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  /  Data Science   /  Five free Resources to Become Data Scientists
Data Science, Data Engineering, Cloud Certifications, data industry, tech consultants

Five free Resources to Become Data Scientists

How to build your portfolio as a data scientist?

 

The data infrastructure for an Internet of Things (IoT) platform requires data science to analyze the massive IoT data. There are some free resources a data scientist may find useful:

 

Subscribe to data newsletters

Before jumping into popular MOOC’s interpretation or purchasing recommended books on Amazon, you can start by subscribing to various data science and data engineering newsletters. Learning to recognize the essential links shared in multiple newsletters may prove beneficial. Newsletters are great to keep yourself updated with new tools, academic research, and popular blog posts shared by large internet giants. Some of the useful newsletters are Tristan Handy’s Data Science Roundup, Data Science Weekly, Hacker newsletter, AI Weekly from VB.

 

Craft your own data curriculum

Depending on your concentration, you need to craft your data science, data engineer, or data analyst curriculum. It may include learning how to program in Python or R if you plan to switch careers from a non-programming role. If budget is not a concern, joining boot camps or opting for Udacity and Dataquest courses could be a great deal to get online mentorship from industry experts. However, you are cost-conscious; you may follow open-source guides to create a free curriculum such as:

Open Source Society University Data Science

Andrew Ng’s ML Coursera Course in Python

Python Machine Learning Book Github Resources

Hacernoon’s Free Data engineering Resources

Data Science for Startups

Topbot’s Top AI Research Summaries

Taking these courses is not enough. Tutorials tend to use a smaller subset of the data to run locally instead of walking through a full production setup on the cloud.

 

Network with experts for free

Before the pandemic, there was one option to attend meetups. Still, that opportunity was primarily limited to residents in major tech hubs such as the Bay area, New York, or Seattle. The other alternative was to attend conferences or workshops focused on data science, machine learning, and data engineering. However, these events’ passes were expensive, making it impractical for people to attend without company sponsorships.

Staying updated with new products and industry trends across the major cloud providers may seem helpful to you. Considering the current situation with COVID-19 and the continued shift towards webinars, this may turn into the new norm in networking instead of attending conferences to hold a meeting with other stakeholders in person.

 

Get Certified

Although cloud certifications are not valid for ability or data knowledge, there’s still value in investing in certificates. If you aim to be a data engineer as cloud knowledge is imperative for running production workloads. Becoming familiar with cloud products enables a data scientist to analyze the data instead of struggling to load and clean data at scale.

Another advantage of getting certified is the network opens up. Many members are active on LinkedIn or in tech consulting. They post about new opportunities in cloud data positions. Certification alone will bring you a new job or position. However, having those badges make it easier to start a conversation with others or recruiters.

 

Solve real issues

Finally, if you are already working as a data scientist or data engineer, getting real-world exposure should not be a problem. Others who are looking for transition are recommended to build a portfolio. But how do you start? Working with the classical Titanic dataset for survival classification for the iris dataset is more likely to affect your portfolio than help you.

Try to use public Github projects as inspiration instead. Based on the network you amassed from LinkedIn via tech sessions and certifications, observe what others are creating. Extract datasets from Udacity or Coursera project on Github and mix them in real datasets from Google Research, Kaggle, and start building solutions for real problems.