Machine Learning

If you know anyone passionate about Microsoft Excel, then you know conversations about its uses can get emotional very quickly. People who’ve mastered every plugin and pivot table sometimes have a hard time believing there’s anything Excel can’t do. This  podcast , titled “A Beehive of Excel Devotees” by Roger Peng and Hilary Parker, is a perfect illustration of that mentality. It underscores a point that is missing in most discussions: you need to pick the right tools for the job at hand. Sometimes, Excel is the right tool. Sometimes, it isn’t. If you’re looking to build a career as a business analyst or data scientist, having a plethora of tools in your back pocket as well as the skill set needed to use them, is a must. And it’s okay to use Excel. It is okay. Excel is a little tool that is suited to fulfill many needs. But professionals working with data must also understand that it’s not the best solution for every task. Let’s dive deeper and look at three scenarios in which Excel m

Access to relevant customer and industry information is the primary competitive advantage businesses have over their direct and indirect competitors today. It’s the smartest approach to remaining vigilant in a business environment where competition is at an all-time high. That’s where data warehousing comes in. Data warehouses are central repositories of integrated data from one or more disparate sources used for reporting and data analysis, which—is an enterprise environment—supports management’s decision-making process. Digitalization is integrated into the foundations of today's business landscape, and there is no going back from here. Software companies are improving data engineering algorithms, and data analytics providers are using advanced techniques to provide better solutions to businesses. The result is much more efficient business intelligence solutions. Businesses who are new to this trend and are skeptical about the availability of the data often inquire, “why do we ne

  Mathematics is an integral part of data science. Any practicing data scientist or person interested in building a career in data science will need to have a strong background in specific mathematical fields.  Depending on your career choice as a data scientist, you will need at least a B.A., M.A., or Ph.D. degree to qualify for hire at most organizations. A significant portion of your ability to translate your data science skills into real-world scenarios depends on your success and understanding of mathematics. Data science careers require mathematical study because machine learning algorithms, and performing analyses and discovering insights from data require math. While math will not be the only requirement for your educational and career path in data science, but it’s often one of the most important. Identifying and understanding business challenges and translating them into mathematical ones is widely considered one of the most important steps in a data scientist’s workflow. Wil

Machine learning has created significant advancements for industries and set the pace for a future built on artificial intelligence (AI) technology. The endless possibilities and technological capabilities that machine learning has brought to the world have simultaneously created new security risks that threaten progress and organizational development.  Understanding machine learning security risks is one of our current technological time's most important undertakings because the consequences are extremely high, especially for industries such as healthcare where lives are on the line.  Let’s first discuss the types of machine learning security risks that you can encounter so that you can be better prepared to face them head-on.  Types of Machine Learning Security Risks Since machine learning uses data, this accounts for a substantial part of the security risks. However, dozens of risks are associated with machine learning that can potentially threaten systems and reduce positive ou

When you do a search on the internet and immediately afterwards an advertisement appears related to what you searched for, it is not by chance.  This happens thanks to an area called “machine learning”, popularly known as “machine learning”.  It allows computer systems to learn to recognize and develop data-based patterns.  In advertising, machine learning technique s are used to analyze the history of a specific target audience, identify patterns, recognize preferences and show specific ads to those users. What makes machine learning so important today is its ability to perform intelligent tasks autonomously, helping to reduce time and increase assertiveness in decision making.  According to Radix data scientist Raul Sena, intelligent data analysis is a global trend in business, as it can positively impact a company's resource use: - With machine learning it is possible to automatically analyze a larger volume of complex data and present more accurate indicators for specialists.