11 Data Scientist Skills In Demand Today
For the fourth year in a row data scientist has been named “the best job in America” by Glassdoor. From healthcare to software to real estate and financial services, the skills required for data scientists are in demand across industries. As demand for data scientist skills remains strong, hiring for these positions poses a challenge. With this ongoing talent shortage, organizations must consider training existing staff with data science skills as well as keeping their data scientists’ skills up to date with a fast-changing industry. For example, Udemy for Business customer Booz Allen Hamilton recently launched an internal training program to help the company meet its goal of reaching 5,000 data scientists. Watch the webinar here: Winning the War on Talent: Scaling Personalizing Learning. Prioritizing a training curriculum on relevant programming languages and cutting-edge data science techniques will help managers keep their teams’ output competitive with global trends. Find out how Udemy for Business can help upskill your data science team.
Data science overlaps with the growing field of artificial intelligence (AI). To function at their full potential, AI applications require data—lots of it. Data is the food that powers AI and knowledgeable data scientists are needed to properly clean, prepare, and extract this data in order to run powerful AI applications. In fact, many of the following AI and data science skills—from programming languages to algorithmic techniques—work in tandem with one another, building on top of each other to garner deeper data insights.
Based on insights from Udemy for Business customers and our expert instructors, here are the technical skills to look for when hiring data scientists or when planning learning paths for your current team.
Python is central to the growth of AI and data science and arguably the most important skill to learn for data science exploration. The programming language is a favorite for data scientists, web developers, and AI experts thanks to the simplicity of its syntax, the wealth of open-source libraries created for the language that drive efficiencies in building algorithms and apps, and its diverse applications across data analysis and AI.
Learn Python: Learn Python Programming Masterclass
The R programming language is most often used for the statistical analysis of large datasets. It was the preferred tool in the data science community for many years as an ideal resource for plotting and building data visualizations. R sees continued strong use in academic circles. However, the fast performance speeds of Python have made it the optimal choice for the convergence of data science and AI applications.
3. Machine learning
Machine learning is a subfield of AI that uses data and algorithms to train computers to identify patterns and take actions or make predictions on those patterns without being specifically programmed to do so. Machine learning uses structured datasets to teach computers across a variety of techniques most commonly differentiated in several categories including supervised learning, unsupervised learning, and reinforcement learning. Fraud detection and recommendation engines are common applications of machine learning.
Learn Machine Learning: Data Science and Machine Learning Bootcamp with R
4. Deep learning
Deep learning is considered a subset of machine learning and uses artificial neural networks, which are computational algorithms built on many layers of datasets. These artificial neural networks are intended to mimic the neural networks of the human brain and learns by observing details in the datasets it’s told (by a data scientist or web engineer) to study. Deep learning is used in applications like image recognition—training digital photo albums to recognize and group photos of your parents together—and also in robotics, teaching robots to recognize common scenarios and to react accordingly in each one.
Learn Deep Learning: Deep Learning A-Z: Hands-On Artificial Neural Networks
5. Regression and classification
Regression and classification are methods of supervised learning where input and output data are provided to the learning algorithms. Both methods attempt to predict a value based on supplied datasets; regression algorithms use numerical data while classification algorithms use categorical data. Regression analysis may be used in predicting housing prices based on data of similar houses in similar neighborhoods. Classification analysis would be used in a mobile app that identifies a plant based on a user’s uploaded photo.
Learn Regression and Classification: Deep Learning Prerequisites: Linear Regression in Python
6. Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that trains computers on reading and understanding language as it’s informally used by humans. NLP uses machine learning algorithms to parse substantial amounts of data on language syntax and semantics, teaching computers to understand human speech inputs and respond accordingly. NLP is at work in digital assistants like Alexa, chatbots for customer service support, and even the legal industry for the quick scan of lengthy legal documents.
Learn Natural Language Processing: Data Science: Natural Language Processing in Python
SQL (Structured Query Language) is a necessary skill for anyone in a data role and is useful for software engineers and system administrators. SQL is great at data manipulation, allowing teams to run basic to advanced queries and merge data from multiple data sources. Like Python, it’s an essential skill for most data scientists today, letting teams derive meaningful business insights from even the most simple of queries.
NoSQL refers to “Not Only SQL,” a type of database that isn’t bound by a relational structured data schema such as those that use SQL syntax. Learning NoSQL isn’t as straightforward as learning the language of SQL. Data scientists and developers instead should learn the principles of building and maintaining unstructured NoSQL databases as they continue to grow in popularity with large tech companies like Facebook and Google relying on NoSQL.
Learn NoSQL Database Administration: The Complete Developers Guide to MongoDB
Elasticsearch is a powerful open-source analytics and full-text search engine. The full-text functionality is often used to power search on applications and websites, and will account for typos, auto-completion, and synonyms to make it highly intuitive for the end user. It can be used as an analytics engine by writing queries to aggregate data. A common use case is monitoring an application’s performance management (APM).
Learn Elasticsearch: Complete Guide to Elasticsearch
Hadoop is open-source software that stores large data volumes across clusters of computers, which allows organizations to scale and distribute data processes without worrying if its computing systems and servers have enough memory available. A data scientist might use Hadoop to quickly process, explore, filter, and sample massive datasets across many clusters.
Learn Hadoop: The Ultimate Hands-On Hadoop — Tame Your Big Data!
11. Data visualization
Data visualization tools allow teams to extract meaningful insights from data and share with stakeholders who can then take business actions. Popular data visualization tools like Tableau and Microsoft Power BI let users make complex datasets more digestible through visual representations and intuitive dashboards.
Learn data visualization: Tableau for Beginners
This list of skills to prioritize with your data science team isn’t exhaustive. Tools and skill specialties are constantly evolving as AI adoption grows and companies recognize the deep tech infrastructure required to arm teams with comprehensive business data. The next step in building a world-class data science team is to tackle the data challenges within your organization’s IT infrastructure. Download “Tackling Your Data Challenges” to learn how to partner with IT on solving some of the most common hurdles to a data-driven company. Find out how Udemy for Business can help upskill your data science team.
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