Photo by SpaceX on Unsplash Companies of all sizes are looking into implementing data science and machine learning into their products, strategies, and reporting. However, as companies start managing data science teams, they quickly realize there are a lot of challenges and inefficiencies that said teams face. Although it has been nearly a decade since…
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Photo by Kaleidico on Unsplash Companies of all sizes have embraced using data to make decisions. However, according to a 2019 report from Goldman Sachs it’s actually quite difficult for businesses to use data to build a sustainable competitive advantage. Our team has worked with and for companies across industries. We have seen the good, the bad and the…
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Did you know that $28.5 billion were spent on investing in machine learning projects, tools, and employees in 2019? Machine learning has taken over pretty much every industry, owing to the automation and flexibility it is bringing to work. Companies of all sizes are using machine learning tools and cloud services. Services like AWS Comprehend and other similar…
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Photo by Ramón Salinero on Unsplash Data scientists and data analysts are constantly required to answer questions for the business. This could result in a more ad-hoc analysis or some form of model that will be implemented into a company’s workflows. But to perform data science and analytics, teams first need access to quality data…
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Photo by Yancy Min on Unsplash In this tech-centric era, companies aim to extract maximum business value from their data in order to stay relevant and efficient via data-driven business decisions. In order to do so, they rely on Artificial Intelligence and Machine Learning. In fact, research shows that artificial intelligence can increase business productivity by 40%! Therefore, it…
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Python really is everywhere at this point. Although many gatekeepers argue whether a person is really a software developer if they don’t code in a language more difficult than Python, it still is everywhere. It’s used to automate, manage websites, analyze data, and wrangle big data. As data grows, the way we manage it becomes more and…
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Photo by ZACHARY STAINES on Unsplash Performing ad hoc analysis is a daily part of life for most data scientists and analysts on operations teams. They are often held back by not having direct and immediate access to their data because the data might not be in a data warehouse or it might be stored across…
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Data science and statistics are not magic. They won’t magically fix all of a company’s problems. However, they are useful tools to help companies make more accurate decisions and automate repetitive work and choices that teams need to make. Machine learning and data science get referenced a lot when referring to natural language processing, imaging…
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Data science, Sigma Six, analytics, business intelligence, all are different sides of the same multi-sided polygon. Each have different tools, vocabularies, projects, and certifications. However, they all serve the business to reduce costs and increase revenues. These are practical tools that help businesses be more effective at what they do! And each comes with their…
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Operating a data science team is not something that can just be learned by watching lectures and videos on Coursera and Udemy. Don’t get us wrong, they are great places to learn data science and machine learning theory with practice problems. However, they don’t teach good business practices, and how to operate a data team…
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