You too can become the data science professional. How?

An analysis insight provided by world-leading data science professionals on how to become the data science professional in your own time

Today, while I was exploring the Kaggle data science survey 2018, for a specific task, my eyes suddenly got stuck at the following analysis graph:

The above graph shows the survey feedback (conducted dring October 2018) from almost 24K (23859 exactly) data science professionals worldwide. In this graph, you will see, how did these professionals become master in the art of data science.

If we extract only the following 3 data science training criteria, which any wannabe data science professional, can try in their own time, we can see the overall probability of getting expertise in the art of data science:

1. Self-taught: 18%
2. Online Courses: 16%
3. Kaggle Competitions: 6%

Combining above the total becomes around 40% which is great for someone to convince they too can follow the same model to gain expertise into data science.

So what you just need to do:

• Join open online data science and data engineering courses
• Read articles, participate in GitHub projects, write your own articles, share the knowledge
• Start competing into Kaggle Competitions as a team (start alone and then either form a team or join a team)

Where does data science professionals spend most of their time at work:

Based on Business over Broadway analysis of the same data science survey analysis the following graphics provide very valuable information:

Based on the above analysis you can see that various kind of data science professionals spends the majority of their time into the following functions:

1. Gathering data
2. Cleaning data
3. Visualizing data
4. Model building/model selection
5. Putting the machine learning model into production
6. Finding insights and communicating them to stakeholders
7. Other

Digging deeper into the same analytics we can also see that over or almost 50% of the time is spent on data engineering tasks which mainly:

• Gathering Data
• Cleaning Data
• Visualizing Data

While in another May 2019 survey reported into Analytics India Magzine shows the time spent by data science professionals survey, the analysis graph is as below:

From the above graph, we can get the following metrics:

1. Cleaning and organizing data: 60%
2. Collecting Data: 19%
3. Modeling and Machine Learning: 9%
4. Other: 5%
5. Refining Algorithms: 4%
6. Building Training datasets: 3%

Combining analysis together:

• Applying Self-taught, online classes and Kaggle competitions method is something you can try your own to become the data science professional
• Majority of data science professionals work is performed on data which can be acquired from various open-source & public web sites from government and private sectors.

@avkashchauhan