In the era of data science and AI, it is easy to skip over some crucial steps such as data cleansing. However, this can cause major problems in your applications later down in the data pipeline. The promise of possible magic like data science solutions can overshadow the necessary steps required to get to the best final product. One such step is cleaning and engineering your data before it even gets placed into your system. Truthfully, this is not limited to data science. Whether you are doing data analytics, data science, machine learning, or just old fashioned statistics, data is never whole and pure before refining. Just like putting bad unprocessed petroleum into your car, putting unprocessed data into your company's systems will either immediately, or eventually wreak havoc(Here are some examples). Whether that means actually causing software to fail, or giving executives bad information both are unacceptable.
We at Acheron Analytics wanted to share few tips to ensure that whatever data science/analytics projects you are taking on, you and your team are successful. This post will go over have some brief examples in R, Python and SQL, feel free to reach out with any questions.
Duplicate data is the scourge of any analyst. Whether you are just using excel, Mysql, or Hadoop. Making sure your systems don’t produce duplicate data is key.
There are several sources to duplicate data. The first comes from when the data is input into your companies data storage system. There is a chance that the same data may try to sneak its way in. This could be due to end-user error, a glitch in the system, a bad ETL, etc. All of this should be managed by your data system. Most people still use RDBMS and thus, using a unique key will avoid duplicates being inserted. Sometimes, this may require a combination of fields to check and see if the data being input is a duplicate. For instance, if you are looking at a vendor invoice line item, you probably shouldn’t have the same line item number and header id twice. This can become more complicated when line items change(but even that can be accounted for). If you are analyzing social media post data, each snapshot you take may have the same post id but have altered social interaction data (likes, retweets, shares, etc). This references slowly changing dimensions, which, is another great topic for another time. Feel free to read up more on the topic here.
In both cases, your systems should be calibrated to safely throw out the duplicate data and store the errors in some error table. All of this will save your team time and confusion later.
Besides the actual source data itself having duplicates. The other common duplicate that can occur is based off an analyst's query. If, by chance, they accidentally don’t have a 1:1 or 1 : Many relationship on the key they are joining on, they may find themselves with several times the amount of data you started with. This could be as simple as restructuring your team's query to make sure they properly create 1:1 relationships, or...you may have to completely restructure your database. It is more likely the former option.
How to Get Rid of Duplicate Data in SQL
Has your company ever purchased data from a data aggregator and found it filled with holes? Missing data is common across every industry, sometimes it is just due to system upgrades and new features being added in, sometimes just bad data gathering. Whatever it might be, this can really skew a data science projects results. What are your options then? You could ignore rows with missing data, but this might cost your company valuable insight and including the gaps will produce incorrect conclusions. So, how do you win?
There are few different thoughts on this. One is to simply put a random and reasonable number in place of nothing. This doesn’t really make sense, as it is difficult to really tell what is being driven by what feature. What is a more common and reasonable practice is using the data set average. However, even this is a little misleading. For instance, on one project we were involved with, we were analyzing a large population of users and their sociometric data(income, neighborhood trends, shopping habits). About 15% of the data was missing that was purchased from a credit card carrier. So throwing it away was not in our best interest.
Instead, because we had the persons zipcodes, we were able to aggregate at a local level. This was a judgement call. A good one in this case. We compared this to averaging the entire data set, and we really got a much clearer picture on our populations features. The problem with a general average over several hundred thousand people is that you will eventually have some odd sways. For instance, income, if your data set is a good distribution, you will end up with your average income being, well, average. Then, suddenly, people that may have lived in richer neighborhoods may suddenly create their own classification. The difference between 400k vs 50k(even when normalized) can drastically alter the rest of the features. Does it really make sense for someone who is making 50K a year to be purchasing over 100k of products a year? In the end, we would get a strange cluster that was large spenders, who made average income. When your focus is socio-economic factors. This can cause some major discrepancies.
How to Handle Missing Data with SQL
Data normalization is one of the first critical steps to making sure your data sensible to run in most algorithms. Simply trying to feed in variables that could be anything from age, income, computer usage time, etc, creates the hassle of trying to compare apple to oranges. Trying to input 400k to 40 years will create bad outputs. The numbers just don’t scale. Instead, the concept of normalization allows your data to be more comparable. It takes the max and min of a data set and sets them to the 0 and 1 of a scale. Now, the rest of the numbers can be scaled. Utilizing 0-1 allows your data science teams to meld the data smoother. They are no longer trying to compare scales that don't match. This is a necessary step in most cases to ensure success.
R Progamming Normalization
Python(This can also depend on whether you are using Numpy, Pandas, etc)
Data preparation can be one of the longer steps when preparing your teams data science project. However, once the data is cleaned, checked, and properly shaped, it is much easier to pull out features, and create accurate insights. Preparation is half the battle. Once the data is organized, it becomes several times easier to mold. Good luck with your future data science projects and feel free to give us a ring here in Seattle if you have more questions about your data science projects
Future Learning! And Other Data Transformations
We wanted to supply some more tools to help you learn how to transform and engineer your data. Here is a great video that covers several data transforms. This particular video relies on the R programming language.
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