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:

- Several shortcuts without using the Mouse
- Excel has 1,048,576 rows.
- 16,384 columns
- 17,179,869,184 cells
- For data to be effectively managed in
*Excel*, you need 3 sheets- Datasheet
- Analysis sheet
- Report sheet

- Datasheet rules
- No empty columns
- No empty rows
- One row of all headers

- Label worksheets
- No Total and no subtotal. (Oh, I loved having totals and subtotals- an eye opener)

- All dates should be on a single column
- All variables and categories should be on a single column
- No obstruction on the data
- Do not merge any cells. (Every document I have ever worked on before now always had merged cells.)
- Do not hide cells
- Avoid using excessive Capital letters
- 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:

- S.C.A.L.E
- S-Staff on Demand

- C- Community and Crowd

- A- Algorithm

- L- Leveraged Assets

- E- Engagements

- I.D.E.A.S
- I-Interface

- D-Dashboard

- E-Experimentation

- A-Autonomy

- 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:

- Classical Method
- Subjective Method
- 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

#MMBA3