Coursera: Old problems, new problems, Data Science solutions.

3 years ago
Organizations can leverage the almost unlimited amount of data now available to them in a growing number of ways. However, all organizations ultimately use data science for the same reason—to discover optimum solutions to existing problems. Let’s take a look at three examples of data science providing innovative solutions for old problems. In transport, Uber collects real-time user data to discover how many drivers are available, if more are needed, and if they should allow a surge charge to attract more drivers. Uber uses data to put the right number of drivers in the right place, at the right time, for a cost the rider is willing to pay. In a different transport related data science effort, the Toronto Transportation Commission has made great strides in solving an old problem with traffic flows, restructuring those flows in and around the city. Using data science tools and analysis, they have: Gathered data to better understand streetcar operations, and identify areas for interventions Analyzed customer complaints data, Used probe data to better understand traffic performance on main routes and created a team to better capitalize on big data for both planning, operations and evaluation By focusing on peak hour clearances and identifying the most congested routes, monthly hours lost for commuters due to traffic congestion dropped from 4.75 hrs. in 2010 to 3 hrs. in mid-2014. In facing issues in our environment, data science can also play a proactive role. Freshwater lakes supply a variety of human and ecological needs, such as providing drinking water and producing food. But lakes across the world are threatened by increasing incidences of harmful cyanobacterial blooms. There are many projects and studies to solve this long-existing dilemma. In the US, a team of scientists from research centers stretching from Maine to South Carolina is developing and deploying high-tech tools to explore cyanobacteria in lakes across the east coast. The team is using robotic boats, buoys, and camera-equipped drones to measure physical, chemical, and biological data in lakes where cyanobacteria are detected, collecting large volumes of data related to the lakes and the development of the harmful blooms. The project is also building new algorithmic models to assess the findings. The information collected will lead to better predictions of when and where cyanobacterial blooms take place, enabling proactive approaches to protect public health in recreational lakes and in those that supply drinking water. Such interdisciplinary training prepares the next generation of scientists to address societal issues with the proper modernized data science tools. It takes gathering a lot of data, cleaning and preparing it, and then analyzing it to gain the insight needed to develop better solutions for today's enterprises. How do you get a better solution that is efficient? You must: Identify the problem and establish a clear understanding of it. Gather the data for analysis. Id