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More data is being collected now than ever before in human history. Data science the field of analyzing, organizing, and gleaning insights from that data is becoming increasingly important to the governments and private companies that collect this data.

Here are some of the developing trends in data science that will shape the field this year and beyond.

Python has developed a reputation as one of the most versatile and powerful programming languages in use. This is for good reason. Pythons popularity is rooted in its simple and accessible syntax along with its statistical and analytical visualizations. It also has massive support in the form of a dedicated online community.

Python is set to become the go-to programming language for data science. Why? Because the object-oriented programming (OOP) concept is ideal when dealing with large datasets. Additionally, the aforementioned simple syntax allows programmers to accomplish a great deal with only a few lines of code.

Cybersecurity continues to be a major concern, with cybersecurity attacks up worldwide. More private and sensitive data is being collected than ever before. While the world will likely always need dedicated cybersecurity experts, artificial intelligence is starting to pick up some of the load.

AI cybersecurity takes some of the burdens of human cybersecurity experts. It does this by processing large amounts of data faster than humans can. AI can detect potential security threats, vulnerabilities in code, and other suspicious activities. It can also use predictive analysis to address security threats before they start.

AI can also be used to address typical weak points in network security such as weak passwords. It does this by integrating security measures such as biometrics or facial recognition.

With the ever-increasing volume of data being collected, driven partially by the Internet of Things, there is more demand than ever for skilled data scientists.

Despite its unique strengths, AI cannot handle every aspect of data science. Data scientists are needed to sort and organize much of that data before it can be meaningfully analyzed by AI. Someone looking to pursue a data science degree is likely to find themselves with a promising array of career options going forward.

Blockchain is an emerging technology that uses decentralized nodes of information to create secure, validated chunks of data. These chunks cant be tampered with, manipulated, or falsified.

Blockchain technology is poised to disrupt certain aspects of data science as both deal fields deal with large amounts of data. While its yet to be fully explored, theres a developing trend toward integration between blockchain and data science. This typically relies on blockchain primarily for data integrity and security. Data science, for its part, emphasizes prediction and actionable insights.

More companies will migrate their data and services to the cloud. This represents an attempt to cut investment costs and increase revenue.

Data science, by nature, requires massive amounts of data. Moving the means to process and store it to the cloud frees up local resources and reduces operating costs. Cloud providers offer pay-as-you-go resources such as databases, storage, and runtime.

Already important to the field, data visualization tools are becoming an ever more vital part of data science.

Visualization provides the key to identifying patterns, finding outliers, gleaning insight, and otherwise gaining an understanding of large amounts of data. Not only is this important to data scientists themselves but its also critical in helping present conclusions and insights to stakeholders and clients. Graphical tools, maps, graphs, charts, and other visualization techniques and the tools to help create them will play an increasing part in the application of data science.

Low-code and no-code platforms are creating some beneficial disruption in the software field.

LCNC platforms increase the accessibility of software solutions by creating application-development platforms that use intuitive, easy-to-use interfaces. These allow users to work without having much (or any) programming experience. Using a no-code platform, a user could create an application using drag-and-drop menus. That user could also use simple interfaces to build an application without having to write any code at all.

MLOps seeks to promote the best practices for using AI in data science and business.

A developing field just starting to get attention, MLOps grew out of DevOps. Its now set to help machine learning become an everyday part of mainstream business and data science. Data scientists are using MLOps to build efficient AI models and curate datasets in precise, disciplined ways. This practice will help create more robust AI and machine learning models that scale and evolve with changing needs.

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8 Data Science Trends to Watch This Year The Tech Report - The Tech Report