Data science is an ever-growing field that is bound to see exponential growth as we go forward. That has not only increased the demand for data scientists but also led a lot of people to go into the field. But, that begs the question, how to become a data scientist in today’s market.
Many tend to opt for MOOCs (Massive Open Online Courses) today, but the truth is that becoming a data scientist is more than what you can learn in one course.
It’s not about learning Python, SQL, Tableau, Hadoop, or Excel; it’s about one’s ability to leverage these tools and the data available to find patterns, develop working models, and interpret data in a meaningful way.
Hence, the data scientist career requires a specific career path, qualifications, credentials, education, and most importantly, experience.
In this article, we’ll go over the field of data science and how to become a data scientist in today’s market.
Let’s dive right in.
How to Become a Data Scientist Today
In 2012, the Harvard Business Review (HBR) posted a detailed article on why the data scientist job is and will continue to be the hottest job of the century. It gave an example of LinkedIn and how they managed to leverage data to develop their platform.
Since that article was posted, the curiosity surrounding data science has only increased. More people are flocking toward big data and data science.
If you have a mathematics or statistics degree, it’s relatively easier to get into data science. The same goes for people with a computer science degree or who have learned various programming languages. They don’t need an official degree to get proficient in data science.
You can attend boot camps or go for online courses and certifications to learn the ropes of the industry at that point. In that case, it’s not that different from the data analyst career path.
However, there is still a specific set of steps you have to go through to become a data scientist, a great one at that.
1. Get an Understanding of How Data Can Be Leveraged
Data science is a lot of things, but if you were to summarize it, it would get down to this; data science helps you answer interesting and hard-to-deduce questions using complex data and code.
For example, if you’re wondering, “What’s the average marketing budget of a marketing agency in the US?” that could be answered using a lot of data. Once you utilize code and your analytical abilities to make sense of the data, you’re answering the question using data science.
However, asking such questions isn’t as easy as you might think it is. You need not only a specific mindset for it but also direction. For example, if you want to get into data science for the marketing industry, your focus has to be on that side of things.
That’s why it’s good to choose a niche or industry beforehand; preferably, something that interests you. It will help make research and analysis much easier.
Once you do, you can start to work on getting your mind trained for such instances. Start by checking out market researches, articles, and videos related to what you want to learn about. Think of a few things once you’re into it.
- The conclusions they have come to, how they did it, and how they utilized the data.
- Any more ways one could do a study using the data available.
- Additional questions you might want to answer using the available data.
Furthermore, use the data to see any patterns you can observe, develop conclusions, and use additional questions to make more assumptions and deductions.
This step is all about creating a process for yourself where you can quickly analyze and visualize data and any patterns that might turn up.
2. Develop Basic Skills and Learn Data Science Fundamentals
Setting yourself up with a process and understanding how data works would help you get into learning the basics of data science. The basics start with technical skills, and the best way to start is to learn the basics of programming in Python.
Python is the most fundamental programming language and the most commonly used. It has consistent syntax, and that makes it easier for beginners to adjust to it. However, once you become a Python expert, you can use it for advanced data science, including things like developing machine learning models, leveraging artificial intelligence, business intelligence, and deep learning.
Keep in mind that it’s about using the programming language to answer questions and interpret data. Therefore, you should be focusing on the data, not the tools that you use. You can learn the technical skills needed, but you need to develop a habit of learning the concept behind them to actually work on any data.
As a data scientist, you’ll be building projects, sharing them, and learning from them. You need technical skills to build projects, but you need that underlying understanding of the process to get your inspiration.
Therefore, when you’re looking to learn the basics, it’s best to look into different online courses and certifications. Start with a beginner course if you’re completely new to programming languages. If you’re used to it, go for some advanced courses or try to go for courses that teach you how to use programming languages with data science.
Other than that, you’ll also have to learn things like MySQL, Microsoft Excel, Hadoop, and Tableau. These help with things like data visualization, data cleaning, data collection, and more.
The process of learning these other software and programs remains the same; complete courses, get certifications, and build experience.
Data Science Fundamentals
As for the data science side of things, you’re better off going for a complete data science boot camp (or a course). You would be learning the essentials of data management, data collection, data analysis, and how to manage model data.
Furthermore, you’ll be learning how to visualize data using each tool. You should focus on building experience on specialized applications like Tableau and PowerBI.
By the time you’re done learning technical skills and data science fundamentals, you’ll be able to use and utilize Python and R. You will know how to build models with these languages, use them to analyze and predict behavior, and learn how to use data in other cases.
Some companies tend to ask for data science degrees. However, most companies tend to look over those requirements and focus on your expertise and portfolio. But, if you do want to get a data science degree, you can get one online. There are a lot of institutions that offer online data science degrees, but they are relatively expensive.
That’s why the best way is to not focus on your educational background; instead, focus on building your credentials and a portfolio. That brings us to the next point.
3. Build a Portfolio by Developing Data Science Projects
Learning coding and programming languages is one thing; putting them to use is another. While learning any programming language, you’ll most likely be doing practice exercises to get an idea of how to code, how to utilize it in a project, and more. That basic experience helps build a baseline for how you’ll be using the programming language.
However, when it comes to data science, you have to understand that the programming languages now have to be used to answer questions. Answering interesting data-based questions can be done by building custom data science projects. These projects can help you figure out how coding in data science works, how to typically answer questions using data science, and best practices.
These data science projects will be part of your portfolio; that would help others get an idea about your skills. This can especially be helpful if you don’t have a data science degree and are looking to build your career based on certifications, online courses, and work experience.
Let’s say you’re doing a data science project on the average marketing spend of Fortune 500 companies. You would have to collect relevant data and datasets for all the stakeholders, and then you would ask questions that you need to answer; and lastly, you will be using coding to find answers.
