Excel is one of those software packages
that I feel is completely underutilized when it comes to advanced analysis.
There's so - there's so much power to Excel beyond data cleaning,
beyond running frequencies and doing pie charts.
Excel has built in it tools that allow you to do difference testing, such as T-tests,
correlation tests and advanced regression analysis for prediction.
I feel that the majority of people
that I have met who use Excel for their financials simply do formulas.
And they're very great formulas and complicated formulas,
but it really scratches the surface of what Excel is capable of doing.
And then you partner Excel with some advanced statistical software like SPSS, SAS and R
and you truly have all the skills
that are necessary to be a very productive member
of a data science team in any organization,
whether it's public sector, private sector,
or going out much like myself and becoming a consult.
So, I would say that the skills that are required in today's workforce
is a range of skills often referred to as data science.
There's the IT skills and that's being able to build systems that collect the data,
store the data, warehouse the data.
Then there's the analytic skills or statistical skills.
And then beyond the statistical skills there's data visualization,
that's the reporting, the pie charts, the dashboards.
And more recently, there is the data journalism or being able to tell the story.
So for instance, you have a pie chart, you have a bar chart.
They do tell stories if you're looking closely enough,
if you're looking into what analysis went into that particular chart.
For instance, you might do a T-test, which then a bar chart is very appropriate.
But that T-test then gives you levels of significance
and you can draw conclusions from that.
And so simply looking at a bar chart
and saying there's one group that performed higher than another group,
simply by looking at the chart, is probably ill-advised.
You need to go and look at the chart,
look at the statistic that was performed and then tell the story.
And I'd say moving forward telling the story
is probably the most important part of data analysis.
Because if you cannot tell the story about what that chart is communicating
and what analysis went into it, your audience isn't going to get the full picture.