I love data science for the journey rather than the results of it. The process of making something out of seemingly nothing delights me every time. In this article, I'd like to share some of the specific things I observed in my job that makes me like it more every day.
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.
Starting to work with data at your current job is one of the best things you can do for yourself, especially if you want to leave your company and start as a data scientist somewhere else. Let me tell you why I think doing projects at your current company matters and how you can make it happen.
Impostor syndrome is simply not feeling up to the job and thinking that others will figure you out and call you out on it. Let me tell you why I think this feeling is particularly common among data scientists, why it is normal to feel this way and how you can change the way you think about it.
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.
It is only natural to think of coding and everything technical when you say data science but that’s not necessarily the case. Even though data science requires you to have some technical skills, most of the time what counts is how much you understand the problem and how well you can explain your solution.
It is hard to give a comprehensive list of all the skills that will be in-demand in the future but I will share three most likely candidates that, in my opinion, companies will be looking for in the coming 5 years.
If you ever ventured into the world of Deep Learning, you might have gotten stuck where many other people do: Which technology do I use? We have quite a few options of deep learning APIs but not all are suitable for first-time users. Let’s look into them one by one and decide on which one to use.
When is the right time for you to share your data science work with others? At what point will you feel comfortable enough with what you have done to proudly show it to your friends, family or mentor? The answer is sooner than you think.
Bias and variance are some of the trickiest concepts to get a solid understanding of. It was explained to me a bunch of times and every single time after a couple of weeks I found myself thinking “Which one was which again?” So today, I’m on a mission to explain it to you in a practical way in hopes that this might be the last time you might need someone to explain it to you!
Previously, we talked about bias and variance. And we learned that high variance means overfitting. But what can we do to deal with overfitting once it happens? A go-to technique is to use regularization. Let's look into it in this article.
When you're first starting out with coding, debugging your code or resolving errors could be one of the hardest things to do. It's actually not at all complicated. You only need to make sure to follow some simple guidelines to determine the source of your problem and then make a plan to solve this problem. Let's see what these guidelines are.
Being a senior data scientists is treated as a holy grail though many do not know what it really means to hold a senior position. Let's look into what responsibilities come with the title. This article might give you something to think about when it comes to what type of senior data scientist you want to be.
LinkedIn is a great source of people who can help you with your professional life. This could be getting advice, acquiring information about a company, hearing about open positions and much more. Though, in order to get the most out of it, you need to follow a smart approach.
What does a data scientist deliver? What do they do that everyone is so hyped about? It's important to know what data scientists deliver not only to understand the profession but also to understand what type of job you want to land.
Data science can take many shapes and forms. It is important to get insights from people working in different industries, different sized companies and different roles to really understand what type of data science that you want to do. In this episode, Víctor García Cazorla joins me to share his data science story.
First episode of a series of interviews with data scientists. Madli is a colleague of mine who is currently working for a new and exciting start-up called Pactum. Take a listen!
Deep learning sounds so cool, doesn’t it? There is machine learning, which is already super cool and now make it deep. Even better. But what makes a learning algorithm deep?
Data Science is evolving rapidly. New techniques are being developed everyday. Naturally, companies are leaning towards hiring people who are willing to keep up. ML explainability and AI Ethics are two of the most important extracurricular themes in AI. Demonstrating your understanding of these themes can do wonders for your prospects. Even if it means just showing that you gave them some thought.
Learning Python and Machine Learning are two of the most fundamental things you need to do to become a data scientist. Out of the two, learning Python is relatively easy. Learning machine learning though is when things get very overwhelming very fast. There are many concepts you need to be aware of and they have very intertwined relationships. That's why you need to have a smart strategy when learning them. I talk about my own approach in this article.
I analyzed 100 entry-level job postings from the US and Europe on LinkedIn. In this article, I will share my results with you. I will show which skills came up most often and tell you what I think about how these requirements reflect reality.
The question of whether you should get a master's degree or not is very personal. It will depend on personal preferences, learning style and career goal. In this article, I will not list arguments for or against getting a master's in data science. Instead, I will give you pointers on what to consider when making this decision.
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.
I would understand it if you were worried about this. You’re getting interested in a new field, looking into how to study it or maybe you’re already studying it and then there is this talk of it dying. You see questions on Quora or on Reddit, tons of articles written as clickbait, seemingly naively asking “Is data science dying?”. Let’s see why not by going back to when the term first started becoming a fact of everyday life.
When changing careers into data science, if you have years of work experience, it's only fair to expect a higher position than junior data scientist. In order to achieve it, you need to approach your job hunt differently than newly grads. Here are some tips for signalling to your future employer that you will bring value.
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.
Pandas can be intimidating at first but it's such a great library with so much potential and so much flexibility. Here are the main working principles of Pandas I have picked up over the years that will boost your coding performance.
