Most In Demand Data Science Technical Skills
Learn what data science technical skills and business skills are in the highest demand that you should have as a data scientist.
Data scientists positions today require a mix of technical knowledge, a strong business acumen, and communication skills to provide meaningful insights from data. Team sizes range from hundreds of members at large companies to a handful at smaller firms. Data science is a growing field, and the Bureau of Labor Statistics predicts that the demand for Data and Mathematical Scientists will increase by nearly 28% by 2026. Recent graduates or those seeking career change may find themselves interested in these roles and the knowledge and skills required to be competitive for positions.
Below, we will outline the top data science skills demanded in the job market for data scientists today.
What Data Science Skills are Needed to Become a Successful Data Scientist Today
1. Programming Skills
Strong programming skills are particularly important for data scientist candidates. Skilled coders compose elegant solutions that are easily understood, scalable, and free from error. Employers tend to prefer candidates with coding experience as a result. Coding for data science is predominantly written in Python, SQL, and R. Each data science programming language has its own strengths and weaknesses, adding to the benefit of knowing multiple languages. Firms may have different preferred languages, but a knowledge of these three will suffice for the majority of data science jobs. In the event that you need to pick up another language, knowledge of one language often reduces the time needed to learn another.
The majority of data science tools are available in Python, and the language is capable of everything from preprocessing data and modeling to visualization. Python code is easily read when written properly and runs quickly considering it’s simplicity. As a result, Python has become the gold standard for data analytics and one of the most in-demand data science skills. The demand for Python programmers in the data space continues to grow.
Within Python, there are several libraries that are very common for data scientists and their functions should be learned when studying Python. Pandas is a popular library for data manipulation and analysis and is found in most data analytics projects. Everything from conveniently reading different file types to deleting columns and replacing blank values is simple in Pandas. You will regularly see Pandas listed as a required library in job requirements, and data science applicants should be well versed. For machine learning in Python there are a few libraries that reoccur including scikit-learn, the most popular ‘out-of-the-box’ machine learning library. Data science applicants must become familiar with the programming syntax and options for scikit-learn in order to be competitive.
At StrataScratch, we have hundreds of Python data science interview coding questions from top employers including Microsoft, Facebook, and Uber. For example, this programming problem where the coder is expected to calculate project budget allocation at an employee-level provides an example of what you can expect from interview problems at top data science companies.
If you're wondering how much python is required for data science work, check out our article on How Much Python is Required for Data Science.
Standard Query Language (SQL) is the foundation of the modern data query, and allows for data scientists to search databases for relevant data. Scripting in SQL is advantageous for data scientists because it enables them to build their own datasets and to perform scalable basic to intermediate analysis. Many data scientists begin their careers as data analysts, which at many firms frequently work with SQL to query databases and to find answers to solve business problems. Data science teams often value this experience as it improves a candidate's understanding of databases which is critical for working with larger data efficiency. At this link, you can get a sense of the SQL programming problems that are common in today’s data science interviews.
Though less common than Python and SQL, R is an important supplemental statistical language that is used by mathematics and data professionals for modeling and data visualization.
R benefits from robust easily implemented statistical libraries that are concisely coded, and results are returned in a table format that is second to none. For the mathematically inclined with less of a computer science or programming background, the simplicity of R may provide a path of entry. R isn’t an absolute requirement in the way that Python or SQL are for data scientist applicants, but is relatively common in the economics and finance sectors among others. R is an open-source project that features tutorials on its website, and the language’s syntax is fairly easy to follow.
Luckily, many tools are available for those looking to learn Python and SQL for data science including those here at StrataScratch.
Check out our article on Python vs R for Data Science to find out which language is better.
The amount of mathematical knowledge required for data science may vary depending on both the company and specific role, but top data scientists understand the mathematical principles of the tools that they use. Mathematic data science skills are desirable due to the technical manner of data science, and employers may inquire about candidates' mathematical proficiency during interviews. While it’s unlikely that you’ll need to solve complicated problems from scratch routinely, a general understanding of the mathematics behind different aspects of data is desired.
An understanding of elementary statistics is helpful when interpreting machine learning results, as metrics are reported in statistical terms. Without a working knowledge of standard error and probability, a data scientist limits their ability to improve predictive models. StrataScratch offers actual interview problems that allow candidates to learn content to prepare for the interview process. At minimum, it is crucial to understand the various types of distributions and descriptive statistics. Using correct terminology not only adds credibility, but also helps to frame data problems accurately for teammates and stakeholders.
Calculus is ever-present in data science projects. For example, optimization problems solve for the best solution using gradient descent and classification algorithms reduce dimensionality and cluster observations using techniques from calculus. Much like statistics, you don’t need to be incredibly advanced in calculus to become a data scientist, but it is helpful and often useful to understand the fundamentals.
