We have established a new data science practice at Canonical. The team will innovate in the open source data science technology stack, deliver advanced business analytics, support product roadmap decisions for Canonical through actionable insights, and lead by example in setting and publicly advocating for industry standards in open source data science. The team will have both Data Scientists and Data Engineers, apply here if you are most excited about the Data Engineer role! As a Data Engineer at Canonical you will act as a technical expert in an exciting field at the intersection of data engineering, data science, and machine learning technologies, with particular emphasis on the open source ecosystem of Canonical and Ubuntu. You will drive the organisation, instrumentation, ingestion, and transformation of data from a wide range of sources in the company.
Always looking for an easy compromise, attackers are now scanning for data-science applications -- such as Jupyter Notebook and JupyterLab -- along with cloud servers and containers for misconfigurations, cloud-protection firm Aqua Security stated in an advisory published on April 13. The two popular data science applications -- used frequently with Python and R for data analysis -- are generally secure by default, but a small fraction of instances are misconfigured, allowing attackers to access the servers with no password, according to the Aqua Security's researchers. In addition, after setting up its own server as a honeypot, the company detected in-the-wild attacks that attempted to install cryptomining tools and ransomware onto accessible instances of the software. Signs that there are attackers targeting data-science environments is worrisome, considering that the researchers setting up those environment are largely uninformed about cybersecurity, says Assaf Morag, lead data analyst with Aqua Security. "We know, based on our experience with application security, that developers are starting to learn more about security, but what about data scientists?" he says.
Artificial Intelligence at UCI and Beyond explores the transformational branch of computer science that is increasingly influencing the ways we live and work. With applications in transportation, healthcare, business, and more, AI-enabled systems are seeking to enhance human capabilities. From autonomous vehicles to business intelligence, this exhibit showcases some of the AI technologies and research models allowing computer systems to learn from experience, operate autonomously, and perform human-like tasks. In addition to highlighting key historical figures and milestones, the exhibit showcases selected examples of AI researchers and initiatives at UCI. It also spotlights the ethical challenges, biases, and privacy concerns raised by the widespread use of AI systems. Artificial Intelligence at UCI and Beyond was curated by Danielle Kane, Computational Research Librarian, edited by Cheryl Baltes and Gaby Camacho, and designed by Allan Helmick, Sylvia Irving, and Luisa Lee.
If you are new to data science, the programming projects will help you get used to syntax, debugging, and learning new tools. Python, R, and Julia are mostly used for data processing, data analysis, machine learning, and research projects. Web scraping is a core part of data engineering and data science, where you collect new data from multiple websites to build a data set for data analysis or machine learning tasks. In general, it is used to create real-time data systems. The analytics project will teach you new tools for data cleaning, processing, and visualization.
Data science is a branch of information technology that deals with the analysis and processing of large volumes of data, which may be structured or unstructured or a mix of both, in order to find unseen patterns and derive meaningful information. Data science is useful to identify market opportunities, for process optimisation and cost reduction, and to identify abnormal financial transactions, among others. A typical project involves components that require expertise from several of these areas in combinations of varying proportions from one project, to another. As technology finds its way into all our daily activities, so do the digital data trails we leave behind, be it at retail outlets, banks and many other places. Many organisations have realised they are sitting on a veritable gold mine of information that they can capitalise on and put to good use.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Let me start this blog by clarifying that I do not consider myself a data scientist nor a technical expert, but I have gained a pragmatic perspective on the various roles in this space through my experiences in leading AI & data science projects and building up and managing teams of data scientists and analytics professionals.
Complex emerging technologies such as artificial intelligence, machine learning and "big data" analysis will be used to create the leading HR organizations of the future, and employers must be willing to invest the time and effort to use these powerful tools responsibly. But that means first getting over the fear of what could go wrong and instead resolving to harness technology's power to better inform decision-making and revolutionize talent management. SHRM Online discussed the critical future-of-work topic with Eric Sydell, an industrial organizational psychologist, expert in AI and machine learning, executive vice president of innovation at recruiting technology firm Modern Hire, and co-author of the new book Decoding Talent (Fast Company, 2022). SHRM Online: People often react to leading-edge technology with trepidation. In the case of using AI in the workplace, government regulators are placing well-intentioned limits on data usage because they fear employers may abuse employee privacy and workers could suffer harm from bias.
Then one year passed and I finally get a job as a data scientist. I was pretty euphoric, I imagined "oh my god I finally will do some data analysis, create a model and put something to production servers". Let's discuss why maybe data science could not be a good choice for you. One of the most important characteristics of a data scientist is having good communication and knowledge about the business. We need to help stakeholders to develop products and observe metrics.
Data forms the foundation of any machine learning algorithm, without it, Data Science can not happen. Sometimes, it can contain a huge number of features, some of which are not even required. Such redundant information makes modeling complicated. Furthermore, interpreting and understanding the data by visualization gets difficult because of the high dimensionality. This is where dimensionality reduction comes into play. Dimensionality reduction is the task of reducing the number of features in a dataset. In machine learning tasks like regression or classification, there are often too many variables to work with. These variables are also called features.
Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion.