Demystifying the Career Transition to Data Science
Data Science is not a mere buzzword anymore as this field of study expanded its horizon across different industry verticals. From gathering meaningful business insights to extrapolating future trends, data science laid the foundation for various cutting-edge artificial intelligence mechanisms in the recent era. In 2012, Harvard Business Review titled “Data Scientist” as the sexiest job of the 21st century which served as a steppingstone to promote this role to a larger audience. And since then, the growing demand coupled with the hype around this job role have made it a lucrative career option for college students and software professionals. As enticing as it seems, the obvious question is how to become a data scientist? This article will help the readers to get a holistic overview of the field of data science so anyone can determine the most efficient and fastest way to transition into this role.
What is Data Science?
As the name implies, data science is all about uncovering meaningful insights (usage, trends, consumer behavior, retention, etc.) and hidden patterns of data by applying mathematical and scientific computation methods. It is an interdisciplinary field that encompasses the power of mathematics, programming skills, and data-driven business acumen. Figure 1 depicts the key prerequisites that aspiring candidates would be required to be familiar with to build the knowledge foundation for a data scientist role.
The field of data science is still relatively new even in 2022. However, we have seen a plethora of industries started utilizing the power of data science in order to revamp their business practices that drive revenue growth. The following examples illustrate some key data science use cases underpinning figure 2 below:
· Financial companies use data science to track anomalous transactions, fraud detection, and insurance scams. It can also be applied in other functions such as risk management, customer analytics, and algorithmic trading.
· E-commerce giants Amazon and Shopify use customer’s demographic data to improve their overall shopping experience and provide better product recommendation to their app users.
· Uber and Tesla are breaking the barriers by manufacturing state-of-the-art autonomous cars that may change the paradigm of the automobile industry in the near future. Although we are still at a very early stage of building fool-proof driver-less cars, doing so wouldn’t be possible without the help of data science.
· Netflix and other streaming platforms tend to build highly efficient recommender engines using data science algorithms that help companies to determine a list of movies/tv series that users are most likely to watch.
· The health care sector has benefited greatly by the advent of various data science driven technologies in the areas of medical imaging, genomics research, drug discovery, and disease prevention.
· Data science techniques are changing the paradigm of the energy sector by influencing innovative A.I. practices such as detecting grid failure, identifying energy consumption and savings to manage power outages, forecasting energy demands, and setting pricing accordingly.
Data Scientist Guidebook
Before you start thinking about choosing this career path, you need to understand what it takes to become a data scientist. Given the wide range of skillsets data scientists possess, there seems to be some confusion around their responsibilities in the broader spectrum.
Are they statisticians, super expert data analysts, or software engineers?
To avoid this confusion Josh Wills, a former head of data engineering at Slack, once said that, “A data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
The aforementioned statement puts the competency of a data scientist in perspective. They are usually responsible for executing the following tasks:
· Identifying and encapsulating potential data analytics problems that can have an adverse impact on a company’s growth strategy.
· Collecting, cleansing, transforming, and processing the structured and unstructured data from different sources. In most cases, data scientists partner with data engineers to carry out similar tasks and extrapolate data?
· Building statistical models and machine learning algorithms to extrapolate projections based on historical processed data.
· Interpreting the data models to identify patterns and communicate the discoveries to various stakeholders in a comprehensible way. Storytelling is one of the most important skills a data scientist must have.
It is their solemn duty to advise the business’ management to use a data-driven decision-making process instead of focusing solely on making ad hoc decisions.
Career Transition from Technical Analyst to Data Scientist
If you are an information technology analyst thinking of getting into the world of AI and machine learning, then data science would be your first transit point. Technology analysts have vast knowledge in improving and maintaining an enterprise’s information technology system. Their analytical mindset coupled with knowledge of databases could easily serve as a steppingstone to their career move to data science. However, the skill set of a data scientist goes beyond problem-solving aptitude as it comprises modular expertise in many fields like data mining, statistics and mathematics, machine learning, data visualization, and business acumen. Therefore, the following descriptions provide a holistic overview of all the major skills that a technology analyst must acquire to become a data scientist and how to obtain them.
- Mathematics (Probability, Statistics, Linear Algebra)
Mathematics is the core foundation of data science because extracting business insights from big data and transforming them into data products requires an ability to view data patterns or textures through a mathematical mindset. A data scientist does often leverage statistics in order to summarize the characteristics of a data set or test a hypothesis for making data-driven business decisions. Therefore, as a rule of thumb, it is a must know skill for data scientists regardless of the industry space.
For example, AI-driven autonomous cars perform real time predictions to identify and locate objects during real-time driving phases. The basic building block of their prediction mechanism relies upon probability and statistics. Probability helps the AI system in finding out the likelihood of a tree or traffic signal or other tangible object based on its physical appearance.
It is also very important to have a good understanding of linear algebra, which is one of the most predominant functions of machine learning algorithms. A data scientist often deals with matrix multiplications to form the basis of advanced machine learning units called neural networks.
2. Programming and storytelling
For building proof of concept solutions or integrating complex data systems, a data scientist must know how to code. It is a very important skill set that helps in cleaning and organizing unstructured data before diving into the model development phase. The most important programming languages and tools that a tech analyst must know or learn to excel in this field are R, Python, SAS, SQL and NoSQL.
Several job trends show that the demand for data scientists that know Python, as opposed to those who know R, has increased significantly in the past few years. R was the language of choice on Kaggle, the famous data-driven competition platform, but Python emerged as a superior programming language when it came to the numbers of kernels written. The R versus Python debate is still ongoing though we can clearly notice a huge shift in data scientist’s programming language of choice since 2017. (See Figure 3)
There is no better way to tell a story than visualize it. As mentioned in the previous section, storytelling is one of the most key aspects of data science, especially in the exploratory phase. From building compelling dashboards to propagating analysis stories for various stakeholders, data scientists need to master the craft of simplifying the message for the mass. Usually, technology analysts are not assigned tasks related to building storytelling dashboards. However, their primitive knowledge about data visualization and BI tools can easily be leveraged to build narrative dashboards that align with business context. Building data-driven dashboards is the easiest way to contribute to data science work and it takes less effort for a technical analyst to learn the respective toolkits.
3. Data Skills
The effectiveness of data science practices relies heavily upon how efficiently data are managed throughout the model development process. An aspiring data scientist must understand the foundation of data modelling and data architecture before diving into the analytics part. According to the general industry practices, data scientists often collaborate with data engineers, technology professionals whose primary job is to prepare data for analytical or operational use, to ensure a continuous supply of clean data from source databases.
However, in most cases data scientists need to perform additional data cleanup to revalidate the data integrity and address data issues such as missing data, data redundancy, and incorrect data attribution. Thus, having a clear understanding of the data management pipeline helps data scientists to alleviate common data quality pitfalls and pass clean data to machine learning models. The technical analyst role often requires the ability to work with data professionals in order to carry out data management duties. Thus, it would not be a steep learning curve for technical analysts to enhance their previously acquired data skills in order to match the respective job criteria of a data scientist.
Conclusion
Being a data scientist, you need to continuously push the limit of your learning capacity as this field is evolving rapidly to accelerate the progress of artificial intelligence evolution. If you thrive for career growth and want to board a lifelong learning ship, then data science could be one of your interests. Given the intricacies of data science, you will be constantly challenged to learn a new technology or a mathematical algorithm to grow in this field, which might be overwhelming sometimes, but you will never run out of innovative project ideas. You might suffer from imposter syndrome while working in this field, but always remember that it is okay to not know everything about data science.
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