The need to extract meaningful information — and business value — from data assets is becoming increasingly critical as firms’ data assets rise. Analyzing data and extracting insights from it necessitates a different set of skills than merely storing and managing it. Many companies are increasingly discovering that they want skilled analytics specialists with expertise in scientific procedures, statistical approaches, data analysis, and other data-centric strategies — or, to put it another way, data science.
The important talents that prospective data scientists and organizations wishing to hire them require to accomplish their jobs effectively include a variety of technical capabilities. Soft skills, on the other hand, are personality traits and characteristics that can assist data scientists achieve their goals and bridging the gap between company executives and workers in technology and data analysis. Let’s take a closer look at these critical data science abilities in both areas.
Data Science Technical Skills
Data scientists must have a number of “hard skills” that require specific training and education in order to ask the proper questions, construct good analytical models, and successfully assess the findings. Data scientists often require the following eight technical abilities.
It should come as no surprise that data scientists need a strong understanding of statistics because they use statistical concepts and procedures on a daily basis. Data scientists can collect, organize, analyze, interpret, and present data more effectively if they are knowledgeable with statistical analysis, distribution curves, probability, standard deviation, variance, and other statistical concepts. This makes it easier for them to work with the data and come up with effective results. Above all, in the era of data understanding the difference between data analytics and statistics is crucial.
2. Multivariable Calculus and Linear Algebra
It’s critical to be able to apply mathematical principles to comprehend and optimize the fitting functions that match a model to a data collection. The model will not be able to provide accurate predictions if this is not done. Data scientists should also be familiar with the use of dimensionality reduction to ease complex analysis challenges involving high-dimensional data. Skills in calculus and algebra are also required in machine learning, such as when training an artificial neural network on vast amounts of data.
3. Programming and Coding
Many data scientists are forced to learn to program. They aren’t normally coding masters and don’t have a computer science degree, but they are familiar with the fundamentals of programming and writing code. By far the most popular programming language among data scientists is Python. More than 80% of the 2,675 respondents who described themselves as working data scientists in a 2022 survey conducted by Google’s Kaggle subsidiary, which runs an online data science community, claimed they use Python. SQL was second on the list, with a little over 40% usage. R is another widely used language for data science projects and applications, including statistical computation and graphics. C and C++, Java, and Julia are some of the other programming languages used by data scientists.
4. Predictive Modeling
Data science is defined by the ability to use data to create predictions and model various scenarios and outcomes. Predictive analytics searches for patterns in current or new data sets to predict future events, behavior, and outcomes, and it may be used in a variety of industries, including customer analytics, equipment maintenance, and medical diagnostics. Predictive modeling is a highly regarded talent for data scientists because of its many applications and benefits.
5. Machine learning and deep learning
While data scientists aren’t required to work with AI technologies, organizations are increasingly hiring them to develop machine learning applications. Someone who can train machine learning algorithms to learn about data sets and then seek patterns, anomalies, or insights that may be utilized to develop analytical models is required to do so. As a result, the demand for data scientists competent in machine learning’s supervised, unsupervised, and reinforcement learning approaches is increasing. Deep learning skills, a more advanced way of creating complex analytical models using neural networks, can help data scientists stand out. Knowledge of many sorts of algorithms, such as the following, is also beneficial:
- decision trees;
- random forests;
- Naïve Bayes classifiers;
- k-nearest neighbor;
- logistic regression;
- linear regression; and
- k-means clustering.
6. Data Wrangling and Preparation
Data scientists frequently claim that wrangling and preparing data for analysis takes up more than 80% of their time on data science initiatives. While data engineers are responsible for the majority of data preparation, data scientists can benefit from being able to perform basic data profiling, cleansing, and modeling tasks. This gives them the ability to cope with data quality issues and flaws in data sets, such as missing or mislabeled fields and formatting errors. Data wrangling abilities also include gathering data from many sources and manipulating various data types, as well as filtering, transforming, and augmenting data for analytics applications. Data scientists should be conversant with popular data warehouse and data lake settings, including relational and NoSQL databases, as well as big data platforms like Apache Spark and Hadoop, to aid in their efforts.
7. Model Deployment and Production
The majority of data scientists’ time is spent developing and deploying models. They must be able to choose the appropriate method, then use training data in supervised learning approaches or execute the algorithm to automatically uncover clusters or patterns in unsupervised learning approaches. Once a model has produced the intended findings, data scientists must deploy it in a production environment, frequently in collaboration with data engineers, to assist their businesses in making realistic business choices on a regular basis.
8. Data Visualization
Another key data science skill is the ability to effectively display data when presenting analytics results, especially when working with massive quantities of big data that contain a variety of data kinds. Data scientists must be able to emphasize and explain the insights they’ve developed via data storytelling, and data visualization is a key way they communicate those insights to business leaders and other stakeholders. As a result, they should be able to use Tableau, D3.js, or a variety of other data visualization tools to aid in the process. They should also learn how to make various data visualizations, such as line, bar, and pie charts, histograms, bubble charts, heat maps, scatter plots, and more.
Nontechnical and Soft Skills
It’s just as crucial for data scientists to have a set of soft skills in addition to technical skills. Many data scientists, as previously stated, must be able to translate analytics findings and report on them to their business counterparts. Furthermore, certain innate characteristics enable them to look at enormous pools of data with an inquisitive mind, create analytics hypotheses, and uncover hidden knowledge jewels. Adding to the total list of abilities, these six soft skills are essential for a well-rounded data scientist.
1. Business Knowledge
Data science teams, rather than being in IT or a centralized analytics group, are often assigned to a line of business. Even if this isn’t the case, their job entails dealing with business issues. As a result, data scientists must be well-versed in the company’s operations and the industry in which it works. This enables them to ask more insightful data analysis questions, develop new methods for the organization to use its data, and determine which analytics issues to prioritize.
Data scientist’s jobs are frequently tasked with finding information needles in the haystacks of massive amounts of data. To do so, they develop a hypothesis about a business opportunity or problem, which they then attempt to validate by examining data. They’ll need a sharp mind for problem-solving as they work through the data science process, figuring out how various pieces fit into the equation and deciding what data should be included or left out, among other things.
Curiosity, asking questions, and a willingness to learn are all essential qualities for a data scientist. Curious minds may sift through a lot of information to uncover answers and insights. Data scientists should not be complacent about how they approach data or limit themselves to the present conclusions they’ve drawn from it because data changes all the time.
4. Critical thinking
Skills in critical thinking are also essential. Data scientists must be able to evaluate data sets and analytics results in order to make meaningful and relevant decisions. Data scientists can achieve accurate and fair conclusions by approaching data with skepticism.
Data scientists that work with data on a daily basis are better than anybody else at understanding it, including its complexities and intricacies. Of course, the same can be said for the results they provide as part of data science applications. They must be able to effectively express their comprehension of the data and explain the analytics results so that corporate executives and employees can make informed decisions based on the information.
It’s also crucial to be able to work as part of a larger group. Data scientists frequently work with other data scientists, as well as data analysts, business leaders, subject matter experts, data engineers, and other employees.
Data science is not a profession that can be fully learned in a few weeks or through informal online courses, code academies, or boot camps due to the numerous technical skills necessary. Data scientists typically hold a variety of academic degrees and certifications, and they engage in ongoing education to stay current on the latest data science and machine learning approaches and tools. However, there are an increasing number of resources and opportunities accessible for people interested in pursuing a career in data science.