How to become a Data Scientist in 2022?
by Analytics Insight
March 30, 2022
Let’s dive into the skills you need to become a data scientist in 2022.
Data science offers lucrative career opportunities in our time. 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 best examples of data scientist work that we use every day in our lives! Data scientists’ algorithms help many companies generate more revenue and improve the customer experience of their products and services. For these reasons, everyone aspires to be a data scientist these days. Let’s dive into the skills you need to become a data scientist.
A solid knowledge of the basics of statistics and probability is essential to get started. Applying good statistical techniques to analyze data is an essential skill for a data scientist. The key is to understand the underlying assumptions and nuances behind these 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 needed to cement your skills for the senior data scientist job application. In this 21st century, there are a plethora of resources available on the internet for learning these rudimentary math skills. However, learning from top academics and reputable practitioners is highly recommended. There is a plethora of business problem statements available in Kaggle, so applying the correct mathematical method can be practiced and learned.
Acquiring rudimentary knowledge of basic algorithms in forecasting, classification and clustering approaches is essential. Learning about emerging artificial intelligence/machine learning techniques also helps solve new business use cases. Applying the right mathematical algorithms and fine-tuning those models based on business needs are unique skills that can only be learned through practice. Natural language understanding and computer vision algorithms are also important skills to highlight in the data scientist resume.
The raw material for generating ideas and forming complex mathematical models is business data. A data scientist should spend quality time understanding the nuances behind data collection, metadata, business context, and business glossary. Understanding data quality helps data scientists create an accurate and predictable behavioral machine learning model. The typical quote among the data science community is “Garbage in, Garbage out!” In addition to verifying data quality, data scientists should be well-versed in performing exploratory data analysis to understand the characteristics of enterprise data; This step includes outlier checking, data distribution, data sampling, etc.
Python is the language of choice for data scientists. Mastering Python programming skills is a must to get a data scientist role. Building APIs based on Python frameworks for the machine learning model is a must for the implementation. In addition to the Python language, data scientists should have a good knowledge of cloud technologies, the 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 worldwide. Even though “R” language is another popular professional programming language among statisticians, the limitation of growing “R” code production makes data enthusiasts prefer Python language. Optimizing Python code for scalable and reliable deployment can only be accomplished through practice.
Business domain skills
The data scientist must understand the business domain to gain a holistic perspective of the business problem they are trying to solve. These issues can relate to user experience, revenue generation mechanism, business hypothesis validation, sales forecasting, churn prediction, etc. Subject Matter Experts (SMEs) can provide valuable insights into the business landscape, data collection limitations, reporting, and tactical insights to data scientists. Asking good probing questions with SMEs is a great skill to develop. This helps data scientists understand “why” the business problem exists, as well as the customer impact of the proposed solution.
Data scientists must have good communication skills which are essential for articulating complex ideas in lay terms that decision makers can understand and act on. Data scientists drive organizations to become data-driven and give decision makers a science-based approach to solving business problems. The communication also helps data scientists explain the inner workings of their algorithm to help the non-technical audience understand the impact of their solution. If your company performs a lot of digital experiments such as A/B testing, providing good recommendations based on the results of the experiments would gain a business advantage over competitors. This way, data scientists achieve business results. Applying the right data visualization techniques is a great skill to acquire that can amplify their storytelling skills with data. Showing your work in a public GitHub project is the best way to get hired quickly as a data scientist. Collaborate intensively with your colleagues on the GitHub data science project, it’s to encourage recruitment agents and companies to line up to hire you!
Selvaraaju Murugesan, Data Strategist, Kovai.co
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