2024 has just started, and there hasn’t been a better time to get skilled in all things data and AI.
Here are some ways, with many tips, resources and a practical guide, to set you up for success now and in the future.
Hi, I'm Mauricio. Connect with me on LinkedIn to share ideas and keep the conversation going!
Just last year, we had what McKinsey called Generative AI’s breakout year. From the debut of ChatGPT in late 2022, 2023 became the year when everyone was talking AI, with many incredible tools becoming available and accessible to the public at large.
And the prevalence of data, in other ways, is undeniable. Being data literate has become a necessity. Similar to what happened with computers in the nineties. According to The Human Impact of Data Literacy report by Qlik and Accenture (2020), employees who identify as data-literate were at least 50 per cent more likely than data novices to say they feel empowered to make better decisions and trusted to make better decisions.
But that presents a challenge in itself: Where do I start? How do I keep learning? 🤔
Here are some ways you can tackle that! 👇
- Network with data people
By data people, I mean everyone and anyone working in the space. Data analysts, scientists, engineers, leaders, storytellers, statisticians…
Read what they post, connect with them, and engage in comments. Just make sure that you consider the credibility of who is posting before taking the information for granted, especially on technical subjects (such as statistics). 🗣️
Sometimes, I just stumble upon an incredibly engaging and enlightening conversation between experts on the topics they love. ❤️
The Growth Tribe Community has people like you: upskilling and eager to learn and share. Reach out to them there and give/receive feedback on your exercises. Not only that, don’t be afraid to ask questions about the topic at large. 🚀
My favourite social media for networking is LinkedIn. Posts tend to be more organised, and conversations have good depth. It also helps that you can check everyone's professional credibility. Keep in mind that most of the data content here will be geared towards beginners just because the audience is a lot bigger, and the algorithm will naturally play better with that.
For deeper conversations, follow some of these (and other) experts:
For more random and open conversations, Twitter (now known as X) is also great! I don’t frequent it, but some smart data colleagues swore by it. Just follow good data-related hashtags: #data, #bigdata, #ai, #dataanalytics, #datascience.
Also, join data forums. Reddit is a fantastic platform for that:
r/datascience subreddit
- Learn a new tool
With data literacy being so important, effectively using data tools is how we get to make sense of the data, to begin with. Learning a data tool will never go out of style.
Look at Microsoft Excel: it launched in 1987, and it has more than 750 million users worldwide today. Or look at SQL (Structured Query Language), which is the “lingua franca” of the data world: it appeared in 1974, and it’s still the foundation of how we engage with databases.
Many modern tools go beyond and have novel capabilities with their particular use cases. Machine learning, for example, is a nightmare to do in Excel (although possible… but seriously, don’t try it 🙃), therefore, newer tools like KNIME or Dataiku are a lot more appropriate.
The cool thing is that many skills and data processes are transferable between tools once you understand the main concepts. Tableau also has formulas, like Excel. You can run Python in Excel, and you can create tables and pivot tables in Python. 📈
If you are looking for hard skills, I can’t recommend this enough. Pick a tool or language and get proficient in it. A quick breakdown of some of the popular, powerful and important data tools and languages 👇:
- Deep-Dive on New Techniques
Once you’ve become familiarised and proficient with one or more tools, you have to look at “what” you want to be doing with them that will be relevant to you. Chances are there may be some very interesting and powerful data techniques out there that would help you solve problems.
Here is how to approach this angle:
For example, say you work as a marketing analyst in a telecom company. The problem you are having is that the promotions aimed at retaining customers are too general. You believe that if you had customer segments, you could create more targeted promotions. You reach out to your network, and a lot of people say that you can cluster customers based on their behaviour by using the k-means algorithm. Then, you search and get familiar with the core idea and how it works. You get the data from your company and transform it to have the shape you need (e.g., each row represents one customer, with columns representing their behaviour). Finally, you use ChatGPT Data Analysis to help you develop the solution and run the algorithm. Keep digging to understand the technique in detail.
Cluster analysis of a telecom company’s customers using ChatGPT Data Analysis
If you don’t even know where to begin, have a look at 17 types of data analysis from Datapine, explore the chart gallery in From Data To Viz, or this very extensive list of statistical techniques available in the NCSS tool.
