Analysis of the Tweets under the 66DaysofData Hashtag.
The challange was created by Data-Science-Youtuber Ken Jee.
Have a look at the code on my Github: Github

Programming language: Python
Libraries: numpy, pandas, searchtweets, tweepy, nltk, matplotlib, wordcloud, streamlit

Brief Overview:


  • Analyze tweets posted under the hashtag: 66DaysofData.
  • Independently collect data and create a usable dataset
  • Opportunity to engage in text analytics (NLP).


  • Tweepy for tweets up to 7 days in the past
  • Searchtweets for hisrotical tweets

Results(as of 2023/04/12)

  • 40191 tweets from #66DaysofData collected
  • Tweets from 2020-08-29 to 2023-04-07
  • 1902unique participants took part in the challenge
  • Streamlit Dashboard

What did I learn?

  • Methodology for data collection.
  • Data collection over a longer period of time
  • Preparing and dealing with text for a data analysis
  • Deploying a web app with Streamlit share


  • How many did finish the challenge?
  • What are the main topics?
  • Differences between first and second round
  • Sentiment analysis
  • Topic analysis


The #66DaysofData Challenge started on 01.09.2020 and was created by data scientist and youtuber Ken Jee. The goal of the challenge is to spend at least five minutes every day on a topic related to data science and share the progress on social media (mainly via Twitter).

Since I have been following Ken Jee on YouTube for a while, I also started the challenge on 01.09 and published what I did every day on Twitter. After the first month, I had the idea to collect the tweets. I was interested in the topics that the participants are doing and it seems like a great project to do.

Data Gathering

I started collecting the tweets two month into the challenge (29.10.2020) - which was somewhat of a problem. I used tweepy to manually query every week to get the current tweets (tweepy returns tweets up to 7 days in the past). By now, I use a cron-job to do the query automatically every 6 days.

For tweets older than 7 days there is the premium API "Search Tweets: Full ArchiveSandbox" but this is limited for free users and does not allow to remove unimportant tweets (e.g. retweets), so I had to do queries over several months to finally cover the whole period. I queried the premium API with seachtweets.

Data Cleaning

  • Removed duplicate tweets
  • Created specific data and time columns
  • Analyzed the time and date of tweets to figure out if I managed to collect every tweet
  • Used regex to create new columns for used hashtags, linked persons, day of the challenge and links.
  • Worked with the nltk library to tokenize, remove stop words and lemmatize the text data


The Results are shown in a dashboard made with streamlit. Streamlit describes itself as 'a faster way to build and share data apps'. The dashboard shows quntitative data about the the tweets and paricipants can create a wordcloud based on their tweets. The web app can be accessed here.