Data science is a great profession—but it can also be a lonely one. For many newly qualified data scientists, the first few years out of college are a real challenge. Without the camaraderie and friendship provided by your peers, it can quickly begin to feel isolating.
However, data science is actually an inherently social profession. Part of a data scientist’s role is to work alongside internal and external stakeholders to deliver key insights; outside of that, many data scientists like to stay connected to their peers working in similar roles as a way to discover new ideas and methodologies.
One of the best ways data scientists network with each other is through data science communities. In this post, we’ll run through the most popular data science communities and show you how you can get involved.
1. Kaggle
Technically, Kaggle is a coding tool rather than a community per se, but the Google-owned software is so popular among data scientists that it now boasts one of the largest data science communities in the world.
Kaggle has mainly been built as a tool to evaluate and assemble teams across competitions. It allows you to find and publish data sets, explore and build models in a web-based environment, and share your work with other scientists and engineers. You can even enter competitions directly from the software, which offers over 50,000 public datasets and 400,000 public notebooks to address even the most complex challenges.
Another extremely useful feature of Kaggle is that it provides free access to NVidia K80 GPUs in kernels. This benchmark shows that enabling a GPU to your Kernel results in a 12.5X speedup during the training of a deep learning model. There are loads of great resources on how to start using the Kaggle in this way, including this guide.
Best of all, the Kaggle community now has more than 3 million active members who can use Kaggle to share their work and expertise.
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2. IBM Data Science Community
It might look a bit dated in 2020, but the IBM Data Science Community web pages are one of the best sources around for expert-level insight into today’s pressing data science challenges. This community has been around for far longer than many of the others on this list, and that means that some of the legends of the industry can be found blogging, podcasting, and even answering direct questions.
In short, the IBM Data Science Community is a great place to visit if you are seeking specific guidance from an industry pro. It’s a little less newbie-friendly than some of the other communities here, but that’s not necessarily a bad thing.
3. Reddit
Reddit might seem like a strange choice if you are looking for a professional network, being better known for sharing memes than addressing complex scientific questions. However, in some cases, it’s actually better to use social networks, rather than their professional ones, because you can communicate more freely with your peers.
There are plenty of resources for data scientists on a number of key subreddits, such as r/datascience, r/dataisbeautiful, and r/MachineLearning. r/dataisbeautiful is a largely visual subreddit; r/datascience is more introductory; and r/machinelearning is more for hardcore discussion of papers. The advantage of discussing your problems on an anonymous network is that your employer will never know what you actually think of their new project.
4. Open Data Science
Open Data Science is a community organized around particular, high-level projects. This site aims to act as a bridge between people working in all the different sub-areas of data science—not just engineers and scientists—but developers and students. Members of the community can suggest and create projects, which are as diverse as the members of the community, and then invite others to work collaboratively toward a solution.
5. Data Science Central
Data Science Central is arguably the largest data science community out there, at least in terms of the raw number of contributors it attracts. The bar to entry to post is low, which means content quality varies, as nearly anybody can post. This is the place to go if you are looking to get on top of the latest trends in the industry or to hear about new jobs before they are officially announced.
Data Science Central is based around a forum system but also includes an editorial platform for experts to share their knowledge via personal blogs. There is also a pretty extensive suite of social interaction tools to make connecting with peers easy.
6. Data Community DC
Data Community DC (DC2 for short) is run on a slightly different model from the other communities on this list. Instead of being a purely volunteer-run affair, DC2 is a non-profit, based in Washington DC, that aims to promote and progress the work of data scientists in the US.
To this end, DC2 offers a range of services to contributors. The core aim of the community is to encourage education in data science. To this end, it has put established professionals in touch with local schools, where they participate in events that are designed to inspire the next generation of data scientists. For those outside the capital, the community also has a lot to offer, though: DC2 has six meetup groups with over 5000 unique members, a board of 12 people, a blog, occasional workshops, and plans for bigger events in the future
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7. Stack Exchange
Stack Exchange often gets forgotten about by data scientists, who tend to see it as a place for developers to discuss the intricacies of new web frameworks. In reality, however, the community is now so big, and so diverse, that it covers an enormous range of tech subjects, data science included.
In addition, the fact of the matter is that in 2020 most scientists are also coders. And if you have a question about coding, need someone to show you how to achieve something, or even want to hire a developer to build some software for you, Stack Exchange should be your first port of call.
8. Data Science Society
The Data Science Society is an initiative of graduate students at Berkeley and is one of the most exciting data science communities around at the moment. This community has taken the lead in encouraging minorities and women to train in data science. This is the place to go if you are looking to find people who share your values and politics, while also sharing the technical and scientific skills needed to make a difference.
9. Driven Data
Driven Data is focused on using data science to create a better world. This community focuses on putting a number of key stakeholders in touch, and not just data scientists and coders, but also activists and lawyers, all of whom are working toward socially progressive goals.
It’s never been more important to be part of the data science community, whether this is to generate new project ideas, improve your skills, or simply to stay sane in these strange times.
This post was written by Brian Skewes.
Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.
In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you.
Is Data Science A Good Career?
Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.
10 Data Science Careers To Consider
Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.
Data Scientist
Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.
They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies.
General Requirements
A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills.
Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.
Average Salary
The average salary of a data scientist in the US is $156,363 per year.
Data Analyst
A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.
A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.
General Requirements
To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills.
For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.
Average Salary
The average base salary of a data analyst is $76,892 per year.
Business Analyst
Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation.
A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.
General Requirements
Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.
Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.
Average Salary
A business analyst can earn an average of $84,435 per year.
Database Administrator
The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools.
A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.
General Requirements
Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server.
Average Salary
Database administrators earn an average salary of $77,391 annually.
Data Engineer
Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).
General Requirements
Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently.
Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.
Average Salary
Data engineers earn a whooping average salary of $125,180 per year.
Database Architect
A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure.
Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.
General Requirements
If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java.
If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.
Average Salary
Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.
Machine Learning Engineer
A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions.
General Requirements
As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.
Average Salary
Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.
Quantitative Analyst
Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions.
They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.
General Requirements
This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background.
Average Salary
A quantitative analyst earns an average of $173,307 per year.
Data Mining Specialist
A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.
General Requirements
If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field.
Average Salary
Data mining specialists earn $109,023 per year.
Data Visualisation Engineer
Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context.
General Requirements
Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.
Average Salary
The average annual pay of a data visualization engineer is $103,031.
Resources To Find Data Science Jobs
The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.
Job Boards
When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity.
Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.
Online Communities
Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.
Network And LinkedIn
Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.
FAQs about Data Science Careers
We answer your most frequently asked questions.
Do I Need A Degree For Data Science?
A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.
Does Data Science Need Coding?
Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.
Is Data Science A Lot Of Math?
It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.
What Skills Do You Need To Land an Entry-Level Data Science Position?
To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.
Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!
