Blog

Welcome Note

Thank you for your visit to my website. I am sure you would find your favorite piece here. Apart from my love for data I love to play with my pen and with words. I write mostly about data and life generally. You can read more of my piece here.

Common Errors in Survey

You don’t have to be a #Professional Data Analyst/Researcher to be able to carry out a “#simple survey”. To carry out a “simple survey” you need to clearly know your objective(s), ie Why you want carry out the survey and you use that objective to derive your questions.Whichever method you are using to collect your data, either with #google form, paper questionnaire, or survey monkey, etc, you should not make this mistakes Read more.

Digit Preference

0 1 2 3 4 5 6 7 8 9 The above ten digits are used to make up figures in most part of the world. During my undergraduate days, I was taught error in age data — one of which is digit preference. It is important to note that most persons have preference for terminal digits ie numbers ending with 0&5, or even numbers(2,4…) For example, a person who is 49years might report his/her age as 50 Read more.

Realationships

One of the first few books I read was “The 360 degree Leader” by John C. Maxwell. The book emphasized on how best to lead upward, downward and side ways. Applying this principle to interpersonal relationships, everyone has – Persons that are above them(upward) – Persons who are below them(downward) and – Persons who on the same cadre or pedestrian(side ways). Read more.

What I Can't See

Everyone has this one or more thing they can’t see. We are all ignorant of something, like no one knows it all The exacerbating thing about ignorance is that one is also “ignorant of what one is ignorance of”- like you don’t even know what you don’t know. What one should ponder on should be “how will I know I don’t know what I don’t know”? My answer to that will be, being ignorant is inevitable, but you can rightly position yourself to reduce the degree at which you are ignorant. Read more.

Part 1: Data Analysis Techniques

What is Data Analysis: Before we dive into the realm of data analysis techniques, let’s take a moment to define data analysis. To be completely candid, there’s no one-size-fits-all answer to define data analysis, but I can provide a perspective that aligns with the role of a Data Analyst Read more.

Part 2: Data Analysis Techniques

Learning Some Jargons: As we delve into the intricacies of data analysis techniques, it's crucial to familiarize ourselves with some key technical jargon. This knowledge lays a solid foundation for understanding upcoming explanations. Here are some concepts to get started: Rows: The horizontal alignment of your data, representing individual Read more.

Part 3: Data Analysis Techniques

Statistics-A Data Expert's Recipe: As I pondered on the next topic in this series, I felt impelled to delve into the importance of statistics within the domain of data expertise. It's not an attempt to intimidate, but rather an acknowledgment that a significant portion of Data Analysis principles is rooted in statistics and, by extension, mathematics. Read more.

Part 4: Data Analysis Techniques

Asking the Right Questions: One of the keys to being a great data expert include asking the right questions. These questions don't need to be extraordinary; they can be as simple as confirming the obvious. When you're given data to analyze, whether the questions are made available or you're formulating them, it's crucial to understand Read more.

Part 5: Data Analysis Techniques

Understanding the Data Understanding the data is crucial in the work of a data expert. One beneficial aspect is the availability of supporting documentation, such as a codebook, design documents, questionnaires, and data descriptions, which can enhance your comprehension Read more.

Part 6: Data Analysis Techniques

Understanding the Tool Once you have framed your questions and understood your dataset, it's crucial to familiarize yourself with the tool(s) that you will use for your analysis. For context, data analysis tools means the software(package) you can use to analyze your data. There are so many of these software but I will mention a few: Excel, PowerBi, Tableau, SQL, STATA, SPSS, R, Python Read more.

Favorite Quote

"Data isn't units of information. Data is a story about human behavior - about real people's wants, goals and fears. Never let the numbers, platforms, charts and methodologies cloud your vision. Our real job with data is to better understand these very human stories."
Daniel Burstein