Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. A data scientist's main objective is to organize and analyze large amounts of data, often using software specifically designed for the task. The final results of a data scientist's analysis needs to be easy enough for all invested stakeholders to understand -- especially those working outside of IT. Get the insights by signing up for our newsletters.
So, you think you can be a data scientist. But, are you sure you have it what it takes to excel in the data science field? It's a very complicated field, and getting competitive day by day. In this post, we will go through what the industry demands of a modern data scientist in the real world, how to become a data scientist, top platforms and resources to learn the data science skills, and career advice & job search tips from data science experts. The data scientist job is definitely one of the most lucrative and hyped job roles out there. More and more businesses are becoming data-driven, the world is increasingly becoming more connected and looks like every business will need a data science practice. So, the demand for data scientists is huge. Even better, everyone acknowledges the shortfall of talent in the industry. But, becoming a data scientist is extremely complicated and competitive. The career path of a data scientist is not going to be easy.
Already crowned as the best job in America for 2016, the definition and skill set required to be a data scientist is in a constant state of flux. In this article, we take a closer look at the role of a Data Scientist in 2016. Dave Holtz writes that the title'data scientist' is often used as a blanket title to describe a set of jobs that are drastically different. He attributes this to the fact that the field of data science is still in its infancy and so is ill-defined. Adopting the all-encompassing sub-title of being part of an'interdisciplinary field', a data scientist works to extract knowledge or insights from large volumes of data in various forms.
As Frank Newport, senior scientist at Gallup, expressed it: "Whether they know it or not, AI has moved into a big percentage of Americans' lives in one way or another already." Newport made the comment in light of the results of a 2018 Gallup consumer survey. It found almost nine of ten (85%) US adults use at least one service regularly that features some element of artificial intelligence. Almost half (47%) said they used smartphone personal assistants while 32% use ride-sharing apps, such as Uber and Lyft. Twenty-two percent have home personal assistants, such as Alexa and Google Home, and 20% use smart home devices, such as smart lights or smart thermostats.
Data science is a combination of various machine learning principles along with tools and algorithms to analyze raw data and conclude hidden patterns or predictions. Data science does not only provide predictive casual analytics and perspective analytics but also machine learning for making predictions and pattern discovery. With these complex and meaningful analytics, it finds the critical insights out of anything that can help to enhance the value. There are a huge number of blogs that talk about all these data science projects and helps to enlighten its users about the new technology. Data science is an evergrowing field of computer science, and it is difficult to keep pace with the trendy additions all the time. The below-mentioned blogs of data science will help you to keep updated and stay ahead in the competition. After acquiring Datascence.com back in 2018, Oracle started focusing on the utilization of Machine learning for its customers. Oracle always wanted to enable people to leverage the power of AI with the combination of big data and data analytics. This big data blog can be seen as a part of this goal as it emphasizes the impact of big data and AI on various applications of our regular life. Besides, how we can transform the data catalog to get more insight from a business alongside the extraction of business value is discussed in Oracle AI and Data Science Blog. If you are planning to start your career in this field, you can follow this blog as you will get everything that you must understand to become a data scientist in 2020. This Belgium based data science community is publishing big data-related content to minimize the gap between data science and common people since 2015. The blogs are available for free, and you will get all of them in their archives. They are intended to generate solutions for the challenges that we face in our day-to-day life through data analytics. It can be seen as a bridge between academics and business as it highlights the power of big data and the value it can add to any business. NGO workers, business leaders, data enthusiasts, university professors, and also Ph.D. students share their skills and experiences through this blog.