The hot cake is already a popular dessert in the market and there’s not even a single food lover who hasn’t tasted it. No, this article hasn’t started off on a wrong note and is definitely not off the topic. Just substitute data science with hot cake and organization with food lover, and it will all make sense.
Picture this- As per the prediction from Forrester, the collective worth of the businesses that use data will be $1.2 trillion by 2020.
Data science has become such a hot topic that it is all-over; professionals are turning towards this field, businesses are adopting data science so as to arrive at a viable solution for their business problems, innovations are being done so as to automate mundane tasks etc.
This great noise around the data has been around since many years. And, year after year, data science trends have been like a mirror, showing what’s there; while some built up on the previous years and some were new.
Let’s have a look at what trended in the first half of 2019. But before we move on to that, here is what you can expect from this post.
- Highlights (Trends) of 2018
- Data Science 2019 (Predictions and Trends)
- Latest Developments in the Data Science Field
Highlights (Trends) of 2018
To know what trended in 2018 and which trends made it to the news, you don’t need to go through the intricate patterns and analytical studies on the web. Here are some of the data science trends of 2018, in brief. For a detailed view, read top 10 data science trends of 2018.
#1. As opposed to previous apprehensions in investing in data related processes, organizations now have a dedicated budget for data related operations. In 2018, there was less confusion between the job roles and the descriptions, with job adverts getting more specific.
#2. Reinforcement learning also trended in 2018. There was increased focus on making algorithms learn from their mistakes. As one of its applications, real-time bidding strategy also gained in popularity. In 2018, 99.51% budget was spent and 350% ROI was achieved with reinforcement learning based bidding.
#3. The face of data warehouse also saw a drastic transformation with most of the business organizations adopting cloud as a crucial part of their data infrastructure. In 2018, there was an increase in expenditure on data science tools based on cloud.
#4. Machine learning was one of the top trends in 2018 and will be the same, year after year. Some of the processes that benefited from it were, identifying what deteriorates the data quality, building automated models, etc.
#5. Data scientists were high in demand in 2018. According to a report in 2018, there was a whooping rise of 400% in the demand for data scientists in India.
What’s in Store for 2019?
Data Science Predictions and Trends 2019
Now you must have got an idea of the data science trends that made it to the news in 2018. Want to know what’s the scene in 2019? With the first half of the year already over, you must be eager to know the face of data science 2019.
To start with, here are the data science predictions for 2019.
- Building productive and perceptive products
The integration of workflows and data science models will ensure that productive and perceptive models are made. Data science powered intelligent products will make precise predictions with the implementation of machine learning algorithms.
- ML/AI shifts focus from analytic platforms to process or industry specific applications
ML/AI has shifted focus to process or industry specific applications with an aim to solve industry focused issues in businesses like healthcare, B2B sales etc. Developers will be the ones providing improvements and updates in these ML/AI integrated applications. With this advancement, medium-sized and small organizations are expected to adopt ML/AI rapidly and house just a few developers, instead of having a group of enormously expensive data scientists.
- Reforming social media sphere
Whether it is image recognition or any other ML/AI powered feature, the social media sphere has started witnessing the change. The social media professionals like social media marketers, are in for a treat as their work is going to get detailed and focused with inclusion of data science. Comprehensive customer insights and knowing the product consumption trend are some of the benefits that these social media professionals will derive from this integrated use.
- Increase in demand for data engineers
An enormous increase in demand for data engineers is anticipated in 2019. These are the superhumans who will unleash the insights about digital data based software and products given by data scientists. With increased number of organizations turning towards data science to give meaning to their huge pile of data, you can expect to see more number of data engineers hired this year.
- Use of predictive analytics in the delivery processes
Along with many other systems and processes that will be integrating ML and AI, delivery processes are also expected to join this league. These integrated delivery processes will be focused more on creating value, instead of only cleaning up. Do you know one of the most popular applications of this process, already in use? It’s the recommendation system.
- Rise of seasoned data science professionals
Instead of a person who can try a bit of everything, the organizations will aim at hiring highly seasoned, experienced and skilled team of data science professionals. In 2019, you won’t see ambiguous job descriptions, but will have clear cut job roles for every designation. Python will gain even more popularity with a majority of businesses going in for this language. These seasoned data science professionals are expected to work on larger datasets than ever.
- Increasingly competent chatbots and virtual assistants will own 2019
With continuous efforts being made towards increasing the competency of chatbots and virtual assistants (like Google’s Virtual Assistant and Apple Siri), 2019 will also see some state-of-the-art innovations in this direction. This means 2019 will be the year of these virtual assistants and chatbots. The stress will be on making these technologies increasingly user-friendly; so, you get what you want.
Current Advancements in Data Science
Did You Notice Them?
With the passage of time, there have been a number of developments in the data science field. Along with the trends, these data science developments/advancements/applications were also in the news in these six months of 2019. But did you notice them? Here are two of these data science developments/advancements/applications.
- The development of data science increased the statistics cut-offs
This is the direct effect of the development of data science and the immense popularity it has garnered. Several universities have now set the cut-off percentage at 90% for all the undergraduate statistics courses. There is also an increase in the total number of seats in these courses. With an anticipated hike in the demand for skilled data scientists, preference for statistics course has also risen.
- Application of Bloom filter for recommendation systems
Bloom filter’s application was also traced to the recommendation systems. Medium, one of the most popular blog resources, suggests stories based on your choice of the author (a simple filter) or because many people who recommended the same story as you did also recommended this story (collaborative filter). So, when the recommendation system suggests a story to the user, it has to ensure that it has neither been suggested nor read before. Here comes the role of the Bloom filter. This system replies best to “was this seen before?”.
Did you read more of such data science applications/news? Do share them in the comment box below.
Data science has stepped in almost every field along with IT, be it health, e-commerce, entertainment etc. With this far-reaching impact, you can expect to come across many such data science trends in the second half of 2019.
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