You’d probably be wondering- Is this article for me? If you’re seeking help on the ‘Secret Recipe to a Perfect Data Science Project’, the answer is clearly no. The following words (read ingredients) do not attempt to fully explain the procedure behind managing and identifying the right data.
While perfection is merely an illusion, imperfection is real.
Think about it. You’re in a rare, blessed situation, wherein, you have a crystal-clear goal in your mind about an upcoming data science project. You’ve data. You know the estimates. Deriving actionable insights from it seems manageable. The discovery phase, as we call it, is a crucial part while starting up a data science project.
But trust us, it’s very easy to go wrong. This is what the following ingredients will talk about.
If you’re experimenting with a data science project – Chances are, your biggest impediment is translating latest concepts into practice. What will you do then? Doubt yourself? No, the answer is you’ll question yourself -To find out solutions and even more about the ongoing project.
QUESTIONS + SELF-DOUBT = DATA SCIENCE PROJECT?
We don’t know. Let’s find out!
- Who are the users? Find out more about the primary stakeholders. Is your project devised to be leveraged by a group of decision makers?
- What is the problem that you’re trying to solve? Scope or agenda – Your decision! We’d suggest you to keep the scope of your project on priority. Try to make a short list of questions and work on them to obtain actionable insights.
- Do you know the constraints? Company targets, time constraints, or hard deadline –Finding out early will help.
While these questions will help you throughout the project, there is a lot more in store.
There is nothing more fatal than making wrong decisions. No researcher, developer, or employer would ever want to do that.
Data science projects are inherently risky and you’ll fail – Repeatedly.
Learn from your failures. Innovate. Repeat. – The Universal code to successful Data Science Projects
Make these pointers your Holy Grail. They’ll help- Believe us.
- Create an effective group/team – Don’t sell for the ‘One size fits all’ approach. Focus on creating a team bringing diverse expertise on board.
- Use Data Storytelling and Visualization – This merges data analysis, visualization, and written/verbal discussion in the form of an infographic. This helps the layman to interpret the results of a particular data science project.
- Data access is an imperative for every member of the team – Whether its semi-structured, structured, or raw – Don’t try to keep every bit of information confidential – This won’t help you in any way!
- Prepare effective data science processes well in advance – Focus on reducing time while deploying various analytical models. In addition to the process improvements, one can leverage latest tech trends to enable models for multiple applications.
- Avoid Creepiness that comes handy with data science – Protect customer’s behavioral patterns and make sure they’re not hacked in any way.
The recipe of creating a successful data science project will come with many hurdles- These steps will help you bring order to the chaos.
However, let’s face it – There’s no perfect recipe. Everyone likes it their way. Make sure you know who you’re serving it to, and add the ingredients accordingly!