Even if it’s been a while since your last statistics class, when you read Stats with Cats: The Domesticated Guide to Statistics, Models, Graphs, and Other Breeds of Data Analysis you’ll figure out that there’s much more to data analysis than just calculating a few averages and creating bar charts. Data analysis is definitely not easy. Even so, there are more political pollsters than ever and baseball announcers still talk endlessly about statistics between pitches.
Most of the things you’ll read about in Stats with Cats were never mentioned in your Statistics 101 class. You’ll have to know about these things, though, if you want to analyze your own data at home and at work. It may look formidable at first, at second, and at third. But like baseball, if data analysis wasn’t hard everybody would do it because it’s a lot of fun.
Stats with Cats has 140,000 words on 376, 7×10-inch pages divided into 25 chapters in 6 parts with 47 figures, 24 tables, 107 quotes, and 99 photos of cats. If all that information could be distilled into one picture, this is what it would look like:
You use statistics because you can; you have the knowledge and software is readily available. You use statistics because you need to, to analyze uncertainty, especially when there are too many data to just make a graph. You use statistics when you have to, such as when the problem can’t be solved any other way or when regulations mandate their use.
As a data analyst, you have to know many things, not just about statistics and the project background, but also about the project’s contract, scope, schedule, budget, and deliverables. You have to communicate effectively, both in speech and in writing, and establish good working relationships with project stakeholders. You have to decide on a performance strategy, ensure you get paid, and never compromise your ethics. Finally, you have to have the expertise and time to do the work, and above all, you have to practice, practice, practice.
Data analysis begins when you want to investigate some phenomenon that occurs in a definable population. You collect samples of the population using an appropriate sampling scheme and other measures to control variance and avoid biases so that you will meet your targets for precision and accuracy. You may need to collect more (or less) than thirty samples to meet the resolution you need for the analysis. You measure variables relevant to the phenomenon on appropriate scales. These measurements are the data, which along with the metadata, form the information you structure in a file format your software can recognize as a matrix. Your objectives and aims for model use, together with the scales and natures of your variables, enable you to select appropriate statistical methods. You scrub the information and do an initial analysis, which together with the objectives and methods, lead to your model specifications. Using the specs, you go through the steps of the modeling process to develop and calibrate a model, and evaluate possible violations of assumptions. From the model, you build on your knowledge of the phenomenon. Eventually, from critical analysis through statistics, you can synthesize the wisdom you need to make informed decisions.
The last three paragraphs describe the contents of Stats with Cats in about 350 words, without the cats of course. See what a big difference they make?
Over time, as you analyze different datasets, you’ll become more comfortable with the process. You’ll learn shortcuts to doing things. You’ll develop an instinct for things that will work and things that won’t. You’ll even be able to impress your friends and co-workers with all the new jargon you’ve learned. You might also learn a bit about yourself. Are you more of a right-brained, intuitive, visual, big-picture, inductive thinker or are you more of a left-brained analytical, verbal, detail-oriented, deductive thinker. Understanding your own preferred thought processes will help you find your best paths in life as well as data analysis.
Being able to analyze data is the asset that sets the knowledge-wielding experts apart from the arm-waving storytellers. Don’t wait for your boss or teacher to send you out into the many unmarked routes of the databahn. Journey to the land of data analysis at your own speed along paths you’re comfortable with. Don’t just endure a data analysis project. Make the journey as fulfilling as the arrival. Make data analysis your passion.
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