A lot of your time will be spent on data cleaning, where you’ll be sorting out the data, finding relevant patterns, and more. If you’re using machine learning, you’re better off with linear regression as it is relatively uncomplicated and easy to use.
Your first project may or may not be as successful, but keep in mind that it’s not an exact science because you can never have perfect data. And that is why consistent experience is crucial to success.
4. Develop Visualizations and Share Your Projects (And Other Work)
As a data analyst or data scientist, you’ll be part of a community that supports each other. A lot of people choose this career path because rather than extensively competing, people tend to offer their help, tips, and advice to others, especially newcomers to data science jobs.
It’s safe to say that as a newcomer, you have to showcase your data science skills and skillsets to be considered. Getting a Bachelor’s degree, Master’s degree, or any other advanced degree is the best way, but not everyone can opt for that.
Therefore, the best course of action is to not focus on your educational background and degree programs but to focus on building a reputation. Once you’ve worked on a few personal data science projects, you should share them with others. One of the best places to upload your projects is on GitHub.
In doing so, you will get an idea of how to present your project, your findings, and more. This helps establish an understanding of the data scientist job and your role in it. Meanwhile, your fellow data scientists, data engineers, and data analysts will be viewing and reviewing your projects. They will comment, provide criticism, offer advice, and more, and all of that will help you make better algorithms, improve data collection, make data mining efficient, manage unstructured data, and more.
Meanwhile, you’ll be building an online portfolio too, and you can share it with potential employers.
Other than that, a good practice is to share what you’re learning too. That means you should use your communication skills to connect with peers, share your learnings, and try to teach people who’ve come to the industry after you.
A display of your software engineering and data analytics skills can go a long way.
5. Make a Habit of Continuous Learning
No matter how experienced you are, what educational background you have, or how strong your credentials are, you still have a lot to learn. If not in your current industry and specialization, then in other data science branches.
Aspiring data scientists have a lot to learn, but even experienced data scientists have plenty to learn. The amount of data to be analyzed is only increasing exponentially, and that means there will always be unique projects, data sets, and situations to analyze.
It’s not as easy as watching tutorials, reading up on FAQs, doing meetups with fellow data scientists, or learning about related fields. You have to continuously and actively be involved in the industry and with your peers.
First of all, a good practice is to join online data science and analytics communities. For example, you can consider the following.
- Try joining subreddits related to data science.
- Join GitHub’s forums.
- Be an active member of Kaggle.
- Join SAS communities.
- Provide answers on Quora and build a following.
- Tweet regularly.
- Write blogs and articles on Medium, LinkedIn, and other platforms.
Doing all of the above would put you in contact with other data scientists, and that will give you an opportunity to learn from them.
Other than that, you can do the following to further your data science knowledge.
- Keep a lookout on massive companies like Amazon, IBM, Nielson, and more to see how they’re managing data science projects.
- Try to learn additional programming languages. For example, get into Java, HTML, and more.
- Focus on learning more of Python. For example, learn more about Spark SQL, Pandas, and more.
- Learn linear algebra, Hive, and other concepts.
This process of continuous learning will ensure that you’re at the top of your game at all times.
6. Improve Online Profile & Go Above and Beyond
At this point, you should have a well-made portfolio, several pet projects, and an online reputation. However, just like learning doesn’t stop, everything else shouldn’t stop either.
That means you should continue to communicate with your peers, take part in online communities, try to help others, and more. Meanwhile, you should continue working on more data science projects.
Considering that you’re experienced enough, you should start to look into things you are interested in. Think about all the questions you’ve had that you wish there was an answer to. This can be a great practice to answer some of your own questions.
This is also a great opportunity to document your experience, write a blog about it, and share it with others. Provide details on why you wanted to work on the project and how you collected the relevant data. Then try to present your findings through proper data visualization while explaining your process, skills, and creativity that went into it.
Telling such a story can not only help you make a greater name for yourself but can also help newcomers be motivated to work harder.
Furthermore, another good practice is to find out about high-demand topics and the questions that need answers. Offering critical insights and findings related to a high-demand topic is a surefire way of getting noticed by the big guns.
Go above and beyond by working with larger datasets to show your commitment to accurate findings. Meanwhile, make tweaks to your process to try to make your project run faster while delivering the same level of results.
An unorthodox way of quick learning is to work on projects that require expertise, knowledge, and information on things you have no experience with.
Understanding the Data Science Role Today
The six-step process above is the ideal path any aspiring data scientist should take. However, there is one more piece to the puzzle. You need to understand the data science role in your industry at any given time to pull it off properly.
That means you should make it a habit to look at job descriptions, employee testimonials, and ask your peers regarding their experiences. Learning what data science entails in your industry is a great way of understanding the expectations. Moreover, it gives you an opportunity to think about ways to improve things.
Lastly, you should always have an estimate of the average data scientist salary so you can negotiate a good salary based on your credentials.
According to Glassdoor, the average data scientist salary in the US is about $114,539. The range is between $81,000 and $162,000.
The data scientist salary will always be more than the data engineer or data analyst salary. That should be a good reminder of why data science is a step above data analytics.
Therefore, keep an eye out for the average data scientist salaries at all times, especially in your industry.
How to Become a Data Scientist and Beyond
It’s safe to say that becoming a data scientist is hard work; it takes time, lots of effort, and countless all-nighters to reach a point where you can be considered a ‘great data scientist.’
However, it’s not so much about the skills, opportunities, and intelligence. It’s about consistency. Being consistent is the best way to become a great data scientist.
The process is laid out above, but most people fail at a data science career because of a lack of consistency. Therefore, if you’re asking yourself how to become a data scientist, the answer is consistency, followed by the process laid out above.
