Data is transforming many different job functions, whether you work in recruiting or sales or marketing or manufacturing or agriculture, data is probably transforming your job function. What's happened in the last few decades is the digitization of our society. So, rather than handing out papers surveys like these, surveys are more likely to be done in digital format, or doctors still write some handwritten notes , but doctors handwritten note is increasingly likely to be a digital record and so to this in just about every single job function. This availability of data means that there's a good chance that your job function could be helped with tools like data science or machine learning. Let's take a look. In this video, I want to run through many different job functions and discuss how data science and machine learning can or will impact these different types of jobs. Let's start with sales. You've already seen in the last video how data science can be used to optimize a sales funnel. How about machine learning? If you're a salesperson you may have a set of leads about different people that you could reach out to to convince them to buy something from your company. Machine learning can help you prioritize these leads. So, you might want to prioritize calling up the CEO of a large company rather than the intern at a much smaller company and this type of automated leads sorting is making salespeople more efficient. Let's look at more examples. Let say you're a manufacturing line manager. You've already seen how data science can help you optimize a manufacturing line. How about machine learning? One of the steps of this manufacturing process is the final inspection. In fact today, in many factories there can be hundreds or thousands of people using the human eye to check over objects, maybe coffee mugs, maybe other things to see if they're scratches or dents and that's called inspection. So, machine learning can take this input, a dataset like this, and learn to automatically figure out if a coffee mug is defective or not. By automatically finding scratches or dents, it can reduce labor costs and also improve quality in your factory. This type of automated visual inspection is one of the technologies that I think will have a big impact on manufacturing. This is something I've been working on myself as well. Let's see more examples. How about recruiting? When recruiting someone to join your company, there may be a pretty predictable sequence of steps where your recruiter or someone else would send an email to a candidate and then you'd have a phone call with them, bring them on-site for an interview and then extend an offer and maybe close the offer. Similar to how data science can be used to optimize a sales funnel, recruiting can also use data science to optimize a recruiting funnel and in fact many recruiting organizations are doing so today. For example, if you find that hardly anyone is making it from the phone screen step to the on-site interviews step then you may conclude that maybe too many people are getting into the phone screen stage or maybe the people doing the phone screen are just being too tough and you should let more people get to the onsite interview stage. This type of data science is already having an impact on recruiting. What about machine learning projects? Well, one of the steps of recruiting is to screen a lot of resumes to decide who to reach out to. So, you may have to look at one resume and say, "Yes, let's email them", look at a different one to say, "No, let us not move ahead with this candidate." Machine learning is starting to make its way into automated resume screening. Does raise important ethical questions such as making sure that your AI software does not exhibit undesirable forms of bias and treats people fairly, but machine learning is starting to make inroads into this and I hope can do so while making sure that the systems are ethical and fair. In the final week of this AI For Everyone course, you'll also learn more about the issues of fairness and ethics in AI. What if you work in marketing? One of the common ways to optimize the performance in a website is called AB testing, in which you launch two versions of website. Here version A has a red button, version B has a green button and you measure which websites causes people to click through more. So with this type of data, a data science team can help you gain insights and suggests hypotheses or actions for optimizing your website. How about machine learning and marketing? Today a lot of websites will give customized product recommendations to show you the things you're most likely to want to buy and this actually significantly increases sales on these websites. For example, a clothing website after seeing the way I shop after while, will hopefully just recommend blue shirts to me because that's frankly pretty much the only type shirt I ever buy, but maybe other customers will have more diverse and more interesting recommendations than mine. But today these customized product recommendations actually drive a large percentage of sales on many large online e-commerce websites. One last example from a totally different sector. Let's say you work in agriculture. Maybe you're a farmer working on the light industrial farm, how can data science help you? Today farmers are already using data science for crop analytics, where you can take data on the soil conditions, the weather conditions, the presence of different crops in the market and have data science teams make recommendations to what to plant, when to plant so as to improve use while maintaining the condition of the soil on your farm. This type of data science is and will play a bigger and bigger role in agriculture. Let's also look at the machine learning example. I think one of the most exciting changes to agriculture is precision agriculture. Here's a picture that I took on a farm with my cell phone. On the upper right is a cotton plant and shown in the middle is a weed. With machine learning, we're starting to see products that can go onto the farms, take a picture like this and spray a weed killer in a very precise way just onto the weeds so that it gets rid of the weed but without having to spray an excessive amount of weed killers. This type of machine learning technology is both helping farmers increase crop yields while also helping to preserve the environment. In this video, you saw how all of these job functions, everything from sales, recruiting to marketing to manufacturing to farming agriculture, how all of these job functions are being affected by data, by data science and machine learning. It seems like there's a lot of different things you could do with AI. But how do you actually select a promising project to work on? Let's talk about that in the next video.
