What is Data Science and Machine Learning?

Welcome! My name is Garry. In this post, we'll unpack the what, why, and how of data science.

By the end of this post, you'll have a better grasp of how data is being used around you and how you can use data.

If we Google "What is data science?", we'll see a huge amount of confusing information.

But data science is actually simple. It's a set of methodologies for taking in thousands of forms of data that are available to us today and using them to draw meaningful conclusions. Data is being collected all around us. Every like, click, email, credit card swipe, or tweet is a new piece of data that can be used to better describe the present or better predict the future.

What can data-driven decisions do for your marketing?

So what can data do? Data can describe our current state, as our energy consumption.

This can be accomplished with dashboards or alerts, simplifying time-intensive reporting processes.

It can help detect anomalous events, such as fraudulent purchases. If we have data on what has happened previously, we can increase efficiency by automatically detecting a new event that is unexpected or abnormal.

Data can also diagnose the causes of observed events and behaviors, for instance, your activity on Spotify or Netflix.

Rather than determining correlations between small numbers of events, data science techniques help us understand complex systems with many possible causes.

Finally, data can predict future events, such as forecasting population size.

We can use new techniques to take various causes into account and predict potential outcomes.

Further, we can evaluate the probability of our prediction mathematically to clarify our level of uncertainty.

Why is Data Science and Machine Learning so popular now?

So now we know what data science is. The next question is why is it so popular?

The answer is pretty obvious: we're collecting more data than ever before. Suppose that you visit a car dealership and fill out some information.

All of that data is automatically entered into a computer, and combined with the data from hundreds of dealerships into one big database.

Once we have that data, it's easy to use the email address that you provided when you bought that car to tie your car purchase data with your data from social media or web browsing.

Suddenly, we have a very complete picture of everyone who purchased a car in the last year: their ages, their likes, and dislikes, their friends and family.

This additional data can be used to predict what price you can pay for your car, what other purchases you're likely to make, or how best to sell you insurance for that new car.

Data is everywhere, and it is incredibly valuable information for businesses, organizations, and governments.

How does the data science workflow work with Marketing?

So, how do we start to use data?

In data science, we generally have four steps to any project.

First, we collect data from many sources, such as surveys, web traffic results, geotagged social media posts, and financial transactions.

Once collected, we store that data in a safe and accessible way.

At this point, data is in its raw form, so the next step is to prepare data. This includes "cleaning data", for instance finding missing or duplicate values, and converting data into a more organized format.

Then, we explore and visualize the cleaned data. This could involve building dashboards to track how the data changes over time or performing comparisons between two sets of data.

Finally, we run experiments and predictions on the data. For example, this could involve building a system that forecasts temperature changes or performing a test to find which web page acquires more customers.