Hello, I'm Sade 👋🏾

Welcome to my blog!

Time is precious, so I'll get straight to it. In June 2022, I started Google's Data Analytics Certificate on Coursera. So far, the course has been incredible.

I started this blog to document my journey into a new career in Data analytics (#CareerSwitcher)!. I will be documenting my learnings and hope it helps you too.

What is the hype about data analytics?

In the next decade, the demand for business analytics skills will probably be higher than the demand for any other career (10.9% vs. 5.2%) (Source: Bureau of Labor Statistics).

In 2006, British mathematician Clive Humby coined the phrase:

"Data is the new oil."

A quick Google search shows data now powers entire industries and holds tremendous value – but is effectively worthless if left crude.

The average business today needs to make data-driven decisions to beat competitors while remembering the real people behind the story the data tells. (More on this later.)

Data is the new oil

Everyday questions I can use data to answer

After a warm introduction to the course, I dived into thinking about everyday questions I can use data to answer.

Example questions I came up with include:

1. How much do I spend each month in what categories?
2. What topics of videos do I watch most on youtube?
3. What is the one thing a brand new business can do to attract new customers, based on the type of business?
4. How much is enough to retire in The UK and what is the best retirement plan?
5. Whats the financial literacy of Millenials?
6. How have people changed their spending habits since covid?
7. How much tea and coffee is needed to stain teeth?
8. Top 5 ways people can eat more fruit and veg.
9. The impact of the food i eat on my health in future.
10. Top 5 - 10 assets owned by self-made millionaires.

Doing this exercise made me realise just how many questions I had. I got excited by the prospect of doing some analysis of these questions soon (stay tuned!)

What everyday questions do you have?

The data analysis process

It would be very awkward to approach data analytics without a standardised process. It would be like working as a pharmacist (my current job) without standard operating procedures!

Below is a comprehensive outline of the steps of data analysis.

1. Ask questions and define the problem. What is the business challenge, objective and question?
2. Prepare data by collecting and storing the information. This involves data generation, collection, storage, and data management.
3. Process data by cleaning and checking the information. This involves data cleaning and maintaining data integrity.
4. Analyze data to find patterns, relationships, and trends. This involves data exploration, visualization, and analysis.
5. Share data with your audience. This requires communicating and interpreting results clearly, potentially to a wide audience.
6. Act on the data and use the analysis results. This requires putting your insights to work to solve the problem.

There are other common iterations of the data analysis process for example Big data analytics life cycle, SAS's iterative life cycle and EMC's data analysis life cycle.

A few definitions

So far, I have used terms like data analysis and data analytics which, believe it or not, are not the same thing. 😄 Let's take a look at some terms commonly used in data analytics.

• Data is a collection of facts
• Data analytics is the science of data, just like biology is the science of life.
• Data analysis on the other hand, is the collection, transformation, and organisation of data to draw conclusions, make predictions, and drive informed decision-making. Think of it as breaking down data; lysis means to break down.
• A data analyst is therefore someone who collects, transforms, and organises data, to drive informed decision-making.
• Data-driven decision-making means using facts to guide business strategy.
• A Data ecosystem is the different elements that interact with one another to produce, manage, store, organise, analyse, and share data.
• Data science is a field of study that uses raw data to create new ways of modelling, understanding and predicting the unknown.
• A Dataset is a collection of data that can be manipulated or analysed as a single unit.

A few definitions

Benefits of data-driven decisions for businesses

The advantages of data-driven business decisions are numerous. Data-driven decisions help:

• Improve processes
• Spot opportunities and trends which competitors haven't, resulting in a first-mover advantage (this is huge!)
• Launch new products
• Serve customers
• Make thoughtful decisions.

Data analysis helps discover easier ways of doing things, identify patterns to save time, and find surprising new perspectives that can completely change experiences.

Red pill or Blue pill?

Data science encompasses three disciplines: machine learning, statistics, and analytics. If you're reading this and you still don't know what field of data to specialise in, think of the branches like this.

If you want to make a few important decisions under uncertainty, that is statistics. If you want to automate, in other words, make many, many, many decisions under uncertainty, that is machine learning and AI.

But what if you don't know how many decisions you want to make before you begin? What if what you're looking for is inspiration? You want to encounter your unknown unknowns and understand your world. That is analytics!

Whatever you choose, remember to let go of temptations towards perfectionism and instead; enjoy the fun and the thrill of exploration. 🙌

What will it be?

Red pill or blue pill?

Gut instinct in data analytics

"Follow your gut; it's always right"...

Except when it comes to data.

Gut instinct is an intuitive understanding of something with little or no explanation. It can be a problem. If data is ignored by making decisions based on personal experience, decisions made may be biased.

But even worse, decisions based on gut instinct without any data to back them up can cause mistakes. This is why analysts often ask “How do I define success for this project?”

Additionally, it is important to ask the following questions about a project to help find the perfect balance:

• What kind of results are needed?
• Who will be informed?
• Am I answering the question being asked?
• How quickly does a decision need to be made?

While experience in an industry is a big advantage, especially in tight deadlines, it can also be disadvantageous due to bias and gut instinct. It is vital to strike the right balance between intuition and data, which are after all; the facts.

Summary

This is the end of my key insights from week 1 of Google's Data Analytics Certificate. I had a blast writing this and hope you took something away too.

We discussed data being the new oil, uncovered everyday questions data can be used to answer, and the six steps of the data analysis process.

We also went over some definitions, outlined some benefits of data-driven decisions for businesses, described the differences in the data science umbrella and discussed gut instinct in data analytics.