Your 2024 (and beyond) data to-do list

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.
The data field is very vast and deep, and ever-expanding. So everyone can find something interesting and useful there, regardless of their current skill level or role. Data skills are immensely valuable, from “soft” skills like analytical thinking and data-informed decision-making to hard skills like machine learning and intelligent automation.

But that presents a challenge in itself: Where do I start? How do I keep learning? 🤔 

Here are some ways you can tackle that! 👇

7 ways to upskill in data for 2024 (and beyond)!

- 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:

• AI and Data Strategy: Vin Vashishta and Bernard Marr
• Statistics: Adrian Olszewski and Joshua Starmer
• Data Engineering: Zach Wilson and Joe Reis
• Data Science: Cassie Kozyrkov, Ravit Jain and Daliana Liu
• Data Analytics: Danny Ma, Alex Freberg and David Langer
• Data Viz & Storytelling: Cole Knaflic, Kate Strachnyi and Brent Dykes
• Data Governance: George Firican
  

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/analytics
• /r/statistics
• /r/datascience
• /r/machinelearning
• /r/learnmachinelearning
• /r/artificial
  
  

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 👇:

• Spreadsheet: Airtable*, Excel, Google Sheets. For a data science or analyst, pick one (likely Excel) and get good at it. 🔢
• Data visualization (dashboarding): Tableau*, PowerBI, Looker. Being great at one of these is essential for a data analyst. 📊
• Data science: Dataiku*, KNIME, Alteryx, Orange Data Mining. For the data scientists that want to quickly prototype or those that want to do data science without (or with little) coding involved. 👩‍💻
• Languages: Python*, R, SQL (no official page because there are many flavours). If you want to pursue a serious career in data (data analyst, scientist or engineer), SQL is non-negotiable. You have to learn it. Then you pick one between Python (general purpose, popular, “easy” to learn, great for machine learning and data engineering) or R (data-focused, very robust for statistical analysis and visualization) to complement it. 🧑‍💻
• GenAI: GPT*, Bard, Github Copilot and the like. They are not “data tools” per se, but these generative text models can help with problem-solving and coding. ChatGPT (Plus the paid version) has a Data Analyst bot that can write and execute code to perform data analysis, for example. 🧠
  
  * Newer, more powerful/versatile tools and/or my personal favourites.

- 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:

1. Don’t try to learn everything. It can't be done and it will be surface level. I’d recommend going one at a time here.
2. Find a problem you have that you believe data would help.
3. Use your network and forums to ask what data techniques can be used to solve it.
4. Pick one that is often mentioned/highly voted on and search about it on the internet to confirm that it is fit for the problem.
5. Give it a go! Use AI tools like ChatGPT or Copilot to help you implement it.
   
6. Study it deeply so you can use it with confidence.

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.

• Newsletters 📰: bite-sized to keep track of what’s going on. Great to read early in the morning with a cup of coffee ☕. 
  • TL/DR: all things tech, startup and AI. And you can decide on more specific topics. It's my personal favourite, and it’s free!
  • Data Science Weekly: great if you are serious about data science. There are videos, guides, jobs, etc., but it’s a paid subscription.
  • The Rundown AI: focused on AI development. Very high quality. Free!
• Books 📚: good for techniques and deep dives (e.g. textbooks), but also for more strategic, business and societal considerations of data work. It can be very good for implementation as well. Some of my recommendations:
  • How To Make The World Add Up, Algorithms to Live By, Naked Statistics, Weapons of Math Destruction, Storytelling With Data, Data Science for Business… and the O’Reilly library for implementation.
  • Audiobooks are great, too, but not for implementation (learning techniques, tools or coding).

Searching “data science” books on Goodreads

• Papers 📄: for details of cutting-edge research, go for academic papers.
  • Google Scholar is my search engine of choice.
  • Connected Papers is an interesting visual graph of related papers. 
  • If you really want to get into machine learning research, Papers With Code has, well, machine learning papers with code (and datasets, methods and tables).

Searching the paper “Attention Is All You Need” on Connected Papers.

• Articles 📝: many of the experts I mentioned before have great articles on their field. They range from light and superficial to very technical. 
  • Medium has a lot of content in this space, and you can search for tags (AI, Machine Learning, Data Science). I think their subscription is very worth it, but watch out that there is no “editing”, so make sure you trust the author (or double-check what they are saying).
    • Towards Data Science, Towards AI and Analytics Vidhya are three focused publications on Medium worth subscribing.
  • KDNuggets and Data Science Central are two other very important platforms/communities in this space.
    

- 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:

• Think about the role you are aiming for, and do projects for that. Specialisation beats generalisation here. Data analysts should focus on insights and visualisations, data scientists should focus on modelling and machine learning, and so on… 🎯
• Use real data. You can find datasets on Kaggle, on the /datasets subreddit, or on government websites. Bonus points if the data aligns with the industry you want to work for (e.g. marketing data for marketing analytics). 🏢
• Try to cover many skills important for that role in your projects, like data collection, cleaning, modelling, etc. 🧰
• Ensure the projects have a clear goal and state that to the audience. The goal should guide your decisions, and it will be used to evaluate your solution. 🧑‍💼
• Don’t forget to demonstrate your reasoning! Having a bunch of code that runs or a final pretty dashboard is not enough… walk the reader through what is happening and why you made the decisions you made. Make it accessible to non-technical audiences, too. 🤔
• Quality beats quantity. A few incredible projects (or even one) are worth a lot more than dozens of so-so projects. Put your best work in a place of prestige! ☝️
• Finally, showcase your work. Add the link to your CV and online presence. Reach out to your network for feedback and validation. 📢

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. 👇

Click here to download a copy

Let's practice!

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.

Wrapping Things Up

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!