How to Become a Data Scientist in 2024: A Complete Guide

If you are reading this article, you may be in the same situation as I was 6 years ago. After completing my master’s degree in Computer Science, I wanted to become a data scientist or at least enter the field of Data Science. I felt a little intimidated as the prospect of mastering math, statistics, programming, etc. was a bit overwhelming. The whole learning process took 2 years, but after working in the data science field for four years, I realized there is a much better approach to getting into the field of data science and becoming a data scientist.

Data Science offers a variety of opportunities and roles in its vast field, so I began asking myself:

‘What kind of data expert do I want to become?’

‘Will it be a data analyst, a big data engineer, a visualization specialist, or maybe a data architect?’

The responsibilities and skills required to get into any data science related role vary depending on the job, it is very important to be clear about this as it helps to prioritize which skills need to be learned first.

I got my first job out of college in the year 2020 and I have experienced significant growth in the Data Science field from the year 2020 to 2024. Various industries across the globe have recognized the potential of data-driven decision-making and there have been rapid technological advancements in areas like Artificial Intelligence, Machine Learning, and Data Analytics. So, In this article, I’ll share a complete guide to become a data scientist in 2024. 

I hope this article will help you get one step closer to getting a job in data science in just a few months.

How did I Navigate my Career Path from Computer Science to Data Science?

Even after I left college, I spent another 2 years laying the foundations for my career. To be precise, I did not know what I wanted to do in the future. Since I had a Master’s degree in Computer Science, there were a plethora of job offers with different job roles, which confused me even more. To find out what I was interested in and what my professional future could be, I did a lot of research into the different perspectives of computer science. 

Initially, I came across a wide range of opportunities across various industries such as software development, cybersecurity, information technology, system administration, web development, etc. but after doing a lot of research, I realized that data science is one of the booming careers and a good fit for my skill set.

Having studied a general computer science degree, I was not very aware of the essential skills and tools required to become a data scientist. I spent a lot of time immersing myself in the various facets of programming, math, and statistical techniques. In the field of data science, it is important to create a lasting foundation that will facilitate your journey.

Why Data Science Stands out as the Prime Career in 2024?

Even if I can’t give an exact figure, I can say that the demand for data scientists will remain strong in 2024 and beyond. Data Science and AI skills are in high demand and there is no sign of slowing down. According to the Bureau of Labor Statistics, about 11.5 million data science jobs will be created by 2026. This fact tells us that data science is one of the most lucrative industries of the 21st century when compared to the IT sector. Therefore, now is most definitely the time to futureproof your career with Data Science skills. 

Can AI Replace Data Scientists in the Future?

AI and tools like ChatGPT will not replace human workers, but they will make existing data scientists much more efficient and can automate certain tasks. These tools have the potential to help with debugging code, writing base code, cleaning data, and solving many mundane tasks that previously took up half of a data scientist’s time. This increases the value that a data scientist brings to an organization and is an argument for why companies will be able to take advantage of data science and leverage it even more in the coming years.

What is Data Science: Data Science Beyond Coding & Maths

Learning about Data Science is crucial. I thought I understood it, but I was mistaken, so, just don’t assume that data science is all about coding, math, and statistics, and learn these things right away. Take time to understand these things, like a week or two, to make sure you understand specifically what data science is, why organizations are interested in data science, and how it’s used in different contexts and fields. Check How to Learn Programming from Scratch

The best way to understand all of this is to watch a few YouTube videos as they are free of cost and contain much accurate information. You can see what the day-to-day life of a data scientist looks like because due to the high demands, it’s not enough to just know how to code. You also need to know what you are doing, when you need to implement a certain type of code to achieve a certain result, and why you are doing it.

Overview of Data Science

In simple words, data science is something like being a detective of numbers. It is about collecting insightful data, analyzing the data, and understanding its patterns using Algorithms, machine learning, artificial intelligence, and other programming languages and transforming them into meaningful information. 

For example, if I were to select a player for my cricket team, I would use data science tools such as geospatial analytics to analyze player performances and game strategies, and also Matplotlib, a visualization tool, to create visual representations of player stats. This helps me recruit the right player for my team and also gives me insight into what training programs and game tactics I can teach them to improve their skills and performance.

