The COVID-19 pandemic has accelerated digital transformation across all industries. As such, organizations are generating data more than ever, and the need for data scientists has never been higher. According to the LinkedIn 2020 emerging jobs report, Data Scientist is the number three fastest-growing job on the market (37% annual growth), only trailing other data-intensive jobs such as robotics engineer and artificial intelligence specialist. This growth does not seem to be slowing down, with the US Bureau of Statistics reporting 11.5 million new data science jobs by 2026 alone.
In this blog post, we take a closer look at what data science is, the types of career opportunities in this space, and some best practices for breaking into data science.
What exactly is data science
In a nutshell, data science is an interdisciplinary field combining programming, statistics, artificial intelligence, and business subject matter expertise that seeks to extract meaningful insights from data. Data science is responsible for delivering some of the most valuable and visible applications in our lives. Here are a few of the use-cases that data scientists work on regularly:
- Recommendation engines: One of the most common data science applications is recommendation engines like which you see in entertainment applications such as Netflix, Spotify, or Youtube.
- Time-series forecasting: Whether stock prices, sales, or energy consumption forecasting, data scientists regularly work with and forecast time-series data.
- Supply chain management: Data scientists are reinventing supply chain management by using data to optimize supply and demand planning, improving shipping processes, and more.
- Dashboards and analytics: Data scientists help decision-makers make better decisions. A key way of doing this is by unearthing never-before-seen insights from data with the use of dashboards.
The different types of data science jobs
Data science is often used as an umbrella term for any form of data-related work. As such, there’s a spectrum of roles within this space that are all equally exciting and important. Here are the most common job titles you can find in data science and what they all mean:
- Data Scientist: Data Scientists investigate, extract, and report meaningful insights unearthed from an organization’s data. They communicate these insights to non-technical stakeholders and understand machine learning workflows and how to tie them back to business applications. Data scientists use a wide variety of technical skills to perform their roles and leverage tools like Python, R, SQL, and more.
- Data Analyst: Data Analysts are responsible for analyzing data and reporting insights from their analysis. They have a deep understanding of the data analysis workflow and report their insights through a combination of coding and non-coding tools such as Python, R, SQL, Tableau, or Power BI.
- Machine Learning Scientist: Machine Learning Scientists are responsible for developing machine learning systems at scale. They derive predictions from data using machine learning models to solve problems like customer churn and customer lifetime value. They are responsible for deploying these models for the organization to use.
Did you know? DataCamp is hiring for various roles, from data scientists, machine learning experts to directors of analytics. If you’re passionate about data science, but feel like your technical skills aren’t fully there, check out other job postings across product, marketing, and content.
How to break into data science
While there is no one-size-fits-all approach to breaking into data science, here are a few pointers that can push you in the right direction.
- Education: Most data science roles require a bachelor’s degree to break into. More importantly, it’s important to showcase that you have the necessary skills to work with programming languages, statistics, and machine learning. Options range here from traditional education to online learning providers and bootcamps.
- Showcase a portfolio of projects: One of the best ways to showcase your knowledge and skills is through a portfolio of projects. The best portfolios contain passion-driven work on datasets that genuinely excite you, not on widely known datasets like Titanic or MNIST. Check out DataCamp Workspace’s publications to see different types of portfolio projects.
- Consider getting certified: Certifications are a great way to supplement your portfolio and prove your skills. Here is a list of data science certifications you can use to prove your case.
- Get experience: A great way to convince hiring managers of your skills is by proving that you’ve already done the work. You can do this through internships, graduate schemes, temporary job placements, freelance work, and more.
- Double down on your non-technical skills: Graduates of liberal arts not only bring great soft skills to the table, but they also possess critical thinking and decision-making skills. Degrees in Economics, Psychology, Sociology, or even Journalism, all contribute to being successful in a multidisciplinary such as data science. Moreover, In the age of ethical AI, non-technical skills such as empathy, active listening, and creative thinking are huge assets.
The future of data science
Throughout this article, we’ve seen how data science is becoming more and more integral for organizations and how that’s fueling its growth. This will only intensify even more. There is reason to believe that the demand for the title “data scientist” may decrease over time, but the need for data skills will not, as data culture and upskilling are top of mind for organizations to remain competitive in the data-driven age.
If you want to be part of the data revolution, consider joining DataCamp, or start learning today and join our community of 9 million+ learners.
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