Tips To Thrive as a Data Scientist
According to the Future of Jobs report by WEF, Data Analysts and Scientists and AI and Machine Learning Specialists will be in the leading positions in growing demand by 2025. As data becomes more valuable to businesses around the world, skilled data scientists who can interpret and extract meaning from it are highly sought after.
Skill shortage of these professionals has already been acute, and business leaders consistently cite difficulties when hiring Data Analysts and Scientists and AI and Machine Learning Specialists. As long as businesses rely on data for making informed decisions and planning effectively, there will be a need for Data Scientists.
Therefore, there is a great opportunity for anyone who is willing to join the thriving job market of Data Science, which will see a 22% growth by 2030 (U.S. Bureau of Labor Statistics).
Nowadays, some refer to data scientists as unicorns in the sense that they need to master a diverse skill set that is rarely found in a single individual. Which begs the question, would it be enough to go through an online course to gain job-ready skills? Most likely if your goal is to find an entry-level data analyst role. But if you want to not only build a career in data science but also advance your career in said field, you need a robust, well-rounded strategy that encompasses aspects like networking, finding a company that values data science, and more...
At AI EdgeLabs, when faced with the challenge of hiring data scientists to develop our disruptive cybersecurity product, we discovered that finding the needed talent is easier said than done, which is probably a direct reflection of the lack of strategy to advance the data science career path. Jobs are there with plenty of opportunities to choose from, but how do you go about advancing your career in data science to the next level?
Here are some of the best tips we’ve put together to define a successful Data Science strategy.
Choose the right company
Data science professionals like data analysts can lean into a data science or data system developer role depending on where they deepen their expertise. By expanding knowledge in artificial intelligence, statistics, data management, and big data analytics, a data analyst can transition into a data scientist role.
By building on existing technical skills in Python, relational databases, and machine learning, a data analyst can become a data system developer. Additionally, data science professionals need to pay close attention to the place data science is given within a company as it will set the tone for how successful a career is possible.
A company like AI EdgeLabs where Data Science is at the core of a product’s development will give you the chance to work with a variety of data sources, project scenarios, and machine learning tools.
Leverage the newest technologies
Data science is experiencing rapid change. Anyone who wants to advance their career in data science should always keep up with the most recent developments in tools and technology, and leverage them effectively. It is important to realize that there are several ways to analyze data, and companies are looking for data scientists who can offer know-how of new technologies that will be beneficial to the organization.
In the context of rapid change and ongoing evolution in data science, new methods emerge constantly. For example, an innovative deep learning approach known as reinforcement learning is widely used for different domains, not only in the gaming industry, but also for building autonomous cyber security agents, robots, autonomous vehicles, creating new nanomaterials, medicines, and more.
Usually, product companies like our own where we built our product, AI EdgeLabs, from the ground up, have more advantages in respect to working with cutting-edge technologies as there are little restrictions about which technologies to work with as compared to outsourcing companies that are usually working with existing or outdated solutions of their clients.
Also, we embrace new methods and approaches, such as semi- and self-supervised learning, reinforcement learning, graph neural networks, all while combining different approaches for high-value metrics for our models giving us a key competitive advantage to try out new technologies that are just emerging.
These are the latest achievements in data science and the approaches that are used for building general AI, which we recommend knowing if you are a data scientist keen on staying on top of the latest trends:
- Multi-domain Modeling (like DALL·E 2)
- Super intelligent and general, multi-domain models (like DeepMind)
- Large language models (like GPT-3)
Nurture your skills
If you're interested in becoming a Data Science expert, keep expanding your knowledge and skills with online courses, boot camps, certifications or other post-graduate learning opportunities. The variety of development opportunities is huge, so you only need to choose the right one for you.
Data analysts are generalists, which means they get to work in different teams and roles. They enjoy working on clearly defined, structured problems. They use data to extract and produce reports that are valuable to a business. Successful data analysts generally enjoy some level of complexity, but not as much as data scientists.
Data scientists love complexity. They enjoy answering questions that are broad and amorphous. They thrive on project-based assignments and get excited about delivering insights. Data scientists are less likely to work on a wide variety of assignments in comparison to data analysts.
From our very own pool of talent, we’ve seen firsthand that data engineers are very technical. They organize and give structure to raw data for the data scientists and data analysts to execute their work. Our data engineers enjoy building data pipelines and software development. They have an advanced understanding of programming languages such as Rust, Java, Python, or SQL which goes on to prove
Specific to the data science field, there are a number of technical skills that are helpful to have before diving in, including:
Deepen your statistical and probability analysis
As a data scientist, you will be tasked with extracting knowledge and insights from data to make educated decisions leveraging methods, algorithms, or systems. Making conclusions, estimating, and forecasting are all crucial to your work. Check out this helpful piece by NYU.
Take your Machine learning skills to the next level
Expanding your machine learning know-how automatically broadens the doors of opportunity as a data scientist, giving you the tools and skills to automatically analyze and investigate vast amounts of data. When applied correctly, you will be able to automate the data analysis process and generate real-time forecasts without the need for human intervention, so we suggest that you constantly grow your knowledge with courses about machine learning.
