right place or location, in the right moment, approached with the right medium for donations for charitable activities. When that can be optimized, the non-profit organization can collect more funds and do more activities.

What makes that project interesting?

First, most non-profit organizations have much data, not necessarily in digitized form, and often not in good quality. The main task is building a database, data crunching, and getting the data in a usable form. You learn to structure the whole data mess, which is still up to 80% of a data science job.

Second, you do something good for the local community, and you show your social responsibility. You interact with people who are not data experts. Both shows needed soft skills for a data science position.

I did voluntarily such projects for an organization that helps children in poverty and for an organization that provides care at home for elderly besides my professional job. Having these experiences builds trust in your person and is a door opener for many other exciting projects.

Finally, non-profit organizations work the same as private banking or wealth management. They also have to acquire the right customer, at the right moment, with the right campaign to bring them money. And I can tell you; the data are also not of better quality than of a non-profit organization. You can directly leverage your experience in other industries.

How to start?

I found the non-profit organizations through my network. There is always somebody within your family, relatives, and friends engaged with a non-profit organization. Then, I agreed on a first get to know meeting and explained to them what my skills are and what is the value of such analyses. I have given them examples from Google and Facebook. And I searched for publicly available information about the increase in leads at other non-profit organizations to provide them with a flavor. After I have given them first the time to think a few days about it, and in each case, they came back and agreed to do the project. Then, I started the whole data crunching work.

When the data is ready to use, you can work through the classical descriptive, predictive, and prescriptive analytics cycle.

artificial-intelligence data-analysis machine-learning data-science data

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Most popular Data Science and Machine Learning courses — July 2020

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science

Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.

Artificial Intelligence vs Machine Learning vs Data Science

Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.

Learn Data Science Today - Data Science Tutorial for Beginners 2020!

How and why to start Learning to be a data scientist in 2020! This Data Science Course will give you a Step by Step idea about the Data Science Career, Data science Hands-On Projects, roles & salary offered to a Data Scientist!