10 Key Things to Know Before your First Data Science Project
Can you imagine navigating through a city without Google Maps? It feels like an alien concept! We have no sense of direction and all paths seem to lead away from where we want to go.
That’s often what the first data science project feels like. I can personally attest to this and I know most data science enthusiasts are caught like a deer in the headlights when they’ve based their learning entirely on just online courses.
Building a machine learning model in Python is great – but doing that in the industry is an entirely different kettle of fish altogether. If you feel that learning Python and the basics of machine learning are going to land you your first data science project or make you a data science rockstar, you’ll be in for a shock.
For me, this reality hit home when I joined an organization as a data scientist. Building a machine learning model was not enough anymore (not even close). There were other tons of things, such as data collection, cleaning, exploration, and a lot more tough work which I had earlier ignored.
A few things I realized quickly – problem-solving skills, creativity, a structured thinking approach, and good storytelling skills will be more helpful than just applying a novel algorithm. Trust me, don’t take this lightly!
In this article, I will be sharing 10 key points that I wish I knew when I started my Data Science career. I hope this will help you out in your own data science journey.
There is a lot of difference in the data science we learn in courses and self-practice and the one we work in the industry. I’d recommend you to go through these crystal clear free courses to understand everything about analytics, machine learning, and artificial intelligence:
Oh boy – if I could shout this from the rooftops, I would scream at the top of my lungs. Hypothesis generation is such a crucial step in a data science project. And yet almost all data science newcomers are ill-prepared for it.
The almighty question at the beginning of any data science project should be – what is the hypothesis behind your analysis?
Let’s say if you go with a non-hypothesis-driven approach, you’ll be bound to look at hundreds or even thousands of variables to analyze without any prior knowledge. This is an extremely hard task for an analyst, right?
A hypothesis-driven approach is much more productive. You’ll first form a hypothesis or an assumption and then accordingly note down the potential variables you’ll need for the analysis. These variables may or may not be available. After this activity, you’ll finally go through the data and select the required variables. If the variable is not available, then you can opt for feature engineering or finding new ways to collect the data.
This hypothesis is the base of your whole project so don’t hesitate to put in the time, effort, and ask for help from your team members. In the industry, you’ll be working with several teams to come up with these hypotheses.
For example, let’s say you are part of the data science team that is working on a fraud detection model at an insurance organization. Here, you’ll be working with the operations team, the leadership team, your supervisors, and perhaps even the sales agents. Your team will have to work with ALL these departments to come up with the hypotheses and figure out which variables you have (or can collect) to validate all these hypotheses.
I found this great discussion on hypothesis generation – you can read more about it here.
There is an endless number of tools out there to build your data science project. Tools like SPSS and SAS had their golden time and now R and Python have taken over the limelight. Now Julia is said to take over both of them. The competition never ends.
Learning the tool takes the least time but learning about the domain and business problems can take years of experience.
