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Data Science offers lucrative career opportunities in this day and age. Data scientists produce actionable business insights using data and implement mathematical algorithms to solve complex business problems. In fact, Amazon product recommendations, Netflix movie suggestions, Google Maps traffic predictions are some of the prime examples of data scientist work that we use every day in our lives! Data scientists' algorithms are helping many companies generate more revenue and enhance the customer experience of their products and services. Owing to these reasons, everybody aspires to be a data scientist these days. Let's dive into what skillsets you need to hold to become a data scientist.
Strong knowledge of the fundamentals of statistics and probability is essential to get started. Applying the right statistical techniques to analyse data is a critical skill set for a data scientist. The key element is understanding the underlying assumptions and nuances behind those statistical techniques! Without this rudimentary knowledge, a data scientist will struggle to produce meaningful and accurate insights from underlying data. Advanced mathematical concepts in linear algebra, causal analysis, optimization, probability, set theory, graphs are required to cement your skillset for the application of the senior data scientist's position. In this 21st century, there are a plethora of resources available on the internet to acquire these rudimentary mathematical skills. However, learning from top scholars and reputed practitioners is highly recommended. There are a plethora of business problem statements available in Kaggle such that applying the right mathematical method can be practiced and learned.
Acquiring rudimentary knowledge about basic algorithms in forecasting, classification, and clustering approaches is vital. Learning about emerging Artificial Intelligence / Machine Learning techniques also aids in solving newer business use-cases. Applying the right mathematical algorithms and fine-tuning those models based on business requirements are unique skills that can be attained only by practice. Natural Language Understanding and computer vision algorithms are also important skillsets to be showcased in the data scientist resume.
The raw material for generating insights and training complex mathematical models is business data. A data scientist needs to spend quality time in understanding nuances behind data collection, metadata, business context, and business glossary. Understanding data quality helps data scientist to build an accurate and predictable behaviour machine learning model. The typical quote among data scientist community is "Garbage in, Garbage out!" In addition to checking data quality, data scientists should be well versed in performing exploratory data analysis to understand the characteristics of the business data; This step includes checking for outliers, data distribution, data sampling, and so on.
Python is the language of choice of data scientists. Mastering Python programming skills is a must to get a data scientist role. Creating APIs based on Python frameworks for machine learning model is indispensable for implementation. In addition to Python language, Data scientists need to have sound knowledge of cloud technologies, Spark computing framework, and Jupyter notebooks. Data scientists can learn from the Python community in StackOverflow and the hackerrank platform provides good opportunities to showcase your Python skills around the globe. Even though "R" language is another popular pro programming language among statisticians, the limitation of production rising "R" code makes data nerds prefer the Python language. Optimising the Python code for scalable and reliable deployment can be accomplished only by practice.
Data scientist needs to understand the business domain for getting a holistic perspective of the business problem they are trying to solve. These problems could be about user experience, revenue generation mechanism, validating a business hypothesis, forecasting sales, churn prediction, and so on. The Subject Matter Experts (SMEs) can provide valuable information about the business landscape, data collection limitations, reporting, and tactical insights to data scientist. Asking good probing questions with SMEs is a great skill to develop. This helps data scientist to understand "why" the business problem exists and also the customer impact of the proposed solution.
Data scientists must possess good communication skills which are crucial for articulating complex insights into laymen's terms that decision-makers can comprehend and act on. Data scientists are driving organisations to be become data-driven and empower decision-makers with a scientific approach to solving business problems. The communication also helps data scientists to explain the internal working of their algorithm to help the non-technical audience understand the impact of their solution. If your company is doing a lot of digital experiments such as A/B testing, providing good recommendations based on the experiment results would gain a business advantage over their competitors. In this way, data scientists are achieving business outcomes. Applying the right data visualisation techniques is a great talent to acquire that can amplify their storytelling skill with data. Showcasing your work in a public GitHub project is the best way to get hired as a data scientist quickly. Collaborate extensively with your colleagues on the GitHub data science project is lure recruitment agents and companies to stand in line to hire you!
Selvaraaju Murugesan, Data Strategist, Kovai.co
