There are a ton of online courses that try to sell the idea that "Learn Data Science in 10 weeks or less," "Cracking Data Science Interviews and earn you first million." This type of clickbait content is ubiquitous on the web, and beginners may be lost in the way of searching for the real stuff. So, my biggest writing motivation is to democratize Data Science by creating authentic content that is accessible to everyone. My writing strategy is to do a thorough "literature review" of the existing works, spot the incremental value (i.e., the gap), and write a series of original posts on the topic. Medium is the best writing platform that supports content writing.

Data Analytics and Mining is often perceived as an extremely tricky task cut out for Data Analysts and Data Scientists having a thorough knowledge encompassing several different domains such as mathematics, statistics, computer algorithms and programming. However, there are several tools available today that make it possible for novice programmers or people with no absolutely no algorithmic or programming expertise to carry out Data Analytics and Mining. One such tool which is very powerful and provides a graphical user interface and an assembly of nodes for ETL: Extraction, Transformation, Loading, for modeling, data analysis and visualization without, or with only slight programming is the KNIME Analytics Platform. KNIME, or the Konstanz Information Miner, was developed by the University of Konstanz and is now popular with a large international community of developers. Initially KNIME was originally made for commercial use but now it is available as an open source software and has been used extensively in pharmaceutical research since 2006 and also a powerful data mining tool for the financial data sector. It is also frequently used in the Business Intelligence (BI) sector.

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. They are focused on educating and empowering people while the scholar and professionals are also included among their target audience. 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.

The most progressive, the most cutting-edge, the most exciting… Data science and machine learning are those areas nowadays that are enormously appealing and hot, hot, super-hot topics. But to stay tuned with all the advances and movements in these fields, you need to put lots of effort -- researching, reading, checking all the information, news, guides, and other stuff. This task is far away from being an easy solution. Right now, you can stumble upon a bunch of places with vivid titles and promising headlines, but are they useful enough? Every day I see a crazy flow of information, and, unfortunately, there are lots of false or worthless stuff, and especially on data science and ML.

Artificial intelligence has grown smarter putting people in awe with a question, "Is my job safe?" It is but a simple question with a rather perplexing answer, I'm not skilled ready. Your view will depend on whether you'll be able to develop skills that will surpass the redundant skills you possess today.No doubt, the AI domain is thriving and humans are scared.

The task of understanding a customer's journey and designing your marketing strategy accordingly can be difficult in this data-driven world. Today, the customer expresses their needs in myriad forms of requests. Consumers express their needs and want attitudes, and values in various forms through search, comments, blogs, Tweets, "likes," videos, and conversations and access such data across many channels like web, mobile, and face to face. Volume, variety, velocity and veracity of the data accumulated through these customer interactions are huge. BigData and data analytics can be leveraged to understand several phases of the customer journey.

An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing.

Facebook Data Science Blog, the official blog of interesting insights presented by Facebook data scientists. Your bridge to careers in Data Science and Data Engineering Information is Beautiful, by Independent data journalist and information designer David McCandless who is also the author of his book'Information is Beautiful'. Large Scale ML & other Animals, by Danny Bickson, started the GraphLab, an award winning large scale open source project. Perpetual Enigma by Prateek Joshi, a computer vision enthusiast writes question-style compelling story reads on machine learning. Facebook Data Science Blog, the official blog of interesting insights presented by Facebook data scientists.

This post was contributed by Chelsea Douglas, a Software Engineer at Plotly. Want to play with the code from this post? I recently had the chance to team up with Domino Data Lab to produce a webinar that demonstrated how to use Plotly to create data visualizations inside of Domino notebooks. In this post, I'll share a few of the benefits that I discovered while using Plotly and Domino together. Plotly is a web-based data visualization platform for data scientists and engineers.

This guest post was written by Daniel Emaasit, a Ph.D Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel's research interests include the development of probabilistic machine learning methods for high-dimensional data, with applications to urban mobility, transport planning, highway safety, & traffic operations. Don't miss Daniel's webinar on Model-Based Machine Learning and Probabilistic Programming using RStan, scheduled for July 20, 2016 at 11:00 AM PST. This blog post follows my journey from traditional statistical modeling to Machine Learning (ML) and introduces a new paradigm of ML called Model-Based Machine Learning (Bishop, 2013). Model-Based Machine Learning may be of particular interest to statisticians, engineers, or related professionals looking to implement machine learning in their research or practice.