Dejan Sarka

Dejan Sarka

Dejan Sarka

Dejan Sarka, MCT and Data Platform MVP, is an independent trainer and consultant that focuses on development of database & business intelligence applications.Besides projects, he spends about half of the time on training and mentoring. He is the founder of the Slovenian SQL Server and .NET Users Group. Dejan Sarka is the main author or coauthor of sixteen books about databases and SQL Server. He also developed many courses and seminars for Microsoft, SolidQ and Pluralsight.

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Dejan Sarka

Data understanding and preparation – grouping and aggregating data II

September 28, 2018 by

You might find the T-SQL GROUPING SETS I described in my previous article a bit complex. However, I am not done with it yet. I will show additional possibilities in this article. But before you give up on reading the article, let me tell you that I will also show a way how to make R code simpler with help of the dplyr package. Finally, I will also show some a bit more advanced techniques of aggregations in Python pandas data frame.

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Interview questions and answers about data science, data understanding and preparation

July 27, 2018 by

Q1: In the data science terminology, how do you call the data that you analyze?

In data science, you analyze datasets. Datasets consists of cases, which are the entities you analyze. Cases are described by their variables, which represent the attributes of the entities. The first important question you need to answer when you start a data science project is what exactly is your case. Is this a person, a family, an order? Then you collect all of the knowledge about each case you can get and store this information in the variables.

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Data understanding and preparation – grouping and aggregating data I

July 10, 2018 by

I already tacitly did quite a few aggregations over the whole dataset and aggregations over groups of data. Of course, the vast majority of the readers here is familiar with the GROUP BY clause in the T-SQL SELECT statement and with the basic aggregate functions. Therefore, in this article, I want to show some advanced aggregation options in T-SQL and grouping in aggregations of data in an R or a Python data frame.

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Data understanding and preparation – basic work with datasets

June 4, 2018 by

In my previous four articles, I worked on a single variable of a dataset. I have shown example code in T-SQL, R, and Python languages. I always used the same dataset. Therefore, you might have gotten the impression that in R and in Python, you can operate on a dataset the same way like you operate on an SQL Server table. However, there is a big difference between an SQL Server table and Python or R data frame.

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Data understanding and preparation – entropy of a discrete variable

May 14, 2018 by

In the conclusion of my last article, Data science, data understanding and preparation – binning a continuous variable, I wrote something about preserving the information when you bin a continuous variable to bins with an equal number of cases. I am explaining this sentence in this article you are currently reading. I will show you how to calculate the information stored in a discrete variable by explaining the measure for the information, namely the entropy.

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Data understanding and preparation – binning a continuous variable

April 23, 2018 by

I started to explain the data preparation part of a data science project with discrete variables. As you should know by now, discrete variables can be categorical or ordinal. For ordinal, you have to define the order either through the values of the variable or inform about the order the R or the Python execution engine. Let me start this article with Python code that shows another way how to define the order of the Education variable from the dbo.vTargetMail view from the AdventureWorksDW2016 demo database.

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Data understanding and preparation – ordinal variables and dummies

March 29, 2018 by

In my previous article, Introduction to data science, data understanding and preparation, I showed how to make an overview of a distribution of a discrete variable. I analyzed the NumberCarsOwned variable from the dbo.vTargetMail view that you can find in the AdventureWorksDW2016 demo database. The graphs I created in R and Python and the histogram created with T-SQL were all very nice. Now let me try to create a histogram for another variable from that view, for the Education variable. I am starting with R, as you can see from the following code.

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Introduction to data science, data understanding and preparation

March 14, 2018 by

Data science, machine learning, data mining, advanced analytics, or however you want to name it, is a hot topic these days. Many people would like to start some project in this area. However, very soon after the start you realize you have a huge problem: your data. Your data might come from your line of business applications, data warehouses, or even external sources. Typically, it is not prepared for applying advanced analytical algorithms on it straight out of the source. In addition, you have to understand your data thoroughly, otherwise you might feed the algorithms with inappropriate variables. Soon you learn the fact that is well known to seasoned data scientists: you spend around 70-80% of the time dedicated to a data science project on data preparation and understanding.

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