A continuous buzzing word, with lots of weight, data science is definitely the hot cake; and many people have already tasted it’s sweetness. If you have been regular in reading and following data science trends, you will know that recommender systems, product and price comparison and other similar things have already set their foot and are deep rooted effects of data science.
Just like any other field, the professionals are always inquisitive about the latest trends, techniques and what’s in and what’s out etc. The intriguing field of data science is no exception to this fact.
What Trended in 2018?
Data Science Trends that were Abuzz in 2018
What do you need to do to know the data science trends? Did someone say dig through the various analysis and the websites that mark the trends of 2018? Don’t worry! Go through the following data science trends of 2018 and you’re sorted. The following data science trends are in no specific order and are just an overview of what all practices/things were popular in 2018. This could have been a long list but I restricted and made it exhaustive, pertaining just to data science.
- Optimum Use of Data Science
As per the prediction from Forrester, the collective worth of the businesses that use data will be $1.2 trillion by 2020. Living up to this, the businesses invested in everything that could help them realize the best use of data for their businesses. This included hiring data scientists and segregating the various data science profiles. This led to better analysis of the existing data, thus generating insights useful for the businesses. Apart from this, searching for new avenues to collect data to open up fresh opportunities, was also in focus. 2018 saw many innovations related to real-life applications. Getting overwhelmed by the quantity of data will not help. So, there was a shift in focus on the required quality datasets that would help achieve the desired business objectives. - Digitization of the Dark Data
The focus was more on the digitization of the dark data. Thinking what is dark data? Historical data that has not been utilized is the dark data; data kept in the dark for a long time and hasn’t been brought under the light. This kind of data was in focus in 2018; recovering and digitizing it. Think the process is complete? No, this is an ongoing process that will take time. But waiting for this change to be completed will be beneficial as this data has the potential to make some precise predictions. - Reinforcement Learning
Apart from its two counterparts, supervised and unsupervised learning, reinforcement learning too, was also the talk of the town in 2018. This is a type of machine learning training in which an algorithm learns by correcting its mistakes; gets rewarded for a correct move and fined for a wrong one. In this method, the algorithm finds out the most favorable solution to a problem. One of the most visible instances of this application was Google DeepMind’s AlphaGo Zero. It became the world’s best Go player with the help of reinforcement learning induced training for just 40 days. Do you know what was the striking fact in this machine training? AlphaGo Zero didn’t have any historical data to refer to.
Internet advertising is also a use case of reinforcement learning. This fetches increased profits for the businesses and reduces the costs incurred. Real-time bidding strategy is also enhanced through reinforcement learning. Alibaba, the multinational Chinese conglomerate, applies reinforcement learning and in 2018, 99.51% budget was spent and 350% ROI was achieved with reinforcement learning based bidding. - Probabilistic Programming
A cluster of programming languages that make it easy to solve and describe statistical problems, Probabilistic programming came into light when the Defense Advanced Research Projects Agency (DARPA) started a project Probabilistic Programming for Advancing Machine Learning (PPAML). The objective of this project was to build new programming languages that speed up the creation of new machine learning applications and models. This was aimed at increasing the efficiency of data scientists and developers. Although the program wrapped up in 2017, the work to be done in probabilistic programming still remains. It is anticipated that with the rise in advanced analytics and machine learning, curiosity in probabilistic programming might also increase. - Open Source
Predictive analytics and machine learning will always find solace for operating in the open source, like they did in 2018. This will be true for both basic analytics as well as advanced analytics. Tensorflow has widened its user base in deep learning. Python rose in popularity and became the first choice of the new users. Open source is the hot topic among the developers creating analytics apps. Models developed in R and Python were deployed on open source.
With the increasing popularity of open source platform for analytics, it is anticipated that it will also touch the not so popular phases of the analytics product cycle, model management and deployment. Anaconda project has started offering these functionalities. In the coming years, open source is one such area that needs increased attention from the users. - 2018 had Cloud All Over
2018 saw increased businesses opting for cloud as an integral component of data infrastructure. The model of serverless computing also surfaced as a crucial constituent of the transformed data warehouse.
Cloud analytics is one prevalent trend that is here to stay. The year 2018 saw a high expenditure on data science tools based on cloud. According to IDC, “Latest cloud pricing models will cater to the distinct workload of analytics by 2020. This will mean a 5X increased expenditure on on-premise vs. cloud analytics.” This practice of working on cloud will find perfect application in the machine learning applications, wherein the high-end systems are easily accessible for agile and economical data processing and analysis. - Machine Learning
Machine learning was one of the most talked about rage in 2018 and will be the same in 2019. Terms, such as automated intelligence, machine intelligence and augmented intelligence, were seen in practice. These were created by mixing commercial analytics and machine learning. Recognizing issues of low quality data, completing data catalogs with metadata, recommending insights in visual analytics products and automated creation of models were the processes that benefited from this mix. The year 2019 will see this amorphous trend mature and become an integral part of a business’ digital transformation. - Machine Translation
2018 was also a year of critical discovery, in terms of machine translation. Tasks like Chinese to English translation reached at par with human performance. Experts also introduced fresh approaches for training translation models through two independent text sets. These sets were unaligned and present in the source and target language. This experiment opens up the possibility of training machine translation models in uncommon languages like Urdu. - Crystal Clear Job Roles (Increase in Demand for Data Scientists)
After all the commotion related to ambiguous job roles, 2018 saw clear specifications regarding the data science job roles. Whether it is for analytics, mining, warehousing, visualization or the top data science roles, skilled and trained professionals are in demand and are likely to be appointed. There may be some companies operating on the same notions of hiring one person and getting most of the tasks done but this scenario is most likely to change in the coming years.
According to a report in 2018, there was a whooping rise of 400% in the demand for data scientists in India. To fill the demand-supply gap of data scientists, an increased number of professionals have started taking data science certification to grab the highly lucrative jobs in the field. - Safeguarding the data (Issues Related to Data Privacy)
2018 was the year for the organizations to assert that they possess modern measures for data security. This was the effect of the implementation of General Data Protection Regulation from European Union (EU). Irrespective of the geographical location of the business, adherence to the rules of data governance and control and privacy rights becomes mandatory. Yet another reason for increased concern for data protection is high level data breaches.
If the companies will still think that having increased access to more and more data will translate to success, they will be en-route to big issues. Cited as one of the major points of concerns in 2018, data security is the number one business challenge of the year.
It is not just about 2018 or 2019 that data science trends have been the focal point, but this wave will stay over the years; develop and play an intriguing and crucial part in every industry. As opposed to the common conception, keeping abreast with the latest data science trends is not that difficult. Just keep your mind focused and alert. You will definitely get to know the latest happenings in the field; just connect, converse and read.
Sources:
https://www.datascience.com/blog/2018-data-science-predictions
https://arxiv.org/pdf/1802.09756.pdf
https://www.idc.com/promo/thirdplatform/fourpillars/cloud
https://blogs.microsoft.com/ai/chinese-to-english-translator-milestone/
https://www.kdnuggets.com/2019/03/poll-analytics-data-science-ml-applied-2018.html
Thanks for sharing all the information with us all.