14 Most In-Demand Data Science Skills You Need to Succeed | 7wData

14 Most In-Demand Data Science Skills You Need to Succeed | 7wData

As companies continue to grow their data assets, the need to extract meaningful information -- and business value -- from that data is becoming increasingly important. Analyzing and gleaning insights from data requires a different skill set than simply storing and managing it. Many organizations are quickly realizing that they need talented analytics professionals who have specific skills in scientific methods, statistical approaches, data analysis and other data-centric methodologies.

Emerging a little over a decade ago, the field of data science focuses on uncovering information and insights in large amounts of both structured data and unstructured data. It enables organizations to get answers to business questions, spot trends and make intelligent predictions based on analysis of their data.

data science work is typically performed by data scientists. With backgrounds in mathematics, Statistics, data mining, advanced analytics, algorithms and now machine learning and AI, data scientists can gain a comprehensive understanding of data and apply their skills to find relevant analytics results.

For prospective data scientists, and organizations looking to hire them, the critical skills they need to do their jobs effectively include both technical capabilities and soft skills -- personality traits and characteristics that can help them achieve the desired outcomes and bridge the gap between technologists and business executives and workers. Let's look more closely at these key data science skills.

In order for data scientists to ask the right questions, develop good analytical models and successfully analyze the findings, they must have a variety of "hard skills" that require specific training and education. Here are eight technical skills that data scientists typically need. Statistics. Since data scientists regularly apply statistical concepts and techniques, it should come as no surprise that it's important for them to have a good understanding of statistics. Being familiar with statistical analysis, distribution curves, probability and other elements of statistics helps data scientists collect, organize, analyze, interpret and present data -- better enabling them to work with the data to find useful results. Calculus and linear algebra. Being able to apply mathematical concepts to understand and optimize fitting functions for matching a model to a data set is incredibly important to getting accurate predictions from the model. Additionally, data scientists should be versed in using dimensionality reduction to simplify complicated analysis problems involving high-dimensional data. These skills are also important in machine learning -- for example, to train an artificial neural network on large volumes of data. Relevant coding skills. Many data scientists learn programming out of necessity. They typically aren't coding masters and usually don't have a degree in computer science, but they are familiar with the basics. Popular programming skills for data scientists include knowledge of the Python, R, SQL and Julia languages. Predictive modeling. Being able to use data to make predictions and model different scenarios and outcomes is a central part of data science. Predictive analytics looks for patterns in existing or new data to forecast future events, behavior and results; it can be applied to various use cases in different industries. As a result, predictive modeling skills are heavily used by data scientists. According to job posting data, these are the top data science skills sought by employers. Machine learning and deep learning. While data scientists don't necessarily need to work with AI technologies, they're increasingly being hired by companies looking to implement machine learning applications, in which they train algorithms to learn about data sets and then look for patterns, anomalies or insights in the data.

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