Linear algebra is common in machine learning models, as dataframes represent data in matrix form and matrices are the domain of linear algebra. Simple concepts such as vectors, matrix manipulations, and eigenvalues are helpful for understanding what happens beneath the hood of modern data science. Image analytics is heavily dependent on linear algebra, as all images are represented as matrices. For example, assume that we want a picture of an apple on a computer in greyscale. Every pixel of the image is represented by a value between 0 and 255, with 0 being absolute black and 255 absolute white. Linear algebra allows us to rotate that image simply by applying a manipulation to the matrix that represents our apple image. While you can program this using libraries and not need to understand the underlying mathematics, familiarity with linear algebra results in a more comprehensive view of the solution.
Set theory is helpful for writing SQL queries, as it provides a foundation for understanding the way in which sets of data can be grouped. The concept of unions, intersections, and cartesian products from set theory for example are all present in SQL. Again, it is very possible to write high-quality SQL script without studying set theory, but it can shorten the process when writing new queries.
3. Data Wrangling and Preparation
One of data’s most common mantras regarding modeling is ’garbage in, garbage out’. The premise is that poorly-prepared or limited data will negatively impact the end product irreversibly. Developing machine learning solutions that perform well is difficult on its own, and ‘garbage’ data makes it impossible. As a result, employers put a premium on data scientists able to improve data quality and build their own datasets.
As a data scientist, the ability to wrangle data ensures that we have good data going into our predictive models so that we can trust our results. Data wrangling is among the most demanded technical data science skills, as the perfect dataset is rarely available immediately in real world projects. Data scientists that are able to wrangle data benefit from the ability to prepare their own datasets, saving time and allowing for more time for model experimentation.
Common data problems include handling missing values and duplicate records, and applying the correct strategy to overcome these limitations can be the difference between a successful project and one that is plagued with error. Data wrangling is broad, and includes examples such as data collection, complex SQL queries across multiple databases, and manipulation of data using Python. It is important for data scientists to construct datasets for analytics from imperfect sources, and adequate data wrangling and preparation skills help to find a solution.
4. Modeling and Machine Learning
Predictive modeling comes to mind for many when we hear the word ‘data scientist’. Machine learning skills are sought by firms around the globe looking to forecast trends, classify customers, or build new technical solutions. Proficiency in predictive analytics is one of the essential data science skills when entering data science, and prospective data scientists should work to understand machine learning models, their use cases, and limitations. StrataScratch has practice problems such as this that give candidates an opportunity to test and sharpen their data science skills in advance. Topics including knowledge of the benefits of specific models, ways to fine tune model performance, and categorizing missing values are available.
Common machine learning models include the traditional statistical models such as linear or support vector machines (SVMs) to the most recent deep networks. Familiarity and expertise with available machine learning models is one of the areas where data scientists can be most impactful. As a result, data scientists should strive to continuously develop their predictive modeling abilities.
In addition to selecting the correct model to apply, data scientists must also master parameter tuning of machine learning models. Most machine learning isn’t automated and a project’s developers are required to adjust a model’s parameters to attain adequate results. Data science applicants that are knowledgeable of parameter tuning differentiate themselves from others by offering better performing models from the same source data. Hiring firms are interested in top talent capable of improving analytical projects results and familiarity of parameter tuning is a great way to gain an advantage on the job market.
5. BI Tools and Developing Dashboards
Knowledge of BI tools is also one of the most in-demand data science skills. Data scientists frequently use BI (Business Intelligence) tools like Tableau, Qlik, and PowerBI for exploratory data analysis (EDA) and general visualization. While each BI tool has its own nuances, these products are similar to one another and skilled users of Power BI for example would be able to create similar end products as one with Tableau. BI tools are extremely useful in assessing the quality and attributes of data, including identifying trends and deriving insights. These products are designed for both technical and non-technical users, and feature ‘drag-and-drop’ user interfaces for data practitioners to create popular visualizations seamlessly. As a data scientist, it is critical to consider your project’s stakeholders at every step. Proper data visualization allows audiences to see a window into your data that otherwise is impossible, and leveraging this technology makes for a more effective Data Scientist and organization.
Dashboards are tools that provide an interactive platform for gathering data insights visually. Dashboards are used by stakeholders to better understand the underlying nature of their data. Compared to traditional flat files, dashboards are far more interactive for the end user. While many consider data visualization to be more in line with the role of a data analyst, data scientists benefit from familiarity with BI tools. Dashboards are advantageous for data scientists as they simplify standard projects which may otherwise be done using a python library to a drag-and-drop user interface capable of basic visualization within minutes. When the dashboard is complete, developers can publish the results online to their organization, and dramatically scale their analytical project’s influence by expanding its audience.
6. Understanding of Non-Relational Databases
A working knowledge of SQL is a prerequisite to become a data scientist, and you should become comfortable with the major database technologies for a career in data science. However, new technologies have become available that can supplement or replace many of the capabilities of traditional relational databases.
Non-relational databases store data in a non-tabular manner, allowing for certain operations to be far faster in NoSQL than in standard SQL. MongoDB and Cassandra are popular NoSQL data platforms. Relational databases are dependent on a manually defined data model and data transformations are common in order to make the datasource compatible with the data model. In the NoSQL platform, schemas are more fluid which is an advantage for frequently changing datasets.
