Local information access (LIA) programs tap into existing public health data flows, and present data in simple and useful ways to ground staff. LIAs hold great potential for improving rural health systems in developing regions; benefits include more evidence-based decision making and optimizations at a local scale, as well as improved service delivery and data quality. Our fledgling LIA program in rural Uganda currently provides clinicians with a small set of static data visualizations for discussion. To increase the program’s effectiveness, we want to automatically identify relevant data visualizations. We propose an adaptive tool that learns from local clinicians’ decision-making processes to predict and generate visualizations that show actionable anomalies.

Data Science is considered as one of the most modern and fascinating jobs of our time. It can be funny and can give you satisfaction, but is it really as it's described? At the beginning of their career, Data Scientists think that Data Science is a wonderful, magical world full of algorithms, Python functions that performs every possible spell with a line of code and statistical models able to detect the most useful correlations among data that could make you an invincible superhero in your company. You start dreaming about your CEO congratulating with you and shaking your hand, you begin to see decision trees and clusters everywhere and, of course, the most terrifying neural network architectures your mind can dream. But since the very first day of your first Data Science project, you start to realize what reality is.

This article was originally posted here, by Mubashir Qasim. "In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization.

"In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization. And ultimately, the importance of data analysis applies not only to data science generally, but machine learning specifically.

This article was originally posted here, by Mubashir Qasim.