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You’re reading this because you’re thinking about joining the ranks of aspiring data scientists. And who can blame you? Data science is a growing field, even a decade after it was infamously named the “sexiest job” by Harvard Business Review. The U.S. Bureau of Labor Statistics currently predicts the employment rate of data scientists will augment by 35 percent from 2022 to 2032. Compare that to the average job growth rate of just 5 percent.
It also has other advantages:
- Well paid (BLS again) found median salary of $103k in 2022)
- It is associated with high quality of life (higher than average level of job satisfaction) According to to Career Explorer)
- Despite the recent wave of layoffs, employment is sheltered – because the position is in such high demand
There are many reasons why it is worth pursuing a job in this industry.
Source: https://www.bls.gov/ooh/math/data-scientists.html
But data science is a very broad field, with many different job titles and skill sets you need to know before you get started. This article will walk you through the different directions you can go in, and what you need to know for each one to get into data science.
To make a successful transition to career in data scienceyou will have to follow the structure:
- Rate yours Data Science Skills and identify gaps.
- Gain practical experience in areas where you are feeble.
- Network. Join data science groups, attend meetups, and participate in forums.
Let’s take a closer look.
Assess your starting position
What do you already know and how can you apply it to data science? Think about: any programming knowledge, statistical skills, or data analysis experience you have.
Then, identify gaps in your skills, especially those needed for data science. SQL is a real must, but Python or R programming, advanced statistics, machine learning, and data visualization are also incredibly useful.
Once you identify these gaps, seek out appropriate education or training to fill them. This could be through online courses, university programs, bootcamps, or self-study, with a focus on hands-on learning.
Practical experience
You shouldn’t just watch videos and read blog posts. Hands-on experience is key in data science. Get involved in projects that allow you to apply your fresh skills to real-world scenarios. These could be personal projects, contributions to open source platforms, or participating in data competitions like those on Kaggle.
If you have some basic skills, you may want to consider an internship or freelance work to gain experience in the industry.
The most essential thing, document all your projects and experiences in a portfoliohighlighting the problem-solving process you used, the techniques you used, and the impact of your work.
Network
Getting into data science often comes down to who you know, in addition to what you know. Find mentors, attend meetups, conferences, and workshops to learn about fresh trends, and get involved in online data science communities like Stack Overflow, GitHub, or Reddit. These platforms allow you to learn from others, share your knowledge, and get noticed in the data science community.
If you want become a data scientist from scratchit makes sense to think of the skills you’ll need to develop as a tree. There are “trunk” skills that are common to every data science job, and then each specialization has “branch” skills that continue to branch out into increasingly specialized roles.
There are three main skills that every data scientist should have, regardless of their field of study:
Data manipulation/data processing using SQL
Data science is basically about handling and organizing gigantic sets of data. To do that, you need to know SQL. That is this an crucial tool for data manipulation and processing.
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Supple skills
Data science doesn’t happen in a vacuum. You have to be nice to others, which means honing your gentle skills. The ability to communicate elaborate data findings in a way that’s clear and understandable to non-technical stakeholders is just as essential as your technical skills. These include effective communication, problem-solving, and business acumen.
Problem-solving helps you address elaborate data challenges, while business acumen ensures that data-driven solutions align with your organization’s goals.
Attitude of continuous learning
Data science is different than it was even five years ago. Just look at where we are today in AI compared to 2018. Novel tools, techniques, and theories are constantly emerging. Therefore, you need a mindset of continuous learning to stay up to date with the latest developments and adapt to fresh technologies and methodologies in the field.
To learn and adapt, you will need internal motivation, as well as a proactive approach to acquiring fresh knowledge and skills.
While there are common skills, as I described above, each role requires its own specific set of skills. (Remember? Branches.) For example, statistical analysis, Python/R programming skills, and data visualization are specific to more specialized data science positions.
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Let’s take a closer look at each data science role so you can determine what you need.
Business/Data Analyst
Yes, that is the role of a data scientist! Even if the skeptics disagree, I still think you can treat it as a stepping stone, at least if you want to get on the data scientist career path.
As a business analyst or data scientist, you’re responsible for connecting data with business strategy. This is an ideal fit for people who have a knack for understanding business needs and translating them into data-driven solutions.
As basic skills you will need: Business Intelligence – nothing surprising – mighty analytical skills, proficiency in data query languages, mainly SQLIn this role, Python and R are optional since the main task is data processing.
There is visualization component However, depending on the job you do, that might mean creating dashboards in Tableau or charts in Excel.
Data analytics
This role focuses on interpreting data to provide actionable insights. This is a great job for you if you enjoy translating numbers into stories and business strategies.
You will need a mighty grip statistical analysis and data visualization – though again, these could be Tableau dashboards and/or Excel charts.) You’ll also need proficiency in analytical tools as Excel, Tableau and SQL. Python/R are again optional, but keep in mind they can really aid with implementing statistics and automation.
Machine learning
Machine learning scientists develop predictive models and algorithms to make predictions or decisions based on data. These roles are suitable for people who have a mighty interest in artificial intelligence and model building.
No surprises in terms of basic skills: you’ll need deep understanding of algorithms, experience using machine learning frameworks such as TensorFlow and PyTorch, and mighty programming skillsPython and/or R are no longer optional, but required.
Data Engineering
This role requires a focus on the architecture, management, and maintenance of data pipelines. It is a good fit for people who enjoy the technical challenges of managing and optimizing the flow and storage of data.
To get this job, you will needExpertise in database management, ETL processes, and proficiency in gigantic data technologies such as Hadoop and Spark. You will also need proficiency in data flow automation using technologies such as Airflow.
Business intelligence
Business intelligence is all about creating visualizations. It’s great for storytellers and people with a mighty business sense.
You need to be a pro in dashboarding technologies like Tableau and Qlik, because those are the tools you’ll be using to create visualizations. You’ll also need data manipulation skills (read: SQL skills) to aid optimize data queries that will speed up your dashboard.
As I mentioned earlier in the article, data science is a rapidly evolving field. Novel positions and roles are opening up all the time. Going back to my tree analogy, I like to think of it as fresh branches being added to the main trunk of data science. There are now cloud engineers, SQL specialists, DevOps roles, and more—all still connected to this data science path. So this article will just give you a quick outline of where you can go with data science.
What’s more, you should also remember that data science comes with the challenges of earning a six-figure salary. There’s a steep learning curve, and the learning never stops. Novel technologies, trends, and tools come difficult and quick—and if you want to keep your job, you have to keep up.
All that said, it’s a great career option. With the three core competencies I mentioned, you’ll be well-prepared to take on any the role of a data scientist what interests you.
Nate Rosidi is a data scientist and product strategist. He is also an associate professor of analytics and the founder of StrataScratch, a platform that helps data scientists prepare for interviews with real questions from top companies. Nate writes about the latest job trends, provides interview advice, shares data science projects, and covers all things SQL.