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What's the difference between 'Data Analytics' and 'Data Analysis'? And, come to that, 'Business Intelligence'?
Used to be called statistical analysis, or business analysis, or maintenance analysis.
There are two basic approaches, or questions that CEOs and managers are going to ask of analytics. The first deals with past and present, the second deals with the future.
For the first, the question is, "What have we been doing right or wrong in the past and present?" You have to select the area you want to measure, you have to collect the appropriate data over time. You have to select the correct statistical tools for analysis. And you have to present that analysis, conclusions, and recommendations in a way that managers can understand. I've had managers order me to collect comparison data on things that I had proved had no relationship to each other for years just for them to be able to say they were analyzing it. Sometimes those orders were based on government requirements. (Small wonder I have such a low opinion of government regulation and enforcement.) Past and present analysis is therefore a Quality Assurance and Maintenance function.
The second question set deals with predictions of the future. You still need past data; but you do trending to find a mathematical model that allows you to make as accurate an estimate as possible of what's going to happen. Quality improves, stocks go up, bottom line fails, we lose experience to competitors as such and such a rate, etc. That's were we all hope the guy or gal in the hot seat makes the right choices.
Grace Simrall is missing two critical elements in her course list: class(es) on Logic, and class(es) on Ethics.
There are two basic approaches, or questions that CEOs and managers are going to ask of analytics. The first deals with past and present, the second deals with the future.
For the first, the question is, "What have we been doing right or wrong in the past and present?" You have to select the area you want to measure, you have to collect the appropriate data over time. You have to select the correct statistical tools for analysis. And you have to present that analysis, conclusions, and recommendations in a way that managers can understand. I've had managers order me to collect comparison data on things that I had proved had no relationship to each other for years just for them to be able to say they were analyzing it. Sometimes those orders were based on government requirements. (Small wonder I have such a low opinion of government regulation and enforcement.) Past and present analysis is therefore a Quality Assurance and Maintenance function.
The second question set deals with predictions of the future. You still need past data; but you do trending to find a mathematical model that allows you to make as accurate an estimate as possible of what's going to happen. Quality improves, stocks go up, bottom line fails, we lose experience to competitors as such and such a rate, etc. That's were we all hope the guy or gal in the hot seat makes the right choices.
Grace Simrall is missing two critical elements in her course list: class(es) on Logic, and class(es) on Ethics.
It's too bad that the author couldn't bother to tell us what Data Analytics is, or are we supposed to know that in order to read this article?
Agree totally with the other comments, so what is Data Analytics and what is new about analyzing data?
A quick search provided the following information.
http://searchdatamanagement.techtarget.com/definition/data-analytics
http://searchdatamanagement.techtarget.com/definition/data-analytics
This topic is very intriguing, since I love to play with data and I'm looking to augment my skills. I really appreciate the links too. I have to agree with the other posters though. Some description from Grace would be helpful, as well as her opinion on skills and qualities that fit well with this career path. Eagerly awaiting more please!
Not that I'm suspicious, but seeing her education background definitely does give you a clue about what type of courses or educational prep would give you a leg-up on what would be required to be a data analytic professional
I've found that my premed BS in Biology, Master's in Public Health (Epidemiology) and training and experience as a science teacher have in fact served me very well in my career in data analysis. Sure, I wish I had the official courses (and will be checking out the links) but intense curiosity, creativity, and ability to look beyond the obvious are the critical skills. These traits are found in lots of folks from lots of different backgrounds. In my opinion the mindset is the key point; the techniques can be added. Not to stomp on the guys that have invested the time and effort in the proper training: I salute you! But I believe that all sorts of folks can play in this sandbox if they have the interest and put in the time learning the tools.
I hope this doesn't come off as flippant, because its not supposed to be, but I cannot see how your BSc, Msc & teaching science have prepared you for a career in Data Analytics, as opposed to someone who has a B.Comp (Applied Computing) with a major in Database Design, and 13 years of experience in ETL (as it is now known, though all flavours and buzzwords).
When it all comes down to it, a B.Sc (Math) or (Stats) might be much more appropriate, as what you are looking for are exceptions from the norm, and a statistical analysis would seem to be much more useful in determining trends and anomalies.
When it all comes down to it, a B.Sc (Math) or (Stats) might be much more appropriate, as what you are looking for are exceptions from the norm, and a statistical analysis would seem to be much more useful in determining trends and anomalies.
Paymeister's education history is as applicable as yours or mine (BSc Aerospace, MS / PhD Mechanical engineering). It's what you learn over the course of your career, and how you apply it. For me, 15+ years of consulting in the field, starting as a green-bean analyst, taught me not only how to do the analysis but how to interpret what it means to the business.
Having the drive (and attention to detail) to work with data is really more important than your education, IMO. You can learn the technology, but for many, it's not so easy to learn how to see the forest for the leaves on the trees... decision or otherwise. =)
Having the drive (and attention to detail) to work with data is really more important than your education, IMO. You can learn the technology, but for many, it's not so easy to learn how to see the forest for the leaves on the trees... decision or otherwise. =)
Google is all over this. You can buy into their engine today,
Store your data, predict the future... Martin Omander
Contact Martin, Goole has this all done and ready in the Cloud.
I am not kidding other than the comment on Grace being a girl...
Store your data, predict the future... Martin Omander
Contact Martin, Goole has this all done and ready in the Cloud.
I am not kidding other than the comment on Grace being a girl...
It's like the old comparison between efficiency and effectiveness. Successful DI (past, present and future) firstly requires clear understanding of the business goals to be achieved, then good toolsets well deployed to carry out data gathering and analysis. If datasets are unrelated (or even unrelated to the business questions being asked) all the great analysis in the world will not provide useful insight or answers. (Remember "Garbage In, Garbage Out" ?) Having dabbled around the edges of DI in telecom operations, I know it pays to start small and simple to ensure Data leads to Information and then to Insight. Small insights can then be used as the basis of bigger investigations. Bottom line: If somebody tries to sell you DI (or DI "Tools") which can solve all your business problems without demonstrating an understanding of YOUR business goals, then check if they also have some good snake oil or a Brooklyn Bridge or two for sale.
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