Microsoft in the News
For well over one hundred years, there have been people who preach doom and gloom when it comes to feeding all the people on this planet. There is little doubt that as our population grows exponentially, we will hit a wall where we simply cannot produce enough food and still maintain a diverse population of species. Technology keeps pushing that wall back.
Today, farmers are practicing digital agriculture. They use AI, machine learning, cloud computing, satellite imaging, IOT sensors, and smart phones in order maximize yields.
Always at the forefront, Microsoft has been collaborating with the International Crop Research Institute to develop an AI seed sowing app. To use this app, all you need is a phone that can receive text messages. Once signed up, the AI will track things like temperature, rainfall, and weather forecasts to calculate the best time to plant. Last year, the 174 farmers in India who joined the pilot project managed an average 30% higher yield than those who were not participating.
In addition to letting you know when to plant, other advisories like planting depth, fertilizer application and more were texted out to the participants.
The next step being worked on is pest risk prediction. This will enable farmers to plan in advance and apply pesticides when they will be most effective.
Climate change has wreaked havoc on traditional planting times, rainfall averages, pest intensity, and has generally made life more difficult for farmers. Microsoft is helping take the guesswork out of the equation and ensuring that more people are getting fed.
So you want to be a data scientist. Nobody could blame you for wanting to put that title on your CV. It is one of the most coveted and respected titles in the IT world today. And, truth be told, it is probably a whole lot easier than getting into AI.
Where to start? Depending on who you ask, the courses recommended may include statistics, Data Visualization, Data Wrangling, Computing Platforms, and the list goes on. What all these courses have at their core, is a computing language that you will use to perform the tasks each course asks of you. That language is usually Python. So, I would argue that a good place to start on the road to adding the title “Data Scientist” to your CV is to learn Python.
Python has become the default language for data scientists. Not only is it versatile (used for web development, networking, AI, games, apps, and virtually everything else) but it is easy to learn, and well suited to the number crunching that data science requires.
Named after the famous Monty Python series, this language has the philosophy that it should be fun to use, and easy to understand. This ease of use has led to it becoming the most popular introductory programing language at universities. The fun part means that learning it should not be a chore.
Python is a very high level language. That means that it reads like English, but the downside is that it is a relatively slow language. For those of you familiar with the English language, flexibility and dynamism means that context is very important (Is he a “winner!” or a “winner”?). Dealing with context (flexibility) requires computing resources and this slows things down. The votes are in and the upside far surpasses the downside. Python is enormously popular.
Some fantastic resources for you to get started include:
If you want to get a certificate saying that you really do know Python, there are introductory Python courses available through Coursera, or, I’m sure you can find a brick-and-mortar location near you.
Because big data is on every CEO’s mind, Python has become the second highest skill demanded by recruiters. With this demand comes salary pressure, so it is also the skill with the highest average salary offered.
To sum up, Python is in high demand, pays well, is easy to learn, fun to use, is a prerequisite to getting the coveted title of data scientist, and there are free or almost free on-line courses teaching it.
Tell me again what’s your excuse for not knowing how to program in Python?
(Full Disclosure: my excuse is that I’m too busy!)