Eze goes to Data school- Data Analytics

Franklin Osuji Onuwa Written by Franklin Osuji Onuwa · 1 min read >

Reflecting on my learnings in Data Analytics (otherwise called statistical analysis) so far, I am sure my ancestors were laughing their arses off when I mentioned earlier my proficiency in excel was above 5. Boy was I wrong!

First, let’s define Data to better understand the topic we are about to delve into. Data is defined as a set of values, facts, and figures. The different sources of data can be categorized into primary sources and secondary sources. Just like the name implies, the primary source of data, which is basically ‘raw data’ includes brainstorming, market research, questionnaire, etc. And the secondary source includes audited accounts, income statements, balance sheets, etc. Data can be classified in different ways.

Data can be classified as Qualitative or Quantitative data. Qualitative, meaning it cannot be quantified; cannot be counted/ numbered. An example includes eye colour, political party, etc. Quantitative, on the other hand, can be numerically counted and is further distinguished into discrete data and continuous later. As the name implies, discrete data is quantified as a whole number, not in fractions; continuous data includes complex numbers and varying data over a period, for example, with weight, volume, etc.

Important to also note that when classifying data, we tend to stratify them into periods. Data collected at a point in time is classified as cross-sectional data, and data collected over a period is classified as time series data. A combination of both is defined as a pool of panel data.

I was drilled to understand the systematic application of statistics/ data. Statistics being a branch of mathematics deals with collecting, organizing, presenting, analysing, and interpreting data to make informed decisions. Quality of decisions is a function of the information available. This is because it is data that drives the business forward. In analysing data, we ask ourselves different questions. We can group these into four different questions. First, we ask ourselves “what has happened”- which is Descriptive Analysis. Second, we ask ourselves “why has it happened”- which is Diagnostic Analysis. Third, we will ask ourselves “what is going to happen”- which is defined as Predictive Analysis. And lastly, we recommend the action to take, which is Prescriptive Analysis. These four distinctions form our basis for the analysis of data.

There are different tools used to analyse Data. Some of them include SQL, MS Excel, Python, R, Power BI, Tableau, etc. There are basic rules in Excel which we must follow in Data Analytics. Some of these rules, which we must remember by heart, include:

  • There should be no empty rows, no empty columns,
  • there should be no total or subtotal,
  • All data should be in a single column,
  • No obstruction around the table,
  • Always label the sheets,
  • Apply colours professionally and not like an amateur,
  • Avoid the use of capital letters, etc.

MS Excel is a central tool that is very important in data analytics. Mastery of MS Excel and shortcuts in it will benefit future users in learning this course and make one faster. Some of these shortcuts in MS Excel include:

  • CTRL + G – Find Special
  • CTRL + SHIFT + L – Filter
  • CTR + T – Convert to table; to mention but a few.

To be continued…



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