If you’re new to data science, you might be struggling with the coding part. Maybe you sometimes get an error that makes you feel like you might not be able to ever solve it. Maybe you feel like it takes you way too long to solve arising errors. Well, I’m here to tell you that is okay. And in fact, it is actually good. Let me tell you why.

This week I welcome Jayeeta Putatunda on the show! Jayeeta is a senior data scientist specialising in Natural Language Processing and Machine Learning solutions at Indellient Inc. in New York. Take a listen to see what the path to data science looks like for someone whose starting point is economy!‍

It is pretty much a stereotype that data scientists can’t write clean and understandable code. This doesn't have to be the case for you. By learning a few principles of how to write code properly, you can use the stereotype to your advantage and set yourself apart from the competition.

As a data scientist, my work has many layers. I need to keep track of project requirements, demands from stakeholders, code development and new ideas I have that I want to try out. Here is how I stay organised and manage my time.

To become a fully formed data scientist you need to go further than theoretical online courses, you need practical knowledge. I define practical knowledge as knowing how to bring together everything you’ve learned and being aware of the data science way of doing things. Let me break down why practising your skills hands-on is crucial for your data science journey.

In this episode, I have Meg Thomason with me. We talk about her journey from ecology to data science, the challenges she faced, the resources she used, her work life and much more. Don't miss it!

Data science concepts are all interconnected and it is always possible to go deeper and deeper when learning a new topic. But instead of following our perfectionist instincts, we should spend our energy cleverly and take it one step at a time. Setting a foundation and building on top of it is the best way to go.

Learning data science can be challenging at times. It’s very possible to get sucked into the bottomless rabbit hole of things to learn and feel defeated when you make little to no progress. That's why it's good to have a conscious approach to learning new concepts. Here is my learning approach when learning new concepts, ideas, solutions.

The word portfolio used to scare me. It sounded so professional and unattainable. I didn’t know what exactly was expected of me. I thought I would have to make my projects look very sleek and perfect. But I learned that there is no reason to be anxious about it. Let's talk about what portfolios are, what they're not and why you should just focus on getting your hands dirty.