- Read More About Data
As straightforward as it goes: learning by reading. 📖 Try to incorporate data-related reading into your life. Start with how you like to consume reading and what you are searching for based on the previous topics. Then iterate: see if the content and format are working for you. These are my considerations based on what I tried for myself, followed by recommendations.
Searching “data science” books on Goodreads
Searching the paper “Attention Is All You Need” on Connected Papers.
- Attend Data-Focused Events
I’ll keep this tip short. Webinars can be quite an effective way of learning frameworks, data tools or processes. And often, you will find very fresh insights from industry experts in all data-related things. Check our Growth Tribe Events page frequently for those, and if you follow the right people on social media, you’ll also be in the loop for other events happening. 🎤
As for conferences, they are more time-intensive. ⏱️ Some of them are fully in-person, others are fully remote, and some are hybrid. But they can be very insightful, with new product demos, discussion of new technology, a great way to meet new people from varied backgrounds interested in the same subjects, and just a ton of fun. 😁 If you can, set aside some days of the year to catch at least one or two of these.
To find them, networking (via social media) is my preferred way. Of course, you can always Google to see what is coming, but to be honest, with our busy schedules, I don’t see that happening much. If you like to plan your year for those events, check out these lists from Unite.ai and Confs.tech
- Participate in Data Challenges
Now, we are getting to the very hands-on ways of learning data skills. 💪 Data challenges and competitions can be difficult but incredibly rewarding. They are a way you can prove to yourself (and others) you have what it takes to do data work. 😌
One famous example was the Netflix Prize competition, launched all the way back in 2006. Netflix launched a competition to improve its film recommendation engine. The challenge was to develop the best collaborative filtering algorithm that would predict user ratings for films based on previous rating data. It needed to outperform Netflix’s CinematchSM algorithm at the time by at least 10%. In 2009, the successful and winning team was awarded the prize of $1M. 🤑
Netflix Prize (2006)
Those sizable prizes are rare 😞, but those kinds of competitions aren’t 😀. Kaggle is the most popular platform dedicated to them. Competitors need to solve a data science problem with the true answers hidden. They can submit predictions based on the algorithms they developed to evaluate how well they are performing. 🏆
If your goal isn’t to win competitions but just keep sharpening your skills, I recommend platforms like Leetcode or HackerRank. There are many questions and problems for you to solve at varying difficulty levels. Although their target audience consists mostly of software developers, you can also find tracks on data and AI. As you progress, your rank gets higher 🏅, which is very good for getting job interviews, and the skills you get are great for passing them.
- Build a data portfolio
If your ultimate goal is to land a job in any data career, this is one of the most important things you should do (the other is getting an education and certifications in the subject). Many employers want to know if you have what it takes to do the job, and a portfolio is a great way to demonstrate that.
Portfolio step-by-step:
GitHub is the platform to host code, files and such. You can build a static page there, too. That would be my recommendation, but another approach is creating a personal website with builders like Wix or Squarespace. They offer a lot of flexibility, and you can make them very beautiful and professional. There are some specialised solutions like DataSciencePortfol.io. A last option is to publish your projects in Medium as articles, with the accompanying code.
Yan Holtz' (creator of From Data To Viz) online portfolio
Bonus - Enrol and complete Growth Tribe data courses and modules
I couldn’t finish this piece without recommending the obvious: Growth Tribe courses and modules on data and AI. You get amazing content already curated by experts. There is a bit of everything: self-paced video content, articles to read, exercises to practice, interactivity, community with experts for feedback, asking questions and networking, certifications to add to your CV and a lot more! 🔥
Find the full editable canvas below, along with instructions on how to select your topic. 👇
Use the provided framework to decide on what ways you will be upskilling in data.
Define your goal, current and ideal role.
Based on your goal, follow the decision tree below for my recommendations on how they best fit the ways you can upskill.
Define and detail the ways you will upskill in data.
Decide when you will be pursuing what you have defined. How will you incorporate it into your life?
Mark in your calendar when you will look back to adjust and evaluate your efforts.
Here are the key points we covered:
Now is a great time to upskill in data and AI: It's becoming ubiquitous in many roles and industries.
7 different ways to do that (+ a bonus): There is enough content to be relevant and interesting for everyone, regardless of their level.
An effective canvas: Something for you to make your data upskilling more targeted and keep yourself accountable for your learnings.
I hope you are all as excited as I am for all the incredible things we will be learning in the near future!
Ciao! See you all in the next piece!