It’s hard to keep up with the pace of change in the data science and machine learning fields. And when you’re under pressure to deliver projects, learning new skills and technologies might be the last thing on your mind. But if you don’t have at least one eye on what you need to learn next you run the risk of falling behind. In turn this means you miss out on new solutions and new opportunities to drive change: you might miss the chance to do things differently.
That’s why we want to make it easy for you with this quick list of what you need to watch out for and learn in 2020.
The growing TensorFlow ecosystem
TensorFlow remains the most popular deep learning framework in the world. With TensorFlow 2.0 the Google-based development team behind it have attempted to rectify a number of issues and improve overall performance. Most notably, some of the problems around usability have been addressed, which should help the project’s continued growth and perhaps even lower the barrier to entry.
Relatedly TensorFlow.js is proving that the wider TensorFlow ecosystem is incredibly healthy. It will be interesting to see what projects emerge in 2020 – it might even bring JavaScript web developers into the machine learning fold.
Explore Packt’s huge range of TensorFlow eBooks and videos on the store.
PyTorch
PyTorch hasn’t quite managed to topple TensorFlow from its perch, but it’s nevertheless growing quickly. Easier to use and more accessible than TensorFlow, if you want to start building deep learning systems quickly your best bet is probably to get started on PyTorch.
Search PyTorch eBooks and videos on the Packt store.
End-to-end data analysis on the cloud
When it comes to data analysis, one of the most pressing issues is to speed up pipelines. This is, of course, notoriously difficult – even in organizations that do their best to be agile and fast, it’s not uncommon to find that their data is fragmented and diffuse, with little alignment across teams.
One of the opportunities for changing this is cloud. When used effectively cloud platforms can dramatically speed up analytics pipelines and make it much easier for data scientists and analysts to deliver insights quickly. This might mean that we need increased collaboration between data professionals, engineers, and architects, but if we’re to really deliver on the data at our disposal, then this shift could be massive.
Learn how to perform analytics on the cloud with Cloud Analytics with Microsoft Azure.
Data science strategy and leadership
While cloud might help to smooth some of the friction that exists in our organizations when it comes to data analytics, there’s no substitute for strong and clear leadership. The split between the engineering side of data and the more scientific or interpretive aspect has been noted, which means that there is going to be a real demand for people that have a strong understanding of what data can do, what it shows, and what it means in terms of action.
Indeed, the article just linked to also mentions that there is likely to be an increasing need for executive level understanding. That means data scientists have the opportunity to take a more senior role inside their organizations, by either working closely with execs or even moving up to that level.
Learn how to build and manage a data science team and initiative that delivers with Managing Data Science.
Going back to the algorithms
In the excitement about the opportunities of machine learning and artificial intelligence, it’s possible that we’ve lost sight of some of the fundamentals: the algorithms. Indeed, given the conversation around algorithmic bias, and unintended consequences it certainly makes sense to place renewed attention on the algorithms that lie right at the center of our work.
Even if you’re not an experienced data analyst or data scientist, if you’re a beginner it’s just as important to dive deep into algorithms. This will give you a robust foundation for everything else you do. And while statistics and mathematics will feel a long way from the supposed sexiness of data science, carefully considering what role they play will ensure that the models you build are accurate and perform as they should.
Get stuck into algorithms with Data Science Algorithms in a Week.
Computer vision and natural language processing
Computer vision and Natural Language Processing are two of the most exciting aspects of modern machine learning and artificial intelligence. Both can be used for analytics projects, but they also have applications in real world digital products. Indeed, with augmented reality and conversational UI becoming more and more common, businesses need to be thinking very carefully about whether this could give them an edge in how they interact with customers.
These sorts of innovations can be driven from many different departments – but technologists and data professionals should be seizing the opportunity to lead the way on how innovation can transform customer relationships.
For more technology eBooks and videos to help you prepare for 2020, head to the Packt store.