For data scientists that already know SQL, picking up a NoSQL language is generally pretty quick. Due to their popularity and scalability, data science candidates should make an effort to learn NoSQL if they aren’t already familiar.
7. Big Data Analytics
Datasets are becoming larger all of the time, as does the demand for data scientists capable of gathering insights from big data. How one defines ‘big data’ is up to debate, but generally datasets larger than a few gigabytes are considered big data. Traditional analytics tools have difficulty processing this much data, and therefore analysis and manipulation require a special set of data science skills. Employers seek candidates comfortable working with large data as they are uniquely impactful in today’s big data environment.
While many tools are available for big data analytics, Apache’s Spark and Hadoop platforms are among the most common. Both tools are open source and allow data scientists to analyze massive datasets (gigabytes to petabytes). Tasks are split across multiple nodes (distributed computing), resulting in much shorter job times. Spark takes advantage of today’s high-powered graphics processors (GPUs) with its machine learning library and streaming capabilities. For streamed data including web data or stock prices, Spark’s Structured Streaming enables real-time analytics. Hadoop is renowned for its YARN (Yet Another Resource Negotiator) and MapReduce. YARN efficiently schedules computational resources for large jobs. MapReduce takes these large jobs and splits them into smaller tasks, distributing the calculation across multiple nodes and aggregating the result. For example, if we wanted to count the number of words in a book, one way would be to start at the beginning and count each word until the book was finished. MapReduce instead splits the task across a determined number of available nodes, shortening the runtime. Opposed to one processing unit counting in a linear manner, the job is split across multiple units which count a few chapters each. The summation of each node is equal to that of the single node working alone, but the end result is reached more quickly.
In addition to Apache’s platforms, Amazon, Google, and Microsoft offer their own big data platforms. Amazon’s S3 and ML, Google’s BigQuery and ML library, and Microsoft's Azure platform streamline much of the data science pipeline. It isn’t necessary to fully master all of these tools, but familiarity with either Azure, Google Cloud, or Amazon Web Services is sought after in the current job market.
8. Communication and Presentation Skills
In addition to the technical data science skills or ability needed to complete projects, data scientists must all communicate their insights to stakeholders. Data scientists with strong communication skills are desirable because of their ability to explain potentially complicated projects in a digestible manner for general audiences. Data science soft skills are often rewarded in data, as better communicators ask the questions necessary to understand the problem and as a result can build better solutions. Asking clarifying questions early in a project can be the difference between an excellent solution that meets the organization's needs or one that is useless.
9. Domain Expertise
Top Data Scientists bring more than only technical knowledge and communication skills to the team. The most capable data scientists are also knowledgeable about their employer’s industry. If you have worked in retail or played sports, for example, applicants for data scientist positions that intersect with their previous experience are often at an advantage. Employers seek applicants with domain expertise as it reduces the time it takes to understand their business and data.
For example, a data scientist working for a retailer may know that December is their most busy month. If a predictive model is developed predicts that sales will peak in March and that December is the second lowest grossing month, the data scientist would be tipped off that something was wrong. Similarly if solving for a logistics problem, a candidate that previously worked in logistics can be preferred. Data scientists that are knowledgeable about their particular domain have the potential to be more impactful, and as a result are in demand by firms of the same sector.
However, you won’t be a domain expert for every available position. Applicants can make up for a lack of specific knowledge in a number of ways, but the most important is a desire to learn. Businesses often hire data scientists without knowledge of their domain, but it is important to communicate your willingness to learn by reading available resources and asking questions.
In this blog, we have discussed many of the most demanded data science skills. Top applicants should have the data science skills such as strong technical knowledge, domain expertise, and communication skills. Python and SQL and the ability to solve data problems using these languages are in high demand in today’s job market. A general understanding of the mathematics (particularly statistics) behind machine learning models allows for a data scientist to make appropriate adjustments to improve model performance. Employers seek candidates skilled in data wrangling and preparation, as datasets are rarely perfect and require preprocessing. Machine learning and modeling skills are important as a candidate, as these are core competencies of data scientists. You will be expected to bring high-quality solutions to your organization and a knowledge of machine learning principles is critical. Familiarity with Business Intelligence data visualization tools is important for communicating insights with other analysts and project stakeholders. NoSQL languages are a useful supplement and occasionally replacement of SQL, and candidates should know the basics of a NoSQL language. As data grows larger in many industries, it is important to master a big data platform, notably Hadoop’s Spark and Apache. Google Cloud, Microsoft’s Azure, and Amazon’s Amazon Web Service infrastructures are similar to the Hadoop infrastructure, so learning Hadoop’s open-source system is advantageous. Data scientists must communicate with teammates and stakeholders, and businesses value strong communicators for their data teams. Lastly, domain expertise of the relevant sector is important for data scientists as it allows for more impactful analytics. However, this article isn’t fully exhaustive. Additional data science skills may be required based on a data scientist’s particular position and firm.
It is important for data scientists to continue to educate themselves on new data tools and to hone their skills frequently. Technological advancements are made every day, and studying resources such as StrataScratch keep data professionals up-to-date on best practices.