Dive deep with deep learning
You’ll be surprised to learn that you will be using deep learning and deep reinforcement learning frequently in your job as a data scientist, especially as you set out for your products to mimic how people acquire knowledge. Some modern-day applications of deep learning can be found in everyday systems like Siri, Cortana, or our cybersecurity platform AI EdgeLabs which is embedded with the best deep reinforcement learning has to offer.
Boost your data visualization know-how
The graphical display of information and data is a critical skill to have as a data scientist as you will be in charge of observing and comprehending trends, outliers, and patterns in data by employing visual components like charts, graphs, and maps. After all, you’ll find out that it’s a whole lot easier to observe data patterns when all of the data is presented out in front of you in a visual format rather than data in a table.
Grow your math skills
We’d be remiss not to mention mathematics as the foundation of modern data science disciplines. You’ll apply them every day as almost all current data science approaches, including machine learning, have a strong mathematical foundation. From experience in building AI EdgeLabs, we’ve found that linear algebra, calculus, and statistics are key to data science.
Advance your programming
Our data scientists answer real-world data science challenges by using core programming concepts, computational thinking, and data analysis approaches that strengthen the AI EdgeLabs platform. We find that Python is one of the most prominent coding languages required in data science professions across all types of seniority, however other programming languages such as Rust, Go, SQL, and Java are also very valuable to possess in your skills toolbelt.
Grow familiar with Python, Rust, Scala, Hadoop, and Spark
Already throughout this piece, we’ve made several references to some of the most sought-after resources for data scientists including Python. Other noteworthy tools include Hadoop, Spark which have become invaluable tools for those in the data science field.
Next up is Hadoop. Using straightforward programming techniques, the open-source software framework Hadoop enables the processing of massive data volumes across computer clusters. Hadoop is made to expand from a single server to thousands of devices. Hadoop is particularly important for data scientists because of:
- Its ability to store and process large amounts of all types of data.
- Hadoop’s distributed computing model processes that allow it to compute big data fast.
- The protection it offers against hardware failures.
- Its flexibility to not have to preprocess data before storing it.
- It's an open-source, free framework model.
- Its scalability to grow a system by adding more nodes with little administration required.
Tools like Apache Spark are bound to become indispensable to Data Scientists and will quickly become the industry standard for performing big data analytics and solving challenging business problems due to the massive explosion of big data and the exponentially increasing speed of computational power. Spark is a parallel data processing framework and set of libraries. Because it keeps data in-memory (RAM) rather than on disk, it is substantially quicker than Hadoop for analytic workloads.
Python is a high-level, open-source, interpreted programming language that offers an excellent approach to object-oriented programming. It is one of the most popular languages used by data scientists for a variety of projects and applications. Data scientists frequently use Python because of its built-in mathematical libraries and methods that make it easy to solve arithmetic problems and perform data analysis.
How to develop these skills? Here are some helpful resources:
- You can check out some of the top Hadoop courses here.
- You can read this introduction to Apache Spark from AWS here.
- You can enroll in this Python for Data Science course here.
Choose to work with an experienced team
Working alongside experienced and ready-to-share professionals can be invaluable. When we first set out to strengthen our data science team, it was challenging to find the right fit in terms of experience and skills. Of course, it’s abundantly clear that the best and most hands-on knowledge is gained by actually working on diverse data science projects, which is why our team has skyrocketed skill-wise. If we can leave you with one piece of advice about working with an experienced team like ours is to always be curious, open to learn, and work hard.
If you are:
- A junior, don’t feel shy to ask for support, raise your questions, and request resources for further development. Talk to people about their work and see what you can learn from them.
- A mid-level data scientist, be open about expressing your opinions and ideas, be accountable, dare to influence the product, and be proactive about suggesting which technologies to use. Check with your manager or the human resources department to learn what kind of mentorship programs may already be available.
- A senior, always work toward developing your team, taking a mentee under your wing to advance their career, becoming a member of an industry association, and participating in events to share your knowledge and build a personal brand.
Also, a data scientist should also be a great team player and have the following non-technical or soft skills:
- Critical thinking
- Analytical thinking
- Strong communication skills
- Data intuition and curiosity
- Problem solving
- Effective written and verbal communication
- Knowledge of the industry in which you plan to work
Of course, these technical and soft skills are only a few examples of the abilities a data scientist must possess.
To handle the increase in corporate use cases, data science teams must expand beyond the core data science staff to include business stakeholders, data engineers, application developers, and more. In the next five years, these extended data science teams will surpass software development teams in size, predicts research company, Forrester. Everyone on the team plays a crucial part in realizing a shared analytic and artificial intelligence vision, which calls for efficient teamwork.
Develop your network
Use conferences and events to meet more people in the data science community and establish connections. Recently, AI EdgeLabs has participated in a number of events like Hannover Messe and the European Business Angels Network congress where we’ve had the opportunity to talk to data scientists in other companies as well as network with like-minded folks looking to grow their community. We recommend that you regularly check out meetups where other data science enthusiasts get together to present, work, and share their projects. It is extremely valuable to ask questions and get answers or advice from industry experts from other companies.
It's crucial to have industry peers you can turn to for support and assistance at any point of your career. A peer group may help you stay motivated, overcome obstacles, and avoid the same mistakes that others have made. Finding like-minded people might be challenging if you're new to the field, so make time to look for gatherings and activities that are pertinent to your line of work.
By Serhiy Protsenko, Lead Data Science at Scalarr/AI EdgeLabs