5 Key Data Science Trends for 2022

-




Due to widespread internet adoption and rapid technological advancements in device connectivity, data is flowing at an exponential rate. It is prompting companies to find new ways to turn the data influx into business insights that can lead to better decisions.


Trends such as python courses, NLP, and understanding of the cloud have become necessary requirements for many data enthusiasts.

Data science has gained popularity and acceptance over the years because it helps businesses identify new markets, manage costs, increase efficiency, and build a competitive advantage.


According to a NEWS18 survey, 70% of employees aimed at upskilling and data science is most in-demand.


So how do you figure out what is important in Data science and stay updated with current trends? Let us understand some of the facts and trends in Data Science.


  1. Python Applications

When it comes to data analysis, Python is the go-to programming language. Data science libraries such as Pandas and machine learning libraries such as Scikit-learn are available for free in Python. Although Data Science courses are gaining popularity in recent years, most people are inclined toward learning python.


Python is used in Blockchain applications, crypto trading and data pre-processing, visualization models, and making machine learning models.

According to RedMonk, Python is now ranked as the third most popular language overall.

With its popularity growing rapidly, it can grab the first position by 2025.


  1. Rising demand for data analysts 

We need to accept that the game revolves around data. There has been a booming demand for data analysts in recent years. 

Global data storage is set to grow from 45 zettabytes to 175 zettabytes by 2025, largely because of the Internet of Things (IoT) and advances in cloud computing.


All this data is going to require experts to analyze. You can sort through all of this data using data analytics programs.


There is no doubt that machines can help analyze data. The problem is that big data is often extremely messy and lacks a proper structure.


Therefore, humans must manually tidy training data before machine learning algorithms can use it.

In addition, data professionals are increasingly involved in the output end of the process.

Often, machine learning companies clean up final data using humans, because AI-generated results aren't always accurate or reliable.


  1. Cloud-Based AI and Data Solutions

Cloud-based solutions are becoming popular. There is already a large amount of data being produced. This massive volume of data must be collected, labeled, cleaned, arranged, formatted, and analyzed in one place. Cloud-based platforms are the solution. As Cloud Computing behemoths battle for minds, arms, and budgets, the next several years will be critical. 


In the coming years, cloud-based AI will benefit from rising costs, as well as developments in workflow optimization technology. As well as the growing need for cognitive computing, the market will be driven by the increasing use of cloud-based solutions across various end-user sectors.


  1. Natural Language Processing

Data analysis and trend identification are frequently carried out using Natural Language Processing in corporate operations. Using NLP, data repositories can retrieve data more quickly. High-quality data enables Natural Language Processing (NLP) to produce high-quality insights.


The use of NLP will increase in areas such as Sentiment Analysis, Twitter Analytics, Customer Satisfaction, etc.


  1. Training data 

Despite all the talk about data being the new oil and its importance to organizations, most of this data is never used. For compliance purposes, dark data is mostly collected, processed, and stored. Additionally, 80-90% of the data generated by businesses today is unstructured, making analysis even more difficult.  


You need a lot of training data to build credible machine learning models. Unfortunately, that is one of the main reasons for which supervised and unsupervised learning applications are inhibited. When a large repository of data is unavailable in a particular area, data science activities can be seriously hampered. 


Final words

Companies can derive valuable insights from data science to make smarter decisions based on data from multiple sources. The listed trends are some of the few advancements which will lead to a better tomorrow in the world of Data Science.

Comments

Post a Comment

Popular posts from this blog

Unity Engine - From Humble Beginnings

-
Learning Unity should be your top priority if you want to build a career in game development. What is the Unity game engine? Unity is a tool used by a large number of game developers in this booming industry for creating and powering their creations. In order to gain a clear understanding of Unity, you may enroll in different Courses in Game Development . For anyone who wishes to develop games for a living and have a solid career in game development, Unity software is a go-to powerful tool. Developers can use Unity as a powerful cross-platform game engine and create miracles out of it. Many of the most important built-in features that make a game work can be found in Unity. In other words, things like physics, 3D rendering, and collision detection. Developers can learn more about such features from Game Design Courses Online . Unity is unique in its own way, for instance, a new physics engine could be created from scratch rather than calculating every movement of each material or the w...
Read more

These 7 Questions will help you in an AR Developer Interview

-
Image
  Making your mark in Augmented Reality tech requires strong technical foundation, likely found only in a augmented reality course . With the growing popularity of both AR and VR technologies, their use in retail, marketing, entertainment and even BIM, and Training prove that the technology is the future of interactivity. Today's world has been revolutionized by technology. Smart devices such as TVs, baby bassinets, self-driving cars, and voice assistants such as Google Home are becoming more common in our everyday lives. Niantic’s (developer) mobile game Pokemon Go took the world by storm and if you’ve played it, you've encountered another branch of this emerging tech - Augmented Reality (AR). AR is one of the hottest job segments in today's job market due to thriving progress in the field. There are lots of opportunities for people to get on board the AR train now. They get to discover and develop innovative ways to craft solutions for long-standing problems. Preparation ...
Read more

Character Design in Video Games: Key Principles

-
Image
  V ideo Game Design is not just about a well-written plot and enjoyable gameplay but also requires excellent and engaging character development. Designers and authors create stories and motivations for characters. The character concept artist develops the initial sketches for the characters and adversaries, then the digital art assets that turn into animated things in the game environment created. Innovation is always welcome. Character designers can draw inspiration from the sources as video games get more innovative, bending genres and blending styles. In other words, there is no need for aspiring video game character artists to delay getting started. Today, you may enroll in various Digital Art Courses that will teach you how to create a game design language for your video game concept, sketch them out, and share them on social media or online portfolios.   Importance of Character Design Consider a game in which the main character is absent. Isn't that strange? A ...
Read more