After consuming hundreds of books, several notes about Data Science and have viewed several videos of Data Scientists sharing their experience. You have all the theoretical knowledge you need to know for becoming a Data Scientists. But are you a Data Scientist now? The next big step is to start applying the concept, think differently and how you can do that is either find real-world problems of fields in which you are interested in or you can take participate in Hackathons and Machine learning Competitions. Hackathons are efficient and new means of hiring professionals in aspects of machine learning, Artificial Intelligence and data science.
There's a stereotype that nonprofits are outdated when it comes to technology. However, many are proving that assumption wrong, as nonprofits use big data in their organizations regularly. Here are five ways organizations can do that even if they don't have extensive financial resources. Indeed, it can be costly to buy a big data platform and use it to collect data about a nonprofit. That's why some decide to tap into big data sources that already exist.
We've all heard of Kaggle, but that also means there's more competition -- recently, Kaggle reached 5 million users. Further, not all competitions are open to everyone in the world. "Members of the Kaggle community who are not United States Citizens or legal permanent residents at the time of entry are allowed to participate in the Competition but are not eligible to win prizes. If a team has one or more members who are not prize eligible, then the entire team is not prize eligible." By trying out other competition platforms, you can be a "big fish in a small pond," as there are a lot fewer competitors.
Data scientists need to assemble predictive analytics workflow benefits to review on processes and algorithms. Data science techniques and tools are constantly evolving and only online resources can aid data scientists to keep up the pace. Even though when traditional models like reading books and journals give the understanding of fundamental concepts that benefit data scientists on a large-scale. The need for a community of experts to support the work of a data scientist has ignited a number of forums and groups where people seek help online. Henceforth, Analytics Insight is bringing a list of top 10 data science communities that professionals can take part in.
The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown.