A Brief Introduction Into A Typical Data Science Project Life Cycle

A Brief Introduction Into A Typical Data Science Project Life Cycle

In this post, I demystified data science and talked about the lifecycle of a typical data science project. It's a good read for everyone.

The first time I heard the word data science was during my final semester as an undergraduate when my mentors - Professor Francisca and Dayo Akinbami, took a course titled “Introduction to Data Science”. 

Before that time, I didn't know what career path to choose, and I wasn't sure what I would do after graduating from university. But, I'm glad I took that introductory data science course. The least I can say is that it motivated me to start a data science career, and I'm thankful for how far I've gone. 

Although I'm not the subject matter for this article, I think it's good you understand how I got into data science. I wish to also use my story to motivate you that if I could come this far, you can do more. Mind you, I'm just starting, and my vision is to become a world-class data professional (by all standards) in the next few years. 

_My primary aim for writing this article is to demystify the word "data science", helping both individuals and enterprises to understand what it means and the different stages involved in building data science models. In subsequent articles, I'll discuss data science-based roles and the skills you need to become a top-notch data scientist. _

I know that we mustn't all become data scientists. However, my joy will be complete if this article helps at least a single person to find the right foot into data science, just as the introduction to data science course inspired me to get started. 

Let's get into this together.

What is Data Science?

The term "Data Science" is one of the most common buzzwords that you can find on the internet today, but it tends to be a difficult concept to grasp. If you ask three experts to explain what Data Science means, you're most likely to get four different definitions. 

For the purpose of this article, I prefer to stick to this definition: 

_"Data Science is a multidisciplinary field of study that combines programming skills, domain expertise and knowledge of statistics and mathematics to extract useful insights and knowledge from data". Those who practice data science are called "data scientists". They combine a wide range of skills and modern technologies to analyze data collected from sensors, customers, smartphones, the web and other sources. _

Let's break it down a little bit. Do you remember those times when you browse a product on an eCommerce platform, and you eventually see a strip of related products placed underneath the product details with the headline "customers who bought this item also bought ..." or "frequently bought together"? That's a good data science technique used by large retailers like Amazon to uncover the association between items and cross-sell to new and existing customers. 

I like to think that data science is one of the most exciting fields in existence today, and there are so many reasons why it has become a buzzword in nearly every industry or niche. One of the major contributors is that every organization is sitting on a treasure trove of data that can provide transformative benefits. The second reason is that data science is a transformative and invaluable technology that fuels the digital economy, just as oil fueled the industrial economy.

When done correctly, data science produces valuable insights and reveals trends that enterprises can leverage to plan strategically, optimize business processes, make better-informed decisions, create more innovative services and products and more. A typical data science lifecycle comprises several stages. In the next section, I'll show you the different phases and what each stage involves.

data-science machine-learning data data-cleaning

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