Data science is the discipline of making data useful.
There is absolutely no doubt that this decade has bought loads of innovation in Artificial Intelligence. Besides Artificial Intelligence, we are witnessing a massive boost in the data generated from thousands of sources. The fact that millions of devices are responsible for this enormous spike in data brings us to the topic of its smart utilization.
The domain of Data Science brings with itself a variety of scientific tools, processes, algorithms, and knowledge extraction systems from structured and unstructured data alike, for identifying meaningful patterns in it.
Data Science also benefits data mining and big data. Brought into the mainstream in the year 2001, Data Science has been evolving ever since and is rated as one of the most exciting career paths of all time.
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Towards Data Science reports:
- Currently, the daily data output is more than 2.5 quintillion bytes.
- In the near future, “1.7 Mb of data will be created every second for every person on the planet.”
- A wide variety of Data Science roles will drive these massive data loads.
Trends in Data Science
With the diversity in data problems and requirements, comes a broad range of innovative solutions. These solutions often bring with themselves a host of data science trends granting businesses the agility they require while offering them deeper insights into their data. A few of these top Data Science trends are briefly explained below:
1. Graph Analytics
With data flowing in from all directions, it becomes harder to analyze.
Graph Analytics aims to solve this problem by acting as a flexible yet powerful tool that analyzes complicated data points and relationships using graphs. The intention behind using graphs is to represent the complex data abstractly and in a visual format that is easier to digest and offers maximum insights. Graph Analytics are applied in a plethora of areas such as:
- Filtering out bots on social media to reduce false information
- Identifying frauds in banking industries
- Preventing financial crime
- Analyzing power and water grids to find flaws
2. Data Fabric
Data Fabric is a relatively new trend, and at its core, it encapsulates an organization’s data collected from a vast number of sources such as APIs, reusable data services, pipelines, semantic tiers, providing transformable access to data.
Created for assisting the business context of data and keeping data in an intelligible way not just for users but also for applications, Data Fabrics enable you to have scalable data while being agile.
By doing so, you get unparalleled access to process, manage, store, and share the data as needed. Business Intelligence and Data Science relies heavily upon Data Fabrics due to its smooth and clean access to enormous amounts of data.
3. Data Privacy by Design
The trend of Data privacy by design incorporates a safer and more proactive approach to collecting and handling user data while training your machine learning model on it.
Corporations need user data to train their models on real-world scenarios, and they collect data from various sources such as browsing patterns and devices.
The idea behind Federated Learning is to collect as little data as possible, keeping the user in the loop by also giving them the option to opt-out and erase all collected data at any time.
While the data may come from an enormous audience, for privacy reasons, it must be guaranteed that any reverse-engineering of the original data to identify the user isn’t possible.
4. Augmented Analytics
Augmented Analytics refers to driving better insights from the data in hand by excluding any incorrect conclusions or bias for optimized decisions. By infusing Artificial Intelligence and Machine Learning, Augmented Analytics aids users in planning a new model.
With reduced dependency on data scientists and machine learning experts, Augmented Analytics aims to deliver relatively better insights on data to aid the entire Business Intelligence process.
This subtle introduction of Artificial Intelligence & Machine Learning has a significant impact on the traditional insight discovery process by automating many aspects of data science. Augmented Analytics is gaining a stronghold in providing better decisions free of any errors and bias in the analysis.
5. Python as the De-Facto Language for Data Science
Python is an absolute all-rounder programming language and is considered a worth entry point if you’re interested in getting into the world of Artificial Intelligence and Data Science.
With a supportive online community, you can get support almost instantly, and the integrations in Python are just the tip of the iceberg.
The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code — not in reams of trivial code that bores the reader to death.
– Guido van Rossum
Python comes stacked with integrations for numerous programming languages and libraries, making it an excellent option for, say, jumping into creating a quick prototype for the problem at hand or going in-depth into large datasets.
Some of its most popular libraries are –
● TensorFlow, for machine learning workloads and working with datasets
● scikit-learn, for training machine learning models
● PyTorch, for computer vision and natural language processing
● Keras, as the code interface for highly complex mathematical calculations and operations
● SparkMLlib, like Apache Spark’s Machine Learning library, making machine learning easy for everyone with tools like algorithms and utilities
6. Widespread Automation in Data Science
Time is a critical component, and none of it should be spent on performing repetitive tasks.
As Artificial intelligence advanced, its automation capabilities expanded as well. Various innovations in automation are turning many complex Artificial Intelligence tasks easier.
Automation in the field of Data Science is already simplifying much of the process, if not all. The entire process of Data Science includes identification of the problem, data collection, processing, exploration, analysis, and sharing of processed information to others.
7. Conversational Analytics and Natural Language Processing
Natural Language Processing and Conversational Analytics are already making big waves in the digital world by simplifying the way we interact with machines and look up information online.
NLP has hugely helped us progress into an era where computers and humans can communicate in common natural language, enabling a constant and fluent conversation between the two.
The applications of NLP and conversational systems can be seen everywhere, such as chatbots and smart digital assistants. It has been predicted that the usage of voice-based searches will exceed the more commonly used text-based searches in a very short time.
8. Super-sized Data Science in the Cloud
The onset of Artificial Intelligence and the amount of data generated from it has skyrocketed ever since. The size of data grew tremendously from a few gigabytes to a few hundred as businesses grew their online presence.
This increased requirement of data storage and processing capabilities gave rise to Data Science for a controlled and precise utilization of data and pushed organizations working on a global scale to opt for cloud solutions.
Various cloud solutions providers such as Google, Amazon, Microsoft offer vast cloud computing options that include enterprise-grade cloud server capabilities ensuring high scalability and zero downtime.
9. Mitigate Model Biases and Discrimination
No model is entirely immune to biases, and they can begin to exhibit discriminatory behavior at any stage due to factors such as lack of sufficient data, historical bias, and incorrect data collection practices. Bias and discrimination is a common problem with models and is an emerging trend. If timely detected, these biases can be mitigated at three stages:
- Pre-Processing Stage
- In-Processing Stage
- Post-Processing Stage
Each stage comes with its own set of corrective aspects including algorithms and techniques to optimize the model for fairness, and to increase its accuracy for eliminating any chance of bias.
10. In-Memory Computing
In-Memory computing is an emerging trend that is vastly different from how we traditionally process data.
In-Memory computing processes data stored in an in-memory database as opposed to the traditional methods using hard drives and relational databases with a querying language. This technique allows for processing and querying of data in real-time for instant decision making and reporting.
With memory becoming cheaper and businesses relying on real-time results, In-Memory computing enables them to have applications with richer, more interactive dashboards that can be supplied with newer data and be ready for reporting almost instantly.
11. Blockchain in Data and Analytics
Blockchain, in simpler terms, is a time-stamped collection of immutable data managed by a cluster of computers, and not by any single entity. The chain here refers to the connection between each of these blocks, bound together using cryptographic algorithms.
Transforming gradually similar to Data Science, Blockchain is crucial for maintaining and validating records while Data Science works on the collecting and information extraction part of the data. Data Science and Blockchain are related as they both use algorithms to govern various segments of their processing.
As businesses begin to grow, they generate more data, and Data Science can help them analyze their areas of improvement. With several of the noteworthy Data Science trends mentioned above, some have begun to consider Data Science as the fourth paradigm of science next to Empirical, Theoretical, Computational. Keeping up to date with newer trends is an absolute must for businesses to achieve maximum efficiency and stay at the forefront of the competition.
This article originally appeared on Medium by Claire D. Costa
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