The Data Science career roadmap

Every organization, in fact, every industry – from healthcare to education and retail – requires the talents of data scientists. Organizations need to stay competitive in the age of Big Data, building and executing data science skills quickly, or risking being left behind.

Yes, for every data scientist reading this, you should know – you’re in demand!

Whether you’re just starting out as a data scientist or looking to progress to a senior-level role, we’ve got the lowdown on growing your career in the world of data science.

Why data science?

Data scientists use their programming, analytical, and statistical skills to collect, analyze, and interpret data. These insights help them to develop data-driven solutions that can be applied to various business demands. Data scientists should have many additional technical skills, from reporting technologies to machine learning, database creation, knowledge of programming languages, and machine and statics learning.

“Data science is a 21st-century job skill that everybody should have,” says Eric Van Dusen, curriculum coordinator for data science education at the University of California (UC), Berkeley. “Every field. I tell students, you all need to come out with this set of skills. You’ll be a lot more powerful in whatever career you go into.”

You’re in demand

Data science has long been described as ‘the sexiest job of the 21st century.’ And now, the demand for data science professionals is increasing. In fact, the U.S. Bureau of Labor Statistics (BLS) estimates that demand will increase by 22% by 2030. Therefore, technologists interested in a long-lasting career should consider data science as their chosen profession. Governments and businesses have spent recent years collecting and mining huge amounts of data, with data becoming the backbone of many industries. As a result, data scientists are relied on to organize and analyze this data, so companies worldwide can make better decisions to ensure efficiency and fuel business growth.

Building your career

There are many reasons to choose data science as a career:

• High salaries.  
• Continuous career growth and demand for skills.
• Increasing career opportunities as the industry develops.  
• Exciting projects and challenging work.
• The chance to change the future of technology.
• Prestigious/respected role.

You don’t even need a bachelor’s degree or master’s degree to begin your career journey – you simply need the right skills, experience, and the ambition to learn and grow.

Skills

The world’s most exciting organization’s are seeking the talents of data scientists. From Google to GitHub and InVision, companies worldwide are looking to upgrade their data science and machine learning capabilities. But, with increasing demand, we’re seeing a shortage in data scientists with the right skills.

But what skills do data scientists need?

Programming/coding languages

Mastering programming languages such as Java, Python, and Golang can benefit any aspiring data scientists. The importance of coding languages in the data science profession can’t be underestimated.  Carlos Melendez, COO and Co-Founder told Forbes that:  “Every student, regardless of their occupation, will need to be data-literate to succeed in a world where data will increasingly be king.” A data scientist uses mathematical and statistical techniques to manipulate, analyze and extract information from data. To perform their tasks, data scientists rely on the power of computers. Programming is the technique that allows data scientists to interact with and send instructions to computers. As one of the world’s most popular programming languages, Python is an essential language you should master. Also, consider swotting up on other coding languages, including SQL, Go, C++ and R.

Machine Learning

Machine Learning lies at the heart of data science. You’ll need to hone your knowledge of various types of algorithms, particularly how they work on supplied datasets. It’s also essential to understand how to evaluate the effectiveness of algorithms and even which algorithms to use and when.

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the world of big data, data visualization tools and technologies are essential for analyzing massive amounts of information and making data-driven decisions. Data Scientists are required to create effective and impactful graphs/charts from insight data that convey the pattern by themselves. There are various paid and free tools available to assist and plenty of online tutorials to help you master them. Have a look at PowerBI, Tableau, and QlikSense in particular. Open-source Python libraries such as Matplotlib and Seaborn are also useful.  

Data Analysis

Data professionals need an experimental mindset, allowing them to explore different methods of manipulating available data and helping you to find ways to extract the most tangible information from it. To extract insights successfully, you need to have a good understanding of data pre-processing operations, including SQL, which is an essential requirement of the data science journey.  

Deep Learning

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. To be a good data scientist, you need to learn and understand the complex concepts of deep learning.  

Big Data

Big data refers to larger, more complex data sets, derived from new data sources. The size and variability of data has changed a lot over the past decade. One of a data scientists’ primary concerns is efficiently capturing, storing, extracting, processing, and analyzing information from these enormous data sets, so it’s essential to have a background in and understanding of big data.

Soft skills

Soft skills, including collaboration, communication, and critical thinking, are amongst the most important skills a data scientist needs to progress in their career. Having empathy for your colleagues, being able to successfully participate in teamwork, and possessing a business mindset to successfully problem solve can elevate your projects – and your career. The ability to understand user behaviors and use common sense to apply solutions to any challenges are vital.

