As we’ve established earlier in this post series, Data Science is a process, with quite a lot of repetitive elements. Many Data Science projects involve a familiar set of tasks to identify, clean and prepare data, before finding the best model for the scenario at hand. And despite the mystique around the whole profession, many Data Scientists spend a lot of time complaining about all this repetitive work. But any repetitive process is ripe for automation, and Data Science is no exception. Enter the field of “AutoML”.
As I mentioned in my first post in this series, the central purpose of Data Science is to find patterns in data and use these patterns to make useful predictions about the future. It’s this predictive part of Data Science which gives the discipline its mystique; even though Data Scientists actually only spend a relatively small fraction of their time on this area compared to the more workaday activities of loading, cleaning and understanding the data, it’s the step of building predictive models which unlocks the value hidden within the data.
Ask any Data Scientist and they will tell you that the process of ‘wrangling’ (loading, understanding and preparing) data represents the lion’s share of their workload – often up to as much as 80%. However, that number is not as alarming as it may at first seem. To understand why, let me tell you about my living room.
Ask an Analyst, particularly a Digital Analyst, how they’d like to develop their career, and they are quite likely to tell you that they want to get into Data Science. But in fact the two disciplines (if they can even be described as separate disciplines) overlap considerably – some would even say completely. So what is the difference between Analytics and Data Science?
There’s a lot of buzz about Data Science these days, and especially its super-cool subfield, Machine Learning. Data Scientists have become the unicorns of the tech industry, commanding astronomical salaries and an equal amount of awe (and envy) to go with them. Partly as a result of this, the field has developed something of a mystical aura – the sense that not only is it complex, it’s too complex to explain to mere mortals, such as managers or business stakeholders.
It’s true that mastery of Data Science involves many complex and specialized activities, but it’s by no means impossible for a non-Data Scientist to build a good understanding of the main building blocks of the field, and how they fit together.
Congratulations! You just got the call – you’ve been asked to start a data team to extract valuable customer insights from your product usage, improve your company’s marketing effectiveness, or make your boss look all “data-savvy” (hopefully not just the last one of these). And even better, you’ve been given carte blanche to go hire … Read more
Five years ago, my worldly possessions gathered together in a knotted handkerchief on the end of a stick, I set off from the shire of Web Analytics to seek my fortune among the bright lights of online advertising. I didn’t exactly become Lord Mayor of London, but the move has been a good one for … Read more
There’s been quite the hullabaloo since Google announced last week that it was going to send signed-in users to Google Secure Search by default. Back when Google first announced Secure Search in May, there was some commentary about how it would reduce the amount of data available to web analytics tools. This is because browsers … Read more
By now, almost 12 hours after the announcement, you’ll have heard the news that Adobe is to buy Omniture for $1.8bn. If you haven’t heard, then, I mean, duh. It’s all over Twitter, dude: (As an aside, the guys at Omniture should be proud of themselves that they managed to beat out Joe Wilson as … Read more
I have just noticed (rather belatedly, to say the least) that Laura Lee Dooley has posted a complete video of my encounter with Avinash Kaushik at the May E-metrics Summit in San Jose on Vimeo. The sound quality is a little poor, but you can more or less follow the thread of the conversation. I … Read more