What you think you know

Ifeyinwa Chioke Written by Ifeyinwa Chioke · 1 min read >

I have always prided myself on knowing and using Excel effectively. I scored myself a 7 out of 10 when we were asked to rate our proficiency in the use of Excel in the data analytics class that fateful Saturday.

By the time the Data Analytics Facilitator got into the basics of Excel, I realized that my proficiency was probably less than 1.  Understanding Excel and its usage was the beginning of my journey in Data Analytics Class.

Data needs to be collected, organized, presented, and analyzed.

I learned the following:

  1. Several shortcuts without using the Mouse
  2. Excel has 1,048,576 rows.
  3. 16,384 columns
  4. 17,179,869,184 cells
  5. For data to be effectively managed in Excel, you need 3 sheets
    • Datasheet
    • Analysis sheet
    • Report sheet
  6. Datasheet rules
    • No empty columns
    • No empty rows
    • One row of all headers
  7. Label worksheets
    • No Total and no subtotal. (Oh, I loved having totals and subtotals- an eye opener)
  8. All dates should be on a single column
  9. All variables and categories should be on a single column
  10. No obstruction on the data
  11. Do not merge any cells. (Every document I have ever worked on before now always had merged cells.)
  12. Do not hide cells
  13. Avoid using excessive Capital letters
  14. Apply color reasonably. (I love using colors)
  • New terminology learned- S.S.R.B- Spreadsheet Standard Review Board.

At the end of Day 1 in Data analytics, I was perplexed and I wondered what lies ahead and if this was just the beginning of a complicated evolution in Excel usage, data gathering, and analysis.

By the time we went into analyzing the data that was presented in the practice Excel worksheet, it was clear that I was just a ‘toddler’ in Excel usage.

Then came the assignment on Probability; the chances of an event happening or not happening. It is impossible to say that an event might occur with 100% accuracy.

The intensive week came with new challenges and new knowledge to acquire. I quickly learned these new terms:

  1. S.C.A.L.E
    1. S-Staff on Demand
    1. C- Community and Crowd
    1. A- Algorithm
    1. L- Leveraged Assets
    1. E- Engagements
  2. I.D.E.A.S
    1. I-Interface
    1. D-Dashboard
    1. E-Experimentation
    1. A-Autonomy
    1. S-Social Technologies

Probability then became the next point of conversation, with simple illustrations. For instance, Investment outcomes can be: make money, lose money or break even.

There are different methods of determining the probability of an event or outcome:

  1. Classical Method
  2. Subjective Method
  3. Relative Frequency Method

Classical Method: This is about assigning probability using patterns e.g., Tossing a coin- it is either a Tail or a Head. So, it is one out of two possible outcomes. The probability of each of the outcomes is therefore ½. In the same vein, the probability of getting each number on a die when the cast is one out of 6 outcomes.

Subjective Method: This type depends on history, intuition, and experience

Relative Frequency: This type looks at the frequency of an occurrence as compared to the total number of outcomes, each outcome/Total number of outcomes.

I can say in a nutshell that Data Analytics is complex, complicated, and difficult to understand. A lot of attention is required to keep up with the pace of the 2 Facilitators. A lot of effort is required. Now I see we are just scratching the surface.

‘What you think you know, you know not, what you do not know you need to learn.

Keep learning one step at a time, soon you will get to a complete whole.

From Adaisky


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