Can you participate in a hackathon if you are a beginner and don’t have any work experience ?

The Answer is YES - that is what I did and learned a lot from my first hackathon.

Picture Courtesy wix.com

In this blog I will share my experience and lessons learned , which could be helpful for beginners like me.

Recently I heard about a Python Hackathon starting in a few days. At that time I had just started learning Python, and I don’t have any work experience. So I was initially hesitant to join - but  I thought why not? Let's give it a try. It can be an opportunity for learning and meeting new people that can help build my professional network.

Then I participated in a python hackathon which was a 1 week project. I was part of a team and most of my teammates were also new to Hackathon and Python, just like me. 

Stage 1

After the launch of the hackathon, I started understanding the dataset .The given dataset had 3 sets of files. Each file had information about Covid-19 survey reports in Canada during the year 2020.

 I started using the Github workflow for the first time to collaborate with my team members and work together. Github is very useful in creating branches in storing code, data and very much helpful in avoiding losing data problems. Github helped a lot in maintaining all different versions of our team’s Python codes, also storing the data files especially in our case working with large datasets.

Stage 2 - Data Cleaning

Next came the major part that is data cleaning . Only with the Cleaned Data set,  the workFlow will be a mess free one. Or else we need to write the same  extra code for each chart and also it will be a repeated one. It’s like “Clean the dirt in your well and you will get the best water” .

It involves

1. Concatenate all the 3 data sets to make one data source.

2. Splitting master table into sub tables for effective work

3. Correcting manual errors in the data

4. Standardizing the data like Yes/No and True/False to 1/0 if needed for better results

It took us 3 days to clean the data and share with our team and verifying it

Stage 3 - Analyzing

Here comes the real work: Analyzing. Our 80% of time went for analyzing data. With clean data in hand, we need to work on 3 different categories of analyzing

•  Descriptive

•  Prescriptive

•  Predictive

Descriptive Analysis

This Descriptive Analysis is a little bit easy one in which we explain what has already happened. With that information in the dataset we make charts to explain it in the crisp view.

Prescriptive Analysis

Prescriptive analysis is the most advanced in analyzing and it is hard too. Here we need to tell that from the information of the dataset what we should prescribe and how it is important to do immediately. Prescriptive analysis involves using algorithms to provide data-informed recommendations. It is like proving our theory with data. This is the most time taking process in all of analytics. Before transforming the data we need to think the questions of real time problems for the data and later  implement it

Predictive Analysis

Predictive analysis uses the data  to make predictions about scenarios likely to occur in the future. It helps in risk management . We were able to do the Predictive analysis effectively  using Python’s powerful libraries such as NumPy, pandas, and Matplotlib / Seaborn and algorithms such as Regression Models,Classification Models,Clustering Models,Advanced Techniques.

Stage 4 - Dashboard making

Dashboards are beautiful and easy for presenting our data but the work behind making it is a little bit hard. Streamlit will turn our python scripts into interactive web page .As a beginner we all struggled hard to make streamlit in our computers.It involves several process.First we have to in stall pip 

pip install streamlit

And then we have to put our python code in the notepad and save it in .py  format. Finally we have to run .py file

streamlit run app.py

It took 1 day for making Dashboards

Stage-5 Presentation making

Finally we came to the easy one: the Presentation making. We made presentations in Canva. It is very user-friendly. In the Presentation, we explained our data-cleaning process ,our insights, our Analysis and what we gained from the dataset.

Pros of hackathon

It helps with team building, helps to grow our skill in programming (in our case Python coding) faster, helps improving our data analytics skills like handling messy dataset in a way we need, improving soft skills : quick decision making like if it is needed or not, prioritizing tasks and giving sufficient time for each task.

We will get a chance to work with motivated people, smart people. We will get mentors. We will develop friendship and make connections with our team

All together I learned new things, I gained confidence,I understood my strength and weaknesses and how to improve myself.

Cons of hackathon

The time pressure makes us to act quickly. It gives stress and pressure . As a beginner I felt overwhelmed with deadlines.

Team work with new members: With the short span of project time and totally new members, it could be difficult to collaborate effectively. There may be difference of opinion among team members. If team members work in silos - like one person cleaning the data and not sharing details about their work with other person who is doing analysis it could become a mess. We will need to make sure team work is transparent and appreciate each other.

Our work will be vast but our presentation should be crispy for easy understanding. 

Conclusion

Hackathons reveal our true strength.

This Python hackathons  helped to improve my ability to think critically, adapt quickly, communicate effectively and apply the python knowledge effectively.

If you’re an analyst, these challenges will feel familiar. And if you’ve never participated in a hackathon, now you know the truth behind the scenes and you should definitely give it a try ! Have fun in Coding!!!