Project 01




Project 02



Project 03



Project 04


Project 05



Project 06













Regular expressions manual





Misleading graphs and charts


Tableau Samples









Below are some examples of my personal projects. To download these projects, please click the images or the links.








Project 01 is a SQL Server 2012 and SPSS project that analyses NHS practise data. Information about this data is found in the appendix of the project.












Project 02 is a statistical project that uses SPSS to analyse statistics from the data.SPSS is a powerful software tool for conducting various types of data analysis and interpretation. Whether you are doing a research project, a business report, or a personal inquiry, you can use SPSS to organize, manipulate, and visualize your data, and to test your hypotheses and draw conclusions.











Project 03: Delivering Restaurants’ Business Intelligence with Microsoft SQL Server 2008 by covering the entire BI lifecycle. Managing, analysing, and distributing enterprise data by building robust data integration (SSIS), reporting (SSRS), and analysis solutions (SSAS).









Project 04: A Marketing Plan PowerPoint presentation














Project 05: A Best Practise manual. It has best practise methods, tricks, short cuts. the advantage of this training manual is; everyone will understand what is expected and that my expectations are consistent for everyone, improves productivity, gives step-by-step directions for every system to get new team members up to speed quickly and codifies department policies to ensure consistency.











Project 06: Excel Dashboard. This dashboard uses Microsoft’s free Power Pivot add-in.  It just demonstrates how easy it is to create powerful reporting structures with Power Pivot. It has one set of slicers (six) that drive six charts. I have tried to make the dashboard not look like excel.

You can download this excel file and play around with the slicers and charts.







Excel in Integration Services: Issues and Alternatives
The good news is, all the issues with the Excel driver are not only known but well-known issues, thanks to its long life and widespread use. However they "present", different symptoms depending on the environment - ADO.NET code, DTS or SSIS packages or other clients. But it's always the same handful of underlying and recurring issues.
The following behaviours of the Jet provider with the Excel driver can lead to unexpected results when reading data from an Excel data source.





Regular expressions
A regular expression is a special text string for describing a search pattern. You can think of regular expressions as wildcards on steroids. You are probably familiar with wildcard notations such as *.txt to find all text files in a file manager. The regex equivalent is .*\.txt.
But you can do much more with regular expressions. In a text editor like Notepad++, EditPad Pro or a specialized text processing tool like PowerGREP, you could use the regular expression to search within the data.  Since "regular expressions" is a mouthful, you will usually find the term abbreviated as "regex" or "regexp".





Misleading graphs and charts
The statistical analyst’s goal should be to present the most accurate and truthful portrayal of a data set that is possible. Such presentation allows managers using the analysis to make informed decisions. However, it is possible to construct statistical summaries that are misleading. Although I do not advocate using misleading statistics, you should be aware of some of the ways statistical graphs and charts can be manipulated in order to distort the truth. By knowing what to look for, you can avoid being misled by a (I hope) small number of unscrupulous practitioners.



Gartner has named Tableau a leader in its BI Magic Quadrant report for the fourth straight year. Gartner evaluated 24 software vendors on 15 criteria for the quadrant.

Tableau leads among modern analytics products by providing a complete end-to-end visual analytics solution. Users can connect to virtually any data source that even non-technical users can then analyse using our drag-and-drop interface. Anyone can ask and answer questions of their data at the speed of thought, then easily share their insights.

Analytics are only as good as the data they are based on. Tableau makes it easy for your organization to curate and use more of your data than ever before. More data means asking more questions and sharing more comprehensive answers. With access to more data you can find answers to questions you didn't even know you had. Tableau + your data gives you the competitive edge.

The Tableau community is where you can connect with others to learn, collaborate, and share what you know. You can ask and answer questions in the Forums, join a local Tableau User Group, and even submit your own ideas for future versions of Tableau. Some of the links can be found here.






Python and R are the two most popular programming languages for data science. Both languages are well suited for any data science tasks you may think of. The Python vs R debate may suggest that you have to choose either Python or R. While this may be true for newcomers to the discipline, in the long run, you’ll likely need to learn both. Rather than seeing the two languages as mutually exclusive, you should see them as complementary tools that you can use together depending on your specific use case.






Can we use precompiled packages, models, and other things with Tableau and R?

Yes. The general rule is, if you can do it in R, you can easily integrate it with Tableau. This includes any statistical packages, parallel computing packages, models and libraries, whether they are standard within R or if you create them independently. This also includes commercialized versions of R, including Revolution Analytics. You can also return data frames from R one column at a time.




If very few people are using R and Python around you, then it is more the reason to learn R and Python, because soon you’ll be wowing them (and future employers) with your skills. Remember: if something is easy to do, everybody is doing that thing; you will become indistinct by becoming a commodity. Better, become indispensable.