Using Data Science to Get Ahead in Business

With the improvement of technology, companies have realized that they have a lot of information, such as current trends, customer preferences, market demand, potential competitors, and so on. So they were looking for someone to extract and harness this information using certain software and data science tools. This way, they can make really good decisions based on this important information. So companies are interested in data science because it enables them to use their data as a valuable asset.

Which is the Most Used Programming Language in Data Science and How to Master it?

Now, that you know what data science is, let’s learn more about the crucial part of this field i.e. coding. Data science is a broad field and sometimes you’ll be coding every minute of every day basically and others you only be doing that once in a while, as the role involves multiple aspects, such as understanding business problems, collecting data, optimizing models, etc., but you have to know what coding is and how to do it to some extent at least, that’s why it’s important to have a baseline language. 

So, there’s a constant debate between two programming languages i.e. Python and R. In my opinion, if I had to learn one, it would be Python, Python is the most used language across the industry and also one of the most broad programming languages. 

Use of Python in Data Science

Let’s be honest, Python is a very extensive programming language that you can’t learn straight away. So let’s get specific: “What parts of Python are we going to use as data scientists?” 

As for my learning path, I first learned the basics of programming at university, and that made it so much easier to build on. So when you’re first learning, forget about complex topics like libraries or knowing how to build models, just learn “how Python works as a programming language” first. 

Python is one of the most readable and simple programming languages, but to understand how Python works and to familiarize yourself with its features, you should try small snippets of code on IDE platforms. Your best bet is to download something like VS Code or Google Collab to explore the language.

If I were to start over, I would focus on the following topics when learning Python:

1. Data Types
2. Variable Assignments
3. Lists
4. Dictionaries
5. Tuples
6. Operators
7. If-else statements 
8. FOR loops
9. WHILE loops
10. Creating functions
11. Working with data frames
12. Loading CSVs

Now that you have ChatGPT, use it to learn how to program, and also make sure you ask it to explain what was done and why, rather than just copying and pasting stuff from ChatGPT.

Once you understand the basics, you can start with two libraries, Panda and Numpy. Panda is primarily used for dealing with data structures, while Numpy focuses on mathematical procedures and you can also start with a visualization library such as Matplotlib or Plotly that helps to create appealing visualizations. Check Best Python Courses on Udemy

Practical Steps to Master Python for Data Science

Now that you know the basics of Python, the next step is to run a project. This is one of the crucial steps. So, I suggest you do not rush through this step, start slowly and go through the project and make sure you know how to do everything because you want a strong foundation that’s going to make everything so much easier when you go forward.

To begin with, you can start with a simple project, assume that you are a marketing data scientist, and perform some basic analysis using Pandas. You could calculate summary statistics, find correlations between variables, or group data to derive insights. If you get stuck, try to use ChatGPT and double-check that what it produced is correct by putting an actual data frame and see if it works. 

How Much Time Should I Invest in Learning Technical Data Science Skills?

If I could start over, I would spend about 8-12 months learning technical skills, such as advanced Python concepts (since many data scientists like me, come from a technical background, basic Python is typically covered in graduation), mathematical and statistical techniques, and data visualization libraries like Panda, Numpy, and Matplotlib, and about 3-5 months mastering the soft skills like problem-solving, critical thinking, and domain knowledge.

However, always remember that there is no fixed timeline for mastering a particular skill. The path to get there depends on various factors such as prior knowledge, intensity of study, access to resources, etc. Data science is one of the most evolving fields and it is important to constantly familiarize yourself with new tools, techniques, and advances in data science.

Final Thoughts

With Python and basic math skills, you can apply for entry-level jobs and internships when you start. You may not get the ideal job you want, but just get the first job and once you have that, you’ll be on the career ladder and it will be easier to move on to the next one and the one after that. Make sure your portfolio looks good though, have 2-3 really good, detailed projects in your portfolio. You can put your portfolio on GitHub and also have a personal website to showcase your work and align it with the job you’re applying for.

So that’s what I would do if I could start over in 2024. But there’s no one-size-fits-all solution, you can develop a learning path that suits you better, after all, it’s about becoming a successful data scientist.