allWomen Talk: Let’s talk about Data!
Decoding some of aspects about data disciplines, tools and myths.
If things like looking to make a transition into a data role, or, start your journey to become a Woman in Tech, are on your New Year’s Resolutions for 2022 🎯, we have bright news and some challenging ones also.
At this stage we all know a shift in this direction would be extremely beneficial for our future careers, because let’s face it — the world is speedily becoming completely data-driven.
When the tech industry start catching my attention, I came across this article from World Data Science where about why Data roles are the hottest professions in the new era — I found out that tech as a career path can feel very appealing, challenging and profitable at first sight and the part that excites me the most is that people are the ones building the jobs of the future and the future itself, not just training for it.
Make sure you understand the type of data role you can enjoy or perform better
I recently had the chance to attend to this stimulating conversation between some data professionals, teachers, and graduates from AllWomen who shared some interesting insights with us about their different paths & current experience within data.
I will show you the outlines and main points in this article. We will talk about data from different perspectives in two distinctive parts, data roles in the first part, and data skills&tools in the second one.
📌 Let’s start with the definition of Data Science from one of our teachers from the AllWomen Data Science Bootcamp: Nohemy Veiga (Lead Data Scientist at NTT Data CoE )
“Data Science is an interdisciplinary field, where you get value from the data. You will be dealing with different sources of information, either raw or most commonly structured information to get meaningful insights.
As a Data Scientist you need to put into work lot of processes such as transformation, statistical analysis, machine learning…with the objective of automating decision making processes”.
- In 2020, users sent around 500,000 Tweets per day.
- By 2022, 70% of the globe’s GDP will have undergone digitization.
- By 2025, 200+ zettabytes of data will be in cloud storage around the world.
Currently data is being used to create massive impact in lots of diverse industries — banking, media, education, online retail, etc, and is being used by companies to detect and predict consumer behaviour. It’s objective is to generate new business opportunities.
If you are that person that can manage big amounts of data and solve problems with the answers from this data, there is room for you in the data scientists ecosystem.
📌 We also had the super interesting vision from Ona de Gibert Bonet (Language Data Engineer at Barcelona Supercomputing Centre) who told us a bit more about new exciting research disciplines in linguistics.
- What is NLP and its goal❓
NLP stands for (Natural Language Processing or Computational Linguistics) The goal of NLP is to merge two worlds which are languages and programming technology to build applications (eg: google translate or many other processes).
Humans want to teach and make understand computers our language almost since they were created but NLP it’s getting very famous into the data science field nowadays, the applications around it can bring tons of money to every business trying to integrate it, from voice assistants, targeted advertising, hiring, email filtering…
You can learn a bit more into detail on this introductory guide about NLP where this explanatory sentence resumes perfectly ⬇️
Natural Language Processing or NLP is a field of Artificial Intelligence that gives the machines the ability to read, understand and derive meaning from human languages.
Regarding the data industry itself there are many other roles that are starting to pop up. Sometimes is not about the position itself but which department might have the need, the business background each individual can contribute with the right set of skills.
Some of the ones mentioned on the talk were:
✅ Data Engineers — What they do is building data pipelines on Big Data. They also take large amounts of data and make it usable for data scientists for example.
✅ Data Modelers — They are more focused on building storytelling with data, present results to build a story. Basically prepare information to make it more digestible.
✅ Data Analysts /Scientists— for any department required.
✅ Research Engineers — They gather relevant information, data or samples, then analyze their research and perform tests to create optimal and innovative solutions.
✅ UX Researchers — Study target users to collect and analyze data that will help inform the product design process.
✅ Web scrappers — Web scraping involves extracting data from specified websites. The scraper is therefore given the URLs to the websites it’s supposed to scrape. It loads the HTML code of these web pages and extracts the data needed, such as prices or customer reviews. The scraper then outputs the data in a readable format.
Never underestimate your soft skills but don’t forget to learn the right tools
First and foremost, ensure that you have at least one solid programming language skill because it will be required in every data science job description.
What we have extracted from the talk is that the hottest one and mostly applied in every role is Python 🐍.
In December 2021, #1 was Python with a 29.69 % of share of searches according to Tiobe, a Dutch software quality assurance company that has been tracking the popularity of programming languages for the past 20 years.
But others are also commonly used today, we need to take into account the word “today”, because we all know by now, that due to the fastest changing industry needs, it’s a good idea keeping in mind how agile technology evolves.
🔩 Data Analysts — SQL, Python and libraries attached, Google Analytics, Periscope and Tableau for visualizations, and Amplitude for the AB testing.
🔩 Computer Vision Scientists (Machine Learning)— Open CV, Tensorflow, R for Times series and Notebooks especially useful when learning or sharing code, even documenting it.
🔩 Data Engineers — Scalar, Airflow, Redshift for cloud warehouse.
However, you need to go beyond programming, there are some skills you should try to acquire in order to stand out from the average data scientists applicants.
- You need to show strong communicating skills through story telling, try to maximize impact with simple words.
- Problem-solving skills. Having creative solutions and knowing there’s no just one right way to find solutions. Learning to code gives you the opportunity to expand those skills. Find some extra content on TED talks that I would recommend watching on problem solving.
- Fast and able to learn. Try to become the best possible generalist version and try to find your space to grow as a specialist. Face that broad expertise takes time and experience.
- Show if you are a team player. Companies want to recruit people that has people skills, who will blend in with their existing data science teams, and someone who is ready to adapt to any difficulties he encounters when problem solving.
The ability to show bravery and overcome fear that you will end up destroying with upskilling
If there’s something in common with either entry level and already on the ground data women is that they’ve all experienced at least once in their careers is the Imposter Syndrome (find a bit more on this allWomen’s talk about Imposter Syndrome with Ana Zamora).
Feeling like a fraud and that your success is a result of luck creating self-doubt, again can be defeated with some of those tips given by our speakers.
📍Learning as fast as you can, as technology evolves really quickly, and see if you feel excitement about it.
📍Focusing more on the things you want to build and upskill, start learning and progress a bit everyday, try to impress yourself celebrating small steps.
📍Understanding that everyday is a new challenge, you will never stop learning.
📍Being aware that programming teach you a new way of thinking.
About the will to become a female reference for future generations and break the gap in tech
In the future, AI will be more present and rule most of our lives, and we need to push forward data driven decisions sharing them with men to avoid flawed algorithms.
Gender gap is obvious and measured, recently a teacher from UPC Engineering University of Barcelona reported around 13% of female graduates in Computer Science, compared to 2018 that it was around the 7%, that translates into a significant increase in becoming more technical, but it’s still a huge challenge to extend that percentage.
One of the conclusions is that girls have a lack of references as they grow, figures which they can admire, more computer or data scientists woman are needed. So as we continue bridging this gender gap, a good mantra for our everyday life could be, to become that inspirational woman that future generations will have to look at.
Data and tech are very exciting fields, new things happen everyday, we are also in a very exciting moment and women need to be there to witness — It’s essential to bring up more diversity and we can do it creating sense of community, belonging and safeness.
🎈Bonus content
Some recommended links for someone with or without programming language experience:
- Datacamp
- University of Helsinki course on Elements of AI
- Khan Academy for statistics (free)
- Crowcast channel AllWomen Masterclasses
- Kaggle
- Codecademy
- Udemy
- Coursera
- Hackerrank (good for practicing)
🎙 If you want to listen the whole talk 🎙
