Rahul Banerjee

Ideas to start your Data Science Blog in 2021

Ideas to start your Data Science Blog in 2021

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8 ideas for your first or nth Blog Post

Before we discuss the ideas for your first Data Science Blog Post, let’s debunk some myths about writing Data Science/ Programming articles

Myth: You Need to be a Subject Matter Expert

Truth: You just need to know a topic well enough to be able to explain it to others. It can be as simple as Linear Regression using scikit-learn or as complex as NLP. My first article published on Towards Data Science was a tutorial on Selenium for building a bot. I didn’t know much about Selenium but I knew how to scrape Google Search results and wrote a tutorial on it.

Myth: You need lots of time to maintain a blog

Truth: If you can devote a few hours a week, it’s good enough. Your first few articles might need you to dedicate a lot of time but once you get comfortable writing articles, it should only take you a few hours to write a good article. I usually spend only 5–6 hours a week writing articles.

Myth: You need to be working in Data Science for X years

Truth: Nope! I am a 4th-year Undergrad Computer Engineering currently on a co-op term with 0 years of industry experience related to Data Science. Most of my experience is related to web dev and I just started learning Data Science in the summer

Myth: Your articles need to be perfect

Truth: Most articles are not perfect. Not everyone is blessed with the mind of a coding prodigy or impeccable grammar and that’s OK! I am pretty sure if you read this article thoroughly, you will probably find quite a few mistakes.

Advantages of Having a Blog

Photo by Micheile Henderson on Unsplash

You Learn

“If you can’t explain it simply, you don’t understand it well enough.” — Albert Einstein

When you write an article on a topic, you end up knowing more about the topic than you did before you started writing your article. When you write about a concept, you research on it and read other articles further understanding the topics. The best way to learn something is by teaching it to others :)

You Earn

If 2020 has taught us one thing, it’s never to be dependant on only a single source of income. You need some side hustles and Blogging is a good side hustle. There are various ways to earn money

  • Medium has Medium Partner Program which lets you earn based on the time Medium Members spend reading your article
  • Ads on your own Blog
  • Affiliate Links
  • Sponsored Posts

It’s been 4 months since I started writing articles consistently and I just crossed 100 bucks on Medium this month.

You grow your Personal Brand

Having a Blog gives you an authority in your domain. This increases your chances of employability as well.

Where to Start your Blog

WordPress

WordPress makes it really easy to set up a blog and monetize. It has a lot of plugins and you don’t need to have any knowledge of HTML/CSS/JS. There are multiple themes available. You can check out my WordPress blog for inspiration

http://realpythonproject.com

Medium

You can start a blog on medium in a few minutes and it’s FREE. Publishing to large publications such as Better Programming, Towards AI and Towards DataScience can get you a lot of views even if you don’t have many followers

Custom Website

You can use React or any other popular framework. This gives you a lot of flexibility and the ability to customize your website as you wish.

Ideas for your Blog Post

Photo by Riccardo Annandale on Unsplash

1. Listicles aka List Posts

Although not scientifically proven, most people love reading listicles. You can list down your favourite Python Libraries or the top 5 Data Science Side Projects.

  • 10 Datasets for your next project
  • 5 SQL Commands you must know
  • Top 20 Data Science Interview Questions

2. Tutorials

If you know how to build something or recently completed a side project, you should consider writing a tutorial on it. It helps you find places in your project you can improve/optimize on and also creates a great blog post

  • Building a Twitter Bot
  • Use Streamlit and Scikit-Learn to build a web app to classify Pokemons
  • Build a Linear Regression Model from Scratch

3. How-To Articles

If you recently resolved an issue or successfully did something after spending hours on stack overflow, you can write an article detailing how you did it. This might be really helpful for others. You can also write articles on Python Libraries

  • How to Deploy a Web App on Heroku
  • How to use Plotly for Data Visualization
  • How to use Beautiful Soup for Web Scraping

4. Comparison

You can compare product with similar use-cases and give your opinions on which you think is better.

  • Python vs R for Data Science
  • Flask vs FastAPI
  • Mac vs Windows vs Linux for Data Science

5. Reviews

If you recently had experience using a product or working with a library, you can write a review on it

  • Macbook 2020 for DataScience
  • Review of GPT-3

6. Cheat Sheets

Reading library docs is tiresome and most of us are looking for cheatsheets, a good cheatsheet is a life-saver.

  • Pandas Cheatsheet
  • Learn SQL basics in 10 minutes
  • Streamlit 101

7. Resources

Mention the resources you used to learn about a subject,Programming Language etc

  • Resources to ace your Data Science Interview
  • Resources to Learn SQL
  • Best Data Science Resources for 2021

8. Tips on Landing a Job/ Client

If you recently landed a job or a freelance client, consider writing an article about it for the benefit of others. You can mention the resources and strategies you use, your experience interviewing at XYZ company

  • Using LinkedIn to land your first Dat Science Internship
  • How to prepare for your Data Science Interview
  • My Experience interviewing at XYZ company


Conclusion

As mentioned above, starting a blog has numerous advantages. The first few articles are always the hardest to write, it is not smooth sailing from there on but it gets easier to spend time writing. The first few months are going to be rough but just be consistent :)

#programming-blogs#blogging#developer-blogging#data-science#python
 
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