How I Would Learn Data Science in 2024

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In 2019, I took on a Data Science Bootcamp course, and honestly, I didn’t know much about the tech field. The only thing that older professionals constantly advised me was to look into data science or software engineering. To be honest, software engineering seemed very daunting so I opted for data science instead.

I got my first data science job in the year 2020 and it was so fun and exciting at the same time. Fast forward to the year 2024, with the rise of generative AI – I sit here and wonder how good I had it. I entered a less competitive industry – a time when you were not offered massive paychecks and the ability to work wherever you wanted.

Now, companies are running against one another to remain competitive and they are throwing money away to ensure they can hire data scientists that can bring the company value. Not only are you competing with 10,000 other people who want that fully remote job and spicy salary – but you are also competing with generative AI and its ability to do your job for you at a slither of cost.

Sounds scary right?

If you are looking to enter the data science world, you are probably scratching your head and wondering if it’s even worth it anymore. How do I overcome these challenges?

In this blog, I will go through a roadmap for learning data science in the year 2024.

What Skills Does a Data Scientist Need?

Before I dive into the roadmap of how to become a data scientist, let’s first address the skills you need.

Hard Skills

These are the following technical skills you will need to become a successful data scientist:

• Python
• R
• Statistics and math
• SQL and NoSQL
• Data visualization
• Machine learning
• Deep learning
• Natural language processing
• Big data
• Cloud computing

Soft Skills

These are the soft skills, also known as human skills that you will need to become a successful data scientist.

• Problem solving
• Critical thinking
• Communication
• Storytelling
• Business acumen
• Teamwork

Data Science Roadmap

Programming Fundamentals

The start of your data science journey is learning the fundamentals of programming. Learning programming is probably the most daunting part of your data science journey because this is where you enter a new world, learn a new language and remember that you have to continue to learn for everything else to make sense.

However, if you don’t get this part down-packed – you are setting yourself up for failure.

Here is a link to a course that I highly recommend: Learn to Program: The Fundamentals.

Data Wrangling

The whole reason you’re interested in becoming a data scientist is because you have some interest in the value of data. You will spend all your time trying to clean data, figure out what it’s trying to tell you and how you can use these insights to make some data-driven business decisions.

Data wrangling is the process of transforming and structuring data from one raw form into a desired format. Therefore, you will need to learn how to load your data, sort, merge, reshape, and group it. You will also need to learn about the different elements of data, for example, strings, etc.

This part of your data science journey consists of a lot of practice. The more you practice, the easier it will get for you.

Here is a link to a course that I highly recommend: HarvardX: Data Science: Wrangling.

Data Visualisations

Once you have learnt how to clean the data and transform it into your desired format, – the next step is to visualize the data to fit your hypothesis or argue your hypothesis.

This part of your journey does not consist of weeks or months to learn, but it is important to help you communicate your insights to stakeholders. Taking your insights and creating visualizations is part of the data science journey which allows you to show your creative side.

With a little bit of practice and trial and error, you can learn this within a week.

Here is a link to a course that I highly recommend: IBM: Visualizing Data with Python.

Maths, Probability, Statistics

People underestimate the power of actually understanding data science through math. A lot of courses there leave the element of math and statistics out of their data science course but these are the foundations of what makes data science. Therefore, the best thing you can do for your career is learn it!

You will need to learn about linear algebra, numerical analysis, descriptive statistics, confidence intervals, t-tests, Chi-square, and more. These topics will help you during your analysis phase and will make or break your journey to proving your hypothesis correct – therefore you want to be able to do it correctly. The best way to master this is by practising using different datasets that you can analyze.

My recommendation would be to take the following course series which dives into linear algebra, calculus, probability and statistics: Mathematics for Machine Learning and Data Science Specialization.

Machine Learning

The two above courses both dive into math, probability and statistics for machine learning and data science which is a good transition for the next phase of your data science journey – machine learning.

In your data science career, you’re going to want to uncover complex patterns and the different relationships in your large dataset. However, statistical analysis may not always be your best option and you will need to leverage machine learning algorithms. Not only will you be able to uncover these insights in a shorter period, but they will also be accurate predictions that you can use down the line during your decision-making process.

Your journey to learn machine learning will include type 1/2 error, train-test split, AUC ROC, confusion matrix, cross-validation, and more. All of these topics will help you in your model selection decision.

Here is a link to a specialized course that I highly recommend: Machine Learning Specialization.

Deep Learning

There’s more learning to do – nobody said it would be an easy path. We are now moving onto deep learning – a subset of machine learning that is used to train computers to perform human-like tasks.

We already know that AI is transforming all industries at the moment and for you to excel as a data scientist you need to understand how they are exactly doing that. Learning about deep learning is the answer.

You will need to learn about deep neural networks, how they are built and trained, as well as identifying architecture parameters and how you can apply your knowledge of deep learning to your applications. Using the best practices and strategies will help you to become a deep learning expert as a data scientist.

Here is a link to a specialized course that I highly recommend: Deep Learning Specialization.

Generative AI

Although it may seem like there is already a lot of content to learn as it is, the above will help keep you competitive in the market when it comes to competing against other people around the world.

The other challenge you need to overcome as a data scientist in the year 2024 is how to remain competitive with the rise of generative AI. If you’re thinking you need to learn elements of data science that generative AI tools such as ChatGPT can’t do – stop thinking that immediately. Rather than seeing it as competition, find ways that you can leverage generative AI tools to enhance your data science career.

Use it to your advantage and learn about it. For example, learn about PandasAI – rather than seeing it as a threat to you landing your dream job, learn about it and add it to your resume and skill set of tools you can utilize to show your future employer.

Wrapping up

I hope this blog has shown you how to manoeuvre your data science journey in a time when competition is not only high from other data scientists but also generative AI tools. If you are an experienced data scientist and have any advice, drop it in the comments below!

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.

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