The demand for data scientists is growing day by day in numerous sectors such as healthcare, education, retail, and consumer goods. In today’s age of ‘Big Data’, it has become necessary for companies to stay ahead in the competition by building and using data science expertise at a rapid pace or run the risk of falling behind.
It is crucial for every data scientist reading this to realize that their skills are highly sought after.
Irrespective of your current position in the data science career – whether you are just starting or have gained considerable experience – we have the insider knowledge on how to enhance your career.
How can data science be advantageous to us?
Data scientists are equipped with the skills to collect, evaluate, and decipher data through multiple computational, analytical, and statistical techniques. With their expertise, they can create solutions powered by data that can cater to different organizational goals. On top of this, data scientists require several technical proficiencies such as knowledge of reporting tools, machine learning, programming languages, statistical and machine learning, and database architecture.
Eric Van Dusen, the Curriculum Coordinator for Data Science Education at the University of California (UC) Berkeley, emphasizes that data science is a crucial skill of the 21st century that one must possess. He encourages his students to graduate with these competencies, as it will allow them to have more impact in their chosen area of work.
To state it easily, you are needed.
Data Science has gained popularity and is often termed as the ‘most appealing job of the 21st century’, and it’s easy to understand why. The Bureau of Labor Statistics projects a 22% rise in demand for skilled data scientists by 2030, making it an alluring career choice for IT professionals. Data has become a fundamental part of many sectors and companies alike, with years of data being collected and scrutinized for insights. Hence, industries worldwide require data scientists to gather, evaluate and interpret such information to make informed decisions that can amplify productivity and optimize profits.
Development of Career
Data science is a field that is expanding quickly, and for several valid reasons:
- The salary is quite lucrative.
- There are abundant prospects in one’s domain, and the requirement for one’s proficiency never diminishes.
- Employment prospects are multiplying as the field keeps growing.
- Work that is challenging yet stimulating.
- The opportunity to influence technological progression in the future.
- A position of high regard or prestige.
To have a flourishing career in data science, you don’t necessarily need an undergraduate or postgraduate degree. What you require is a suitable set of skills and a resolute spirit to enhance them.
Skills
Organizations across the globe are seeking skilled data scientists to reinforce their data science and machine learning capabilities. From Google and Microsoft to GitHub and InVision, companies of all magnitudes require this proficiencies. However, the number of competent experts is relatively limited.
Nonetheless, what are the essential skills that data scientists should possess?
Programming and coding languages
To pursue a career in Data Science, it is crucial to develop skills in at least one programming language, such as Java, Python or Golang. Programming languages hold significant importance in the field of Data Science. As per Carlos Melendez, COO, and Co-Founder, in his interview with Forbes, “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 specialize in data manipulation through mathematical and statistical approaches. Data Science is rooted in information technology, and consequently, data scientists must acquire programming skills. Python is a highly popular language and an excellent language for beginners to get started with. It is beneficial to acquire knowledge in multiple programming languages, including R, C++, SQL, and Go.
Machine learning
Machine Learning is the backbone of Data Science. To become proficient in Data Science, one should gain a comprehensive understanding of different algorithms and their execution on datasets. It is equally important to be familiar with algorithm evaluation techniques and the appropriate selection of algorithms.
Data visualization
Data visualization is a powerful technique for comprehending massive datasets, enabling users to quickly recognize trends, outliers and patterns. Expert data scientists skillfully present raw data as aesthetically appealing charts and graphs that accurately convey the data trends. Commercial and free tools are available, and online courses can assist in comprehending the functioning of these tools. Popular BI (Business Intelligence) tools are PowerBI, Tableau and QlikSense, while open-source Python libraries like Matplotlib and Seaborn tend to be highly beneficial.
Data analysis
Data professionals should adopt an experimental approach to data handling that enables them to experiment with different data manipulation and analysis techniques. Meaningful insights can only be extracted by experts possessing a comprehensive understanding of data-processing techniques, such as SQL.
Deep learning
Deep learning is an influential machine learning approach that combines artificial neural networks with representation learning. It can be used for various learning methods, such as supervised, semi-supervised, and unsupervised learning. A profound knowledge of the principles of deep learning is crucial for individuals aspiring to become successful data scientists.
Big Data
Big Data is a terminology used for vast and multifaceted datasets originating from previously unexplored sources. Over the past ten years, the enormity and variety of data have seen a significant upsurge. It is imperative for data scientists to be able to capture, store, access, process and analyze information from such vast datasets effectively. Therefore, possessing a sound comprehension of Big Data is fundamental.
Intangible skills
To achieve success, data scientists must have a gamut of indispensable skills, including the adeptness to work collaboratively, exercise critical thinking and solve complex problems. A profound insight into business dynamics, user behavior, and the aptitude to apply common sense to overcome challenges could significantly boost project success and even augment career prospects.
Additional options
Although not imperative, the following proficiencies can significantly benefit individuals pursuing a career in data science:
- A fervent liking for data, bolstered by a profound comprehension of it.
