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Data Science


AI in Analytics: Powering the Future of Data Analytics - Dataconomy

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Augmented analytics: the combination of AI and analytics is the latest innovation in data analytics. For organizations, data analysis has evolved from hiring "unicorn" data scientists – to having smart applications that provide actionable insights for decision-making in just a few clicks, thanks to AI. Augmenting by definition means making something greater in strength or value. Augmented analytics, also known as AI-driven analytics, helps in identifying hidden patterns in large data sets and uncovers trends and actionable insights. It leverages technologies such as Analytics, Machine Learning, and Natural Language Generation to automate data management processes and assist with the hard parts of analytics. The capabilities of AI are poised to augment analytics activities and enable companies to internalize data-driven decision-making while enabling everyone in the organization to easily deal with data.


7 Things to Do Before Starting a Data Science Bootcamp

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I am a recent graduate of the Galvanize Data Science Immersive Bootcamp. In this Data Science Bootcamp we spent 3 months learning Statistics, Linear Algebra, Calculus, Machine Learning, SQL, and Python Programming. The San Francisco based program I attended was transferred from in-person to remote due to the COVID-19 pandemic. To say this experience was challenging would be an understatement. My official day at the Bootcamp started at 8:30 AM and ended at 8:30 PM Monday through Friday.


The Top 5 Data Science Qualifications

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As someone who has interviewed with several companies for Data Scientist positions, as well as someone who has searched and explored countless required qualifications for interviews, I have compiled my top five Data Science qualifications. These qualifications are not only expected to be required by the time of interview, but also just important qualifications to keep in mind at your current work, even if you are not interviewing. Data Science is always evolving so it is critical to be aware of new technologies within the field. These requirements may differ from your personal experiences, so keep in mind this article is stemming from my opinion as a professional Data Scientist. These qualifications will be described as key skills, concepts, and various experiences that are expected to have before entering the new role or current role.


2021's crystal ball: 6 AI predictions that will shape a new commercial model - MedCity News

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Alan Kalton, Vice President and General Manager of Aktana Europe, is a leader in data analytics and manages all new Contextual Intelligence implementations and developments across Europe. He comes to Aktana from Cape Town, South Africa where he led a data analytics venture called BroadReach and prior was the Analytics Leader of EY in South Africa. He also held prominent executive leadership positions in data analytics at IBM, Elsevier, Cognizant, Steris, Novartis, GSK, and ZS Associates. He graduated with a BS and MSc of industrial and operations engineering from the University of Michigan. Kalton can be reached at alan.kalton@aktana.com.


C and C++ Are Surprisingly Useful for Data Science Applications

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We recently heard from a number of C and C experts talk about its merits with data science. Cristiano L. Fontana of OpenSource.com talked about some of these benefits in a recent article. "While languages like Python and R are increasingly popular for data science, C and C can be a strong choice for efficient and effective data science. It is the language I use the most for number crunching, mostly because of its performance. I find it rather tedious to use, as it needs a lot of boilerplate code, but it is well supported in various environments. The C99 standard is a recent revision that adds some nifty features and is well supported by compilers."


Accelerating the Practical Use of AI - InformationWeek

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The hype for how artificial intelligence can miraculously change the world continues to fill media outlets. Still, the reality of how rapidly the science behind AI is evolving and becoming mainstream in every industry and facet of business will not be impeded. By the year 2025, the intersection of "advanced" AI and intelligent machines will become a part of every user's "things I just know how to use." As more industries adopt AI solutions and become savvy about how AI impacts their engagement with suppliers and employees, it is important for organizations to follow four key steps to implement it. While roles like data scientist, chief data officer, and senior data engineer are vital to implementing AI/ML systems, the two following roles are imperative for practical implementation.


AI changing dynamics of healthcare

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Globally, healthcare organisations have accelerated adoption of artificial intelligence (AI), with the ones still implementing frameworks planning to go live within 24 months. Hardly surprising given the improved consumer engagement that results from the technology. But more than that, the challenging economic climate is seeing healthcare organisations looking for better ways to make processes more efficient, enhance their existing products and services and lower costs. The key to this is AI that brings with it a more innovative environment to automate manual, error-prone processes and introduce a sophisticated layer of analytics that can deliver new insights to the wealth of data already available. These platforms use algorithms and machine learning to analyse and interpret data, while empowering the healthcare organisation with the means to provide more personalised customer experiences.


Data Science is Cremated in 2020. So, Is Business Science Gaining Spotlight?

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It is been so long since Harvard Business Review declared data science to be the sexiest job in 2012. Unfortunately, if we look back at how data scientist role is performing in the technology sector, it is more like the profession is slowly dying. Experts too think that the world is overrating data science professions throwing data at off-the-shelf algorithms. If we consider the'best jobs' ranking from 2017 to 2019, we see the data scientist role being dramatically losing its place. Data science played similar to'business analyst' position in the 2010s.


Machine Learning : Handle missing data

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In the previous blog post on Machine Learning, we saw how you can import a library into a GOOGLE COLAB and how you can run your first Machine learning program using the data in a CSV file. If you haven't read that post, here is the link. I urge you to read it first. In today's post on machine learning, I will explain how to work in your CSV file if there is no data/ missing data in a row, which means some of your rows contain blank space. I will explain this to you.


Global Big Data Conference

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According to the National Oceanic and Atmospheric Administration (NOAA), more than 80% of the ocean "remains unmapped, unobserved, and unexplored" – despite constituting more than 70% of the planet's surface. Now, a pair of Navy veterans are looking to change that with a line of autonomous robot vehicles that will plunge the ocean's depths in search of big data for the company's clients. "The company really started when Joe [Wolfel] and I first got together, which was back in 2004," said Judson Kauffman, who shares the CEO role with Wolfel, in an interview with Datanami. "We met in [Navy] SEAL training together, and ended up being assigned the same unit, and then went into combat together and became very close friends. There, they developed the idea for Terradepth, which "stemmed from some knowledge that we gained in the Navy" – really, Kauffman said, "just of how ignorant humanity is of what's underwater, what's in the sea." "It was shocking to learn how little we know, how little the U.S. Navy knew," he continued – and the more they dug into the issue after their time in the Navy, the more surprised they were.