One of my student’s on the Hands-on Data Science course asked me an intriguing question this week. After completing the course, he reminded me of the project goal we formulated at the beginning of the course and wanted to know how the model we built helped solved this problem that we defined. And the answer to his questions was: it doesn't. But with good reason.
This week I wanted to write about the disciplines people commonly transfer to data science from. All I needed was to collect some data off of LinkedIn, analyze it and report what I found. I thought it was going to be an easy feat. I was wrong...
Being curious about how much money you would make when you become a data scientist is expected. Though it is good to keep your intentions in check when it comes to switching careers. You want to make sure you pursue a position for the right reasons. But either way, it's good to realise early if you have the wrong idea in mind...
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.
You've probably heard that in data science knowing your data is important. But why and what does it mean to know your data anyways? I answer these questions in this article.
Job hunting sometimes feels like looking for a candle in a dark room we’re unfamiliar with. We don't have complete information, every company has different standards, and it’s unclear where this position might take you in the coming years. Knowing what to expect from team structures can give you hints on what type of data science work will be expected of you and which direction your career will grow towards.
Two weeks ago, a bunch of people stormed the Capitol building in the U.S. This whole situation reminded me of a saying we have in computer/data science: garbage in, garbage out. In this article, I explain why.
I break down my decision process while searching for, finding and applying to a new job, to help you at your job search. I also will talk about how I managed to land a position where I think I’ll be very happy. This story has lessons in it for anyone who wants to get the type of job they want.
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.
Deep learning has its base in neural networks. Neural networks are (you guessed it!) networks of neurons. Vertical groups of neurons are called a layer. Each neuron accepts inputs, does some calculations and spits out an output that is sent to all the neurons in the next layer. Today we look into forward and backward propagation in neural networks (aka how a neural network learns).
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.
By now you have probably heard this explanation: ML algorithms learn like humans. You give it examples and it recognizes and remembers the patterns in them. You need to give it a lot of examples, though, so that it can learn accurately. Okay, that’s clear. But HOW does it learn?
Khuyen Tran is a student and a data scientist who got her first job before even graduating. She tells me all about how she got her first job as a data scientist while still studying, how her writing on Medium brought a lot of opportunities to her foot, what resources she used to gain new skills and more. Do not miss this episode!
A lot of people with a background in computer science become data scientists and the main tool of a data scientist is programming. It's only natural to think that these two fields are closely related. But they are not...
In this episode, we get two perspectives for the same data science position with Nikola and Petr. We talk about how they ended up where they are, what kind of responsibilities they have day-to-day and how they like their work right now.
In this episode we get to learn more about technical writing in an ML team and what this job entails. Melissa talks about her journey of becoming a technical writer, shares the kind of tasks she performs day-to-day.
This week on the show I have Yaakov Bressler. A free data scientists from New York who combines his many interests such as data science and theater and comes up with fascinating projects. Take a listen!
On this week's episode, I welcome Katia Stambolieva, proud co-founder of the space tech company NinaSpace and a freelance data scientist who primarily works on social, civic or environmental projects.
In this week's episode, Jigyasa Grover joins me to talk about her career and machine learning engineering. She shares her journey of becoming a machine learning engineer at Twitter and the steps she took throughout the years.
In this episode, we look into one of the many data-related titles: product analyst. Kasia Rachuta is a product analyst at Square, a financial services company in San Francisco. We talk about how being a product analyst differs from being a data scientist, what her daily responsibilities are, what she loves about her job and what she did to get where she is right now.
Samantha joins me to talk about her career in the fourth episode of So you want to be a data scientist. We talk about her journey from academia to business, discuss her daily responsibilities and more.
In this episode, I talk to Sadie St. Lawrence, the founder of Women in Data. Women in Data is an international organization that aims to increase diversity in data careers by providing awareness and education to its members. Listen to my chat with Sadie to find out how she herself became a data scientist, what advice she has for the aspiring data scientist, how you can also become part of Women in Data and much more!
In this episode, I talk to AWS data scientist Naz Levent. Naz has been with Amazon for more than two years and has been part of some exciting projects in fashion, energy and entertainment industries. She shares with us how she got into data science, how she got her first job, what her days look like and why she loves her job. She has some unusual and great advice on how to get ready for a data science career.
This week on the show I have Nicole Janeway Bills. She works for Atlas Research. After realizing her passion in data analytics, Nicole followed her heart and started the journey that brought her to where she is today. We talk about how she decided to become a data scientist, what qualifications she went through, how she landed her first job and much more.
This week on the show I have Mikiko Bazeley. She is currently busy co-founding Sidewalk.ai, an AI based solution for residential real-estate. We talk about sidewalk.ai, her story of becoming a data scientist, the projects she's involved with and the future of AI. Mikiko also shares her advice to aspiring data scientists on how they can turn their career around in the direction they want.
In this episode, we hear how Ceren changed her career from being a logistics consultant to a machine learning engineer, which resources she used to learn data science, how she landed a remote position, her projects and more.
A consultant’s life is an exciting one. You get to experience many different industries, domains and technologies. Rossy is one such consultant. She joins me this week to discuss how she got to her position as a consultant data scientist, what she does day to day, what kind of projects she participates in and more. Tune in to learn what you can expect from a consultancy position!
Have you ever thought about starting your own business? It is very accessible to anyone who has some time to invest. Because becoming a data scientist or starting a career in data doesn't have to mean that you will work at a company or have a boss. In this episode, I talk to Susan Walsh. She started her own business in an area she wasn't particularly trained on: data. And now her business is flourishing and she's having a lot of fun.
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!
I mention to people that to become a data scientist you need to have a data drive mindset. But what does it mean to have a data-driven mindset? How do we learn it? In this article, I break it down into three main concepts.
Sakshi Mishra from the National Renewable Energy Laboratory joins me to talk about her journey and her work. We discuss her contributions to renewable energy research, how the lab operates, how she got this dreamy position and many other things. Don't miss it!
Selene is a PhD student working on interaction between humans and robots. We discuss her journey towards starting a PhD, how she acquired her knowledge, what she did before starting her PhD and the details of her research.
Shivali is a globalisation engineer at Adobe. She uses NLP as her main tool working on making Adobe software applicable to different cultures and languages. Her work is a great example of a non-data scientist position where data science skills and ML techniques are used as the main tool.
You can go as fancy as you want when setting up a data science environment. There are many tutorials out there showing how to customise and personalise your environment for perfect comfort. I want to show you the simplest way that you can get set up, so that you can stop worrying about having a working environment and start coding away.
It is important to have a good understanding of possible approaches to hyperparameter tuning to be able to efficiently make the correct decisions when it comes to tuning your network. Let’s take a quick look into why this is an issue, to begin with, and review the current techniques out there that you can use on your projects.
From what I’ve experienced so far, being a data scientist has its fair share of … side-effects let’s call them. In this article, I will tell you some of the day-to-day problems/realities a data scientist most likely deals with, in hopes to illustrate the reality of the job.
Since I started working in Amsterdam, I have been getting a lot of messages through social media about living in Amsterdam, the schools, applying for masters and working as a data scientist. After all, Amsterdam is a great city to live in and data science is an exciting career option.
Have you heard about the recent groundbreaking achievements in NLP? Wow, you have to read this paper... Everyone is talking about this woman/man in AI. If reading headlines like these stress you out, you're not alone.
There is a group of people who seem to give off only negative messages about becoming a data scientist. I call them the data science elitists. In this article, I want to show you how you can have your shield up against negativity on your way to becoming a data scientist.
Data Science Kick-starter course is ready! The course is prepared to help you set the right goals for your data science learning efforts and meet them.
Having a portfolio is crucial to land the data science job of your dreams. And as long as you get your hands dirty working with data working with interesting use cases, you will impress your future employer. But you can always go the next mile when it comes to presenting... and make an interactive web app out of the project you built.
There are some misconceptions and confusion about what data scientists do day-to-day. Let me take you on a journey in the world of data science. In this article, we will follow along the steps of a data scientist on a project.
It's not easy to choose courses. There are many courses offering different sets of knowledge and nearly all claiming that they are the best course. In this article, I will share my opinions on the popular data science courses and books and recommend ways of studying data science using these resources.
In every profession, there are disagreements between the members of the community. Having an opinion on these disagreements is a neat cheat to look and feel like part of the community. Sooner or later you will be in the middle of these discussions anyways. I just want to give a small boost with this article.
There are many interesting topics out there and there is no shortage of good online courses. So we tend to attempt learning things which end up being unrelated to each other even though they are in the same, massive, overarching category of “Data Science”. This, in turn, makes us feel unsatisfied and inadequate when it comes to our skills.
It’s important to know what type of position you want to work at, in order to get the most satisfaction out of your job. I understand this is not very easy to do when you have little to no experience in a field. Inspired by everything I’ve heard from my guests on the podcasts, I prepared a list of possible positions you can consider when planning your future.
It’s nice to be working from home. I like the comfort of my own home, my tea collection and being able to work in comfy clothes. Still, it has been getting to me at times. That’s why I thought I’d bring together a list of small personal pursuits or data adventures you can go on to get your mind off the current events.
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!
Here are some lessons I've learned being a junior data scientist. Hopefully, this will help you understand the struggles you’ll likely face when you start off your journey as a professional data scientist. Knowing the potential problems is a good way of understanding the reality of a job.