As technology continues to advance so will the field of data science. Much of 2018 was viewed as the year of data breaches and leaks; this year however, will be known for technological disruption and use of Artificial Intelligence. In 2019 we will also see a focus on maturing technologies. Here are some of the top data science trends for 2019:
Reinforcement Learning: During 2019 there will be a resurgence of reinforcement learning. Even though this is best described as a “human-like learning behavior,” we will see its use in data science through statistics and algorithms. Concepts using reinforcement learning will start to turn into actual products as well.
Augmented Data Management: This latest trend in data science is a game changer for the industry. According to Information Age, “Augmented data management utilises machine learning capabilities and AI technology to make data management categories including data quality, master data management, metadata management, data integration as well as database management systems (DBMSs) self-configuring and self-tuning.” Thanks to augmented data management, more individuals will be able to use data. It also gives high-level data scientists the opportunity to work on more important tasks.
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Data scientists are in high demand as companies are constantly seeking to get the most value from their resources. Greg Boyd, director at consulting firm Protiviti, says, “as organizations begin to fully capitalize on the use of their internal data assets and examine the integration of hundreds of third-party data sources, the role of the data scientist will continue to expand in relevance.” The rising stars of many businesses are the savvy data scientists who can not only manipulate large amounts of data using complex statistical and visualization techniques but also have a strong acumen that they can derive forward-looking insights. Now, to be an elite data scientists, one must have a particular set of skills. Here are some of the essential traits that data scientists have.
In order to be able to apply objective analysis of facts on a given topic or problem, they need to be solid critical thinkers. According to Anand Rao who is a global artificial intelligence and innovation lead for data and analytics at PwC, “they need to understand the business problem or decision being made and be able to model or abstract what is critical to solving the problem, versus what is extraneous and can be ignored.”
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Even companies that seem to be at the forefront of the digital age are not taking full advantage of their resources. While they may be instituting all of the latest marketing trends, most are ignoring the data that those efforts are helping them collect.
Data Science Requires a Vast Amount of Data
In fact, there are several ways to collect and analyze data in smaller clusters. By looking at the speed at which data was collected, you can get a snapshot of current consumer trends. Looking at a variety of data, on the other hand, tells you how your consumers are finding you and what they’re looking for. Even a history of data can give you insights that you would not otherwise have obtained.
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Data is taking the world by storm. And in today’s digital world, with the internet and other forms of software dominating the markets, it’s important to know what to look forward to in terms of data science. Here are trends that are heating up:
There are a certain amount of customers in any business that provide the most profits. These customers can be tracked based on their past behaviors and purchases. Then, using advanced algorithms you can predict what they will buy in the future and when. This gives you the foresight to prepare your business in the best way possible based on powerful data.
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In the world of data science, Python is becoming widely popular. People are using it in a variety of ways from back-end web servers to even front-end game development, along with everything in between. It has become a real general purpose language and a must-have tool for any programmer’s arsenal. However, besides Python being a multi-use tool, one of the other reasons why it has become so popular is that it is easy to learn. It reads like pseudo-code and is surprisingly agile. Now, while it isn’t the most difficult to learn, picking up any new language in code can be a daunting task, and it is essential to find the right places to learn. Take a look at some of these tips and tricks that can help you with Python.
One of the things that most people enjoy with Python is that you can create your own functions and modules and put them all together in a separate folder. So, you can write down particular codes that you would use in common in a majority of your work and then convert them in a module and keep it to the side in that separate folder.
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The world is full of data. Data that requires extraction to turn into something more useful. Many data scientists took courses in computer science which provided necessary coding and programming skills. However, there is always room to improve, and with codes changing all the time, it can get difficult to stay on top of it if you are not working to hone your craft. Here are five ways that can help you improve your coding and programming skills.
One of the best ways to learn and improve in coding is by reading books. There are two ways to improve yourself. You improve either by learning from your own experience or someone else’s experience. Most of the authors are great programmers themselves so you will find their experiences in that book. Two great suggestions for coding books are Clean Code by Uncle Bob and Effective Java by Joshua Bloch. These books provide first-hand experiences from the authors and contain great advice for finding problems with your code.
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