Nice to haves

These aren’t necessarily essential skills but will go a long way in enabling you to build a career in data science:

• Deep love for data – and a wealth of understanding of data.
• Analytical mindset.
• Experience with programming languages.  
• Strong maths and statics skills.
• Business background/understanding of business operations.
• Passion for learning
• Excellent communication and collaboration skills.

Data Scientist career path

So you have the skills – but what can you do with them? There are, thankfully, an increasing number of opportunities available to data science professionals across the industry.

Data science is a broad field that features various paths and career options within it.

Senior Data Scientist

The Senior Data Scientist oversees the activities of the junior data scientists and provides advanced expertise on statistical and mathematical concepts for the broader Data and Analytics department. The Senior Data Scientist applies and inspires the adoption of advanced data science and analytics across the business. Senior data scientists also build machine learning or deep learning models for prediction, finding patterns and trends in data, visualizing data, and even pitching in with marketing strategies.

Senior Data Analyst

The Senior Data Analyst brings advanced analysis, modeling, and performance measurement on projects across their teams. Clear on the purpose of data and how to capture, manipulate, interpret and apply it; they demonstrate the value and impact of this sophisticated science to inform client behaviors and activities.

Data Manager

Data Managers  are responsible for building and managing systems around data as per the specifications of Data Architects. A data manager develops and governs data-oriented systems designed to meet the needs of an organization or research team. Data Management includes accessing, validating, and storing data that is needed for research and day-to-day business operations. Data Managers work hard to ensure that information flows timely and securely to and from the organization and within.

Data Architect

A Data Architect is responsible for defining the policies, procedures, models, and technologies to be used in collecting, organizing, storing, and accessing company information. Data architects create a blueprint for all the data management systems. The company’s every system and infrastructure related to data needs to be built and maintained by identifying all possible structural and installation solutions. Data architects are responsible for ensuring their company’s data solutions are built for performance and scalability and also for designing analytics for multiple platforms.

Big Data Developer

Big data is yet another important technology in the arena of data science. Big data developers are responsible for the actual coding or programming of Hadoop applications: quite similar to a Software Developer. They could work on trillions of bytes of data each day with the help of different programming languages like Java, C++, Ruby, etc., along with several databases. This field largely deals with managing hundreds and thousands of petabytes of data in a secured and easy-to-access manner. Big data developers are technically savvy individuals with heavy knowledge of computer architecture.  

Director of Data Science

This is a leadership role in the field of Data Science. The Director of Data Science leads the entire data science team, and heads up the department’s engagement with clients. They also partner with these clients to enhance the existing data management methodologies and develop new approaches and methodologies.

Additional roles include Data Engineer, Machine Learning Scientist, Business Intelligence Developer, Marketing Analyst, Clinical Data Manager, and Statistician.

The future of Data Science

Once you’ve defined you’re career goals – whether you want to become a Senior Data Engineer or a Director of Data Science – you may want to investigate potential industries that interest you.

Several industries are noticing a growing demand for data science professionals, including:

Healthcare: 30% of the world’s data is created by the healthcare industry, which is expected to increase to 36% by 2025. Often information can be siloed, with systems unable to share data easily, meaning healthcare professionals can find it difficult to access vital details during their patient’s care. This is an ongoing challenge for hospitals and clinics – but one that data scientists can help solve. This can be done by culling data from various sources (electronic health records, genomics, imaging, etc.) and analyzing it, thereby providing clinicians with insights that will enable them to personalize care.

FinTech: Much of the FinTech industry involves interpreting real-time data and forecasting future trends or market events. Artificial Intelligence and Machine Learning are becoming increasingly important for the success of those processes, and Data Scientists utilize those tools to analyze and manage risk, leading to better decision-making and greater profitability.

Transportation: AVs – automated vehicles – are advanced forms of artificial intelligence, requiring vast amounts of data to function – data that can be analyzed and managed by data science professionals. If this AV technology achieves its full potential, it can lead to fewer car accidents and much safer roads.

Supply chain management: The global supply chain was already undergoing a digital transformation before the pandemic hit, but the outbreak of COVID-19 accelerated the trend, increasing the need for AI, robotics and blockchain. Data scientists use predictive analytics to ensure the supply chain is more efficient and agile, by anticipating demand, advising where inventory should be positioned proactively to avoid items showing incorrectly as out of stock, determining the optimal network of manufacturers and storage facilities, and developing optimized routes for transporting inventory.

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We hope this data science career roadmap offers you some useful tips and advice. If you’re interested in growing your career in other areas of technology, check out our other career roadmaps:

The UX Designer career roadmap

The complete Ruby career roadmap

The Senior ReactJS Developer Roadmap

If you’re a Data Scientist looking to hone your skills, check out Andela’s Learning Community.

If you’re a data professional seeking new career opportunities, find out what Andela can do for you!

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If you found this blog useful, check out our other blog posts for more essential insights!