Data Science used to forecast air quality

Data Science and analytics are being leveraged in various areas such as production and pharmaceutical. With climate change being such an important discussion in today’s world, people are finding ways to make use of Data Science to create air quality forecasts. Read further to know more about the latest advancements in the world of Data Science and Analytics.

How to move Data Science into production

Integrating Data Science into production still proves to be a challenge. It needs to be updated frequently and the data types change rapidly, making it limiting to rely solely on the framework and proprietary tools. KNIME focuses on delivering the latest Data Science developments. Data Scientists can access and combine data from repositories and apply preferred tools. With the help of KNIME workflow for production you will have access to various data sources and algorithms.

Data scientists’ global battle to create air quality forecast solution in Uganda

Over 400 experts from different countries such as India, Nigeria, Tanzania, Japan, and UAE have collected information to forecast air quality accurately. Accurate predictions of air quality over a short period allows the government to make informed decisions that can protect people’s health. AirQo, a Ugandan Air Quality Forecast challenge runs till 31st May 2020. The prize fund is a share of $5000. The solution will be put into practice in Uganda.

Data Science And Machine Learning. With Java?

As we all know, Open source programming languages such as Python and R have dominated Data Science for a few years. Before this, MATLAB was considered as a commercial programming language. Research shows that R is a statistical library ecosystem. Whereas Tensorflow, PyTorch are accessible from Python. What about Java, C++, and .NET? Java can be used for the development of almost everything due to its multitude of JVM languages. Java excels in secure data handling, transfer, and connectivity.

Artificial Intelligence and Data Science are rapidly growing. The use of Data Science in the Pharmaceutical industry has shown an increase. The report talks about key market opportunities, product segments, sales channels, key countries based on import/export. The market size, emerging trends, and investment risks of AI and Data Science within the pharmaceutical industry. It gives an overview of the industry in terms of value and volume. The report also gives insight into the international markets.

For more such weekly guides, you can check this space. If you wish to learn more, upskill with our PGP – Data Science and Engineering Program!