- Analytical thinking
- Proficiency in computer programming languages.
- Fluency in mathematics and statistics.
- Familiarity with or experience in the corporate world.
- An ardent curiosity for knowledge
- Excellent interpersonal skills and a penchant for teamwork.
Data Science as a Career
It is heartening to observe that the demand for data scientists presently surpasses the available skilled workforce. With your possession of the necessary expertise and proficiencies, this provides an ideal occasion to contemplate how to optimize them.
Data Science is a vast field with numerous potential specializations and career paths.
Lead Data Scientist
As a Senior Data Scientist, your role encompasses supervising the work of junior Data Scientists and offering the Data and Analytics team with expert guidance in statistical and mathematical concepts. Your responisbilities entail fostering creativity and novel approaches for data science and analytics, rendering you an invaluable asset to the organisation. In addition to employing techniques such as machine learning and deep learning for generating forecasts and designing marketing strategies, you will also be responsible for detecting trends and patterns in data.
Premier Data Analyst
The Senior Data Analyst adds to the accomplishment of team initiatives by leveraging sophisticated analysis, modelling and performance assessment techniques. They elucidate the significance of data and illustrate how to acquire, modify, evaluate and employ it efficaciously, with the objective of favourably influencing client behaviours and actions.
Data Manager
Data Managers are accountable for building and managing data-centric systems, using the guidelines specified by Data Architects. This encompasses identifying, validating and preserving data to meet the requirements of a company or research unit. Data Management is vital for both research and business endeavours, and Data Managers put in substantial time and effort to ensure a steady stream of dependable data, both internal and external to the organisation.
Data Architecture Specialist
Data Architects are liable for formulating the policies for gathering, preserving, retrieving and analysing data in an organisation. They establish an architecture which elucidates the strategies for storing, accessing and utilizing data across the organisation. For this purpose, they must recognise the probable structural and deployment alternatives available to the organisation and construct analytics that are compatible with various platforms. They also guarantee the operational efficiency and scalability of the company’s data solutions.
Data Scientist/Big Data Developer
The advent of Big Data has brought about a significant new tool for the realm of Data Science. Big Data Developers, similar to Software Developers, are assigned the task of writing code for Hadoop applications. By employing programming languages such as Java, C++, Ruby, and a variety of databases, they are capable of processing billions of bytes of data each day. The primary objective of this field involves secure and streamlined handling of mammoth data sets, which frequently measure in the hundreds of petabytes. Developers operating in this domain typically possess a profound knowledge of computer systems.
Data Scientist Management
The position of Director of Data Science belongs to the upper echelons of the rapidly growing field of Data Science. This role entails overseeing customer relationships within their department, in addition to directing and supervising the entire data science team. Moreover, they partner with clients in order to fine-tune and enhance existing data management methodologies.
Other roles include: Data Engineer, Machine Learning Scientist, Business Intelligence Developer, Marketing Analyst, Clinical Data Manager, and Statistician.
Views on the Bright Future of Data Science
Explore potential areas of interest once you have defined your career goals, such as Senior Data Engineer or Director of Data Science.
The demand for data scientists is on the rise across a multitude of industries, which include:
Healthcare: Presently, the healthcare industry accounts for 30% of all global data, a statistic that is projected to rise to 36% by 2025. Unfortunately, if data is kept in disparate systems and not readily exchangeable, healthcare professionals face difficulty accessing crucial data when treating patients. This has been a longstanding issue for healthcare providers, however, data scientists possess the means to find a resolution. By aggregating and examining data from various sources (such as electronic health records, genetics, imaging, etc.), it is plausible to achieve this objective and equip healthcare professionals with the insights they require to deliver patient-focused, customised care.
FinTech: Financial Technology is progressively emphasizing analysis of real-time data to generate predictions. The significance of Data Scientists in evaluating and regulating risk has become more pronounced due to the emergence of Artificial Intelligence and Machine Learning, which have the potential to contribute to enhanced decision-making and augmented profitability.
Transportation: Automated Vehicles (AVs) employ advanced Artificial Intelligence (AI) to operate effectively, which necessitates amassing voluminous quantities of data. Data Science professionals are imperative in evaluating and administering this data. If these AVs fulfill their potential, they stand to considerably diminish traffic accidents.
Logistics Planning: The digital transformation of the global supply chain was already underway prior to the pandemic, but the advent of COVID-19 has expedited this process and amplified the necessity of technologies such as Artificial Intelligence (AI), Robotics (R), and Blockchain. Data scientists utilize predictive analytics to enhance the agility and responsiveness of the supply chain, forecasting demand, providing pre-emptive guidance on inventory placement to avoid items from appearing as out of stock, identifying the most efficient combination of production facilities, warehouses, and delivery vehicles, and more.
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This Career Pathfinder for Data Science ought to be beneficial. If you wish to venture into other areas of technology, do peruse our other career guides.
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If you are a Data Scientist seeking to enhance your knowledge and skills, peruse Works’s Learning Community.
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