Data scientists are increasingly in demand across a wide range of industries, from healthcare and education to retail and consumer goods. In our era of ‘Big Data’, organizations must stay ahead of the competition by rapidly establishing and leveraging data science capabilities, or risk being left behind.
Every data scientist who happens to be reading this should take note: your skills are in high demand.
No matter where you are in your data science career – from beginner to seasoned veteran – we have the inside scoop on how to advance your career.
In what ways might we benefit from data science?
Data scientists are trained to gather, analyze, and interpret information using a variety of computational, analytic, and statistical methods. This expertise enables them to develop data-driven solutions that can address a range of corporate objectives. Additionally, they must possess various technical skills, such as familiarity with reporting technologies, machine learning, database building, programming language comprehension, and machine and statistical learning.
Eric Van Dusen, the Curriculum Coordinator for Data Science Education at the University of California (UC) Berkeley, believes that data science is an essential 21st century skill that everyone should possess. He stresses to his students that they should graduate with these capabilities, as this will enable them to be more influential in whichever field they choose to work in.
To put it simply, you’re wanted.
Data Science has been dubbed the ‘sexiest job of the 21st century’, and it is no surprise why. Demand for skilled data scientists is projected to increase by 22% by 2030, according to the Bureau of Labor Statistics, making it a very attractive career option for computer professionals. Data has become integral to many industries and organizations, with years of data being gathered and mined for insights. As such, businesses around the world need data scientists to collect, analyze and interpret this data in order to make more informed decisions that will increase productivity and maximize profits.
The field of data science is rapidly growing, and for many good reasons:
- Pay is quite good.
- Increased opportunities in one’s field and the need for one’s expertise never cease.
- Possibilities for employment are growing as the field expands.
- Work that is both interesting and difficult.
- The potential to impact technological development going forward.
- Position of honour or esteem.
A postgraduate or undergraduate qualification is not essential to embark on a successful career; what is required is the right set of skills and a determination to progress.
Companies worldwide are searching for experienced data scientists to bolster their data science and machine learning capabilities. From Google and Microsoft to GitHub and InVision, organizations of all sizes are in need of this expertise; however, the pool of qualified professionals is relatively small.
Nonetheless, what abilities do data scientists need?
Languages for programming and coding
It is essential for those wishing to enter the field of Data Science to become proficient in at least one programming language, such as Java, Python, or Golang. The importance of programming languages within the field of Data Science cannot be overstated. During an interview with Forbes, Carlos Melendez, COO and Co-Founder, stated that “Every student, regardless of their vocation, will need to be data-literate to survive in a world where data will increasingly be king.” Data Scientists are experts in the manipulation of data through the use of mathematical and statistical methods. Information technology is a core component of data science and, as such, data scientists must learn to code. Python is an especially popular language and is highly recommended as a starting point. It is also beneficial to learn more than one programming language, such as SQL, Go, C++, and R.
Data science is fundamentally rooted in Machine Learning. It is essential to gain a comprehensive insight into algorithms and how they operate when presented with datasets. It is also vital to be familiar with methods of algorithm evaluation and the correct selection of algorithms.
Visualization of Data
Data visualization is a powerful tool for making sense of large datasets, allowing users to quickly identify patterns, anomalies and trends. Data scientists are responsible for transforming raw data into visually appealing graphs and charts that accurately display the underlying trends. There are various commercial and free tools available, as well as online courses to assist in understanding how to use them. Popular BI tools include PowerBI, Tableau and QlikSense, while Matplotlib and Seaborn are two open-source Python libraries that can be of great use.
Analysis of Data
Data professionals should take an experimental approach to working with data, which allows them to experiment with different ways of manipulating and analyzing the available data. It is essential to have a good understanding of data pre-processing techniques, such as SQL, in order to extract meaningful insights.
Deep learning is a powerful machine learning technique which combines artificial neural networks with representation learning. It can be used with a variety of learning methods, such as supervised, semi-supervised and unsupervised learning. To become a successful data scientist, it is essential to have a deep understanding of the principles of deep learning.
Big data is a term used to refer to large and complex datasets sourced from previously unexplored sources. Over the past decade, there has been a significant surge in both the extent and diversity of data. It is essential for data scientists to be able to effectively capture, store, access, process and analyze information from these vast datasets, thus having an understanding of big data is essential.
Skills that are more intangible
Data scientists must possess a range of essential skills to be successful, including the ability to work collaboratively, think critically and solve problems. Having a sound understanding of business dynamics, user behavior and the ability to apply common sense to challenges can make a significant difference to the success of projects and could even enhance career prospects.
Although not strictly necessary, the following abilities can help you immensely if you pursue a career in data science:
- An intense fondness for data, backed up by a depth of knowledge about it.
- Thinking analytically
- Familiarity with computer programming languages.
- Knowledgeable in mathematics and statics.
- Knowledge of or experience in the business world.
- Intense curiosity for knowledge
- Superb ability to interact with others and work as a team.
Data Scientist as a Profession
It is encouraging to note that the demand for data scientists currently exceeds the supply of skilled professionals. As you possess the requisite knowledge and abilities, this presents an opportune moment to consider how best to utilize them.
Data science is an expansive discipline with many possible specializations and career outcomes.
Chief Data Scientist
As a Senior Data Scientist, you will be responsible for overseeing the work of junior Data Scientists and providing the Data and Analytics team with sound expertise in statistical and mathematical principles. You will be expected to motivate and introduce innovative data science and analytics strategies, making you a vital part of the company. In addition to utilizing machine learning and deep learning for forecasting, data visualization and even marketing strategies, you will also be responsible for identifying patterns and trends within data.
Top-Level Data Analyst
The Senior Data Analyst contributes to the success of projects across teams by utilizing advanced analysis, modelling and performance evaluation. They articulate the importance of data and demonstrate how to effectively gather, modify, evaluate and utilize it, with the aim of having a positive impact on client behaviors and activities.
Manager of Data
Data Managers are responsible for constructing and supervising data-centric systems, based on the blueprints provided by Data Architects. This involves identifying, verifying and storing data to meet the demands of a business or research group. Data Management is crucial for both research and corporate activities, and Data Managers dedicate significant time and effort to ensure a continuous supply of reliable data both within and outside of the organization.
Professional in Data Architecture
Data Architects are responsible for defining the strategies for gathering, storing, retrieving and analyzing data within an organization. They create an architecture which outlines the strategies for storing, accessing and utilizing data throughout the organization. To do this they must identify the potential structural and installation options available to the organization and build analytics that can be used across multiple platforms. They also ensure the performance and scalability of the organisation’s data solutions.
Data Scientist/Big-Data Developer
The field of Data Science has recently seen the introduction of a vital new tool: Big Data. Big Data Developers, similar to Software Developers, are responsible for writing the code for Hadoop applications. By utilizing programming languages such as Java, C++, Ruby, etc. and a range of databases, they are able to process billions of bytes of data on a daily basis. The primary focus of this area is the secure and efficient management of enormous data sets, which often measure in the hundreds of petabytes. Developers working within this field often have a deep understanding of computer systems.
Managing Data Scientists
This is a senior-level role in the rapidly expanding field of Data Science. The Director of Data Science is responsible for managing customer relations within the department, as well as leading and monitoring the work of the entire data science team. Furthermore, they collaborate with customers to refine and improve existing data management practices.
Some more functions include Data Engineer, Machine Learning Scientist, Business Intelligence Developer, Marketing Analyst, Clinical Data Manager, and Statistician.
Perspectives on Data Science’s Promising Future
Research prospective sectors of interest after you have identified your career objectives, such as Senior Data Engineer or Director of Data Science.
There is an increasing need for data scientists in a variety of sectors, including:
Healthcare: The healthcare industry currently accounts for 30% of all global data, with this figure expected to rise to 36% by 2025. Unfortunately, if data is stored in disparate systems and is unable to be easily exchanged, it can be difficult for medical professionals to access crucial data when treating patients. This is a long-standing issue for healthcare providers, however, data scientists have the resources to find a solution. By gathering and analyzing data from multiple sources (electronic health records, genetics, imaging, etc.) it is possible to reach this goal and provide physicians with the insights to deliver patient-centric, personalized care.
FinTech: The use of financial technology is increasingly focusing on the analysis of real-time data to produce predictions. The role of Data Scientists in assessing and managing risk has become increasingly important due to the emergence of Artificial Intelligence and Machine Learning, which can lead to improved decision-making and increased profitability.
Transportation: Automated Vehicles (AVs) utilize advanced Artificial Intelligence (AI) to function correctly, and require vast amounts of data to do so. Data Science professionals are needed to assess and manage this data. Should these AVs reach their potential, it has the potential to drastically reduce traffic collisions.
Logistics planning: The global supply chain had already started its digital transformation before the pandemic, but the emergence of COVID-19 has accelerated this process and increased the need for technologies like Artificial Intelligence (AI), Robotics (R) and Blockchain. Data scientists are using predictive analytics to make the supply chain more agile and responsive by predicting demand, offering proactive advice on where inventory should be located to avoid items showing as out of stock, identifying the most efficient combination of production facilities, warehouses and delivery vehicles, etc.
This Data Science Career Pathfinder should be a useful resource. If you’re interested in exploring other areas of technology, please take a look at our other career guides.
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