My First Romance With Data Analysis.

Coming from a science background, I never liked or believed I could have any interest in mathematics not to talk more of understanding the concept of analysis data for business sustainability.

Clicking on that zoom link at the first pre-module class was a case of “Let’s See” for me. Then I saw our faculty, heard him introduce himself, and asked us to not be too hard on ourselves concerning the course. He then started with his first topic and as every class went by, I found out all I needed was to follow through in his course, create time to read and

There are a few take homes from the class :

1. Excel spreadsheet has different types i.e numbers aligned to the right and text aligned to the left
2. Excel has 16,384 columns, 1048,576 rows, 17,179,869,184 cells.
3. Statistics is Data Science which is the heart of data analysis
4. Collect Data- Organise Data-present data and analyze data
5. Statistics are broken down into Descriptive Statistics (summary of data without conclusion i.e Mean, median, and mode) and Inferential Statistics (Here the data is analyzed and the conclusion is drawn from the analysis.
6. Information gathering Excel document needs to have 3 sheets:

• Datasheet: Used to put all your data
• Analysis sheet: Used to do all your analysis
• Report sheet: Used for all your reports.

Above are the few take-homes am able to highlight now. However, data analysis is also the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making processes. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names and in use in different business and science domains. In the world of business today, data analysis plays a role in making decisions more scientific and helping business operations be more effective.

Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while the word business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information.

In statistics applications, data analysis can be divided into descriptive statistics, exploratory data analysis, and confirmatory data analysis. EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analysis focuses on the application of statistical models for predictive forecasting or classification.

With that is been said and described as I have found that improving and going further to becoming a data analyst is one of the best things that can help my job as the Managing director of two brands and the Founder of a Non-Profit that has actively trained and impacted love 12,000 women in Africa. I sincerely look forward to more days of analyzing Data and using it for better-informed choices as a business manager.

So far, I think what started as a #babyblogger is slowly transforming into #teenblogger lol. Slowly but surely like they say I guess.

Talk to you soon.

#Babybloggerisevolving #Memba11