Photo by Sean Pollock on Unsplash Banks from Seattle to New York trust us with their data science solutions. Why’s that? The short answer: while the finance industry has been leveraging data science and statistics for decades, times are changing. Data science and financial strategy are evolving. This means teams need to stay current in order to maximize…
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Photo by Sean Pollock on Unsplash You may think of ATM workings as a revolutionary experience but since the advent of Fintech, the entire financial services domain has entered a new era. Whether you purchase a cup of coffee or manage your finances, fintech is everywhere. From payments via apps such as Payoneer or Paypal to getting reports,…
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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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Are All NoSQL Systems The Same? Photo by Campaign Creators on Unsplash Which database is best for your current business needs is usually dependent on the skill set of your dev team and the applications in place already. Understanding which database system will best fit your companies both current and future needs is an important step. Databases…
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Data is fast becoming an essential part of small and big companies globally. Treating data as an asset implies using tools to perform data analytics to the vital aspects of your business. The increasing importance and demand for data analytics have generated several global prospects and ideas on data. According to International Data Corporation, Big data…
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Why Invest In A Data Warehouse We recently had a medium sized company ask us why they might want to build a data warehouse. (If you are curious about what a data warehouse, feel free to read here) They had an operational relational database and a few ideas for products that we found brilliant. We…
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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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