How to start thinking about data science

How much better would you be at your job if you could predict the future?

Imagine, as a marketer, you knew all about the big trends the second they took off. Or as a salesperson, you knew exactly what your customers needed, before they did. Or as an auditor you knew about the next fraud scheme, before it started.

Well, you don’t have to imagine it. Because if you knew some basic data science techniques, you could actually predict some of these things. Right now, your month-on-month reports are compiled using basic data analytics techniques and they point to quite obvious trends. Data science gives you the more sophisticated version of that report. It gives you advanced techniques to work with much larger volumes and types of data and make predictions from trends that are not obvious.

Learning data science seems intimidating at first. But these sophisticated skills are valuable (80% of firms across the globe are looking for people who have them). They’re also very attainable.

Here’s a step-by-step guide to help you start thinking about data science.

Put simply, data science is about using data to reveal insight that will help you solve problems and make decisions.

It’s about collecting and analyzing data to answer specific questions (who is buying our products?). It’s about experimentation to test ideas and validate assumptions (are sales really dropping?). It’s about using techniques to bring together data from different sources (we won’t know the full sales figures until we can combine online and in-store sales data). It’s about analyzing vast volumes of disparate data to reveal previously unseen trends and predict what will happen in the future (what’s the sales trend really going to be in 18 months?).

In data science you use concepts – like statistics and probability – and advanced techniques – like machine learning – to explore and analyze your data and make predictions. You use visualization techniques to communicate your findings, and you use your own domain expertise – your unique knowledge of the data – to ask the right questions along the way as you build your analysis.

Today, we deal with a lot more data and a lot more data types – from social media posts, images, and videos, to stock market and weather data, readings from sensors and wearables, scientific experiment data, and a whole lot more.

This increase in volume, variety, and complexity has led to a need for techniques that allow us to use all this data to reveal new insight that would otherwise remain hidden from us. Basic data analytics is good at extracting insight from data from a single source, or a limited amount of data. Data science, however, allows us to work meaningfully with big data and data from multiple sources.

While spreadsheets are great for a simple month-over-month campaign report, they struggle if you want to integrate the full range of new social media monitoring KPIs your boss recently requested; BI tools are a popular tool of choice for reporting and visualizing, but without the data science techniques to clean all those messy social media texts and ensure the quality of the input data, report accuracy will suffer; proprietary data analytics tools are excellent for specific use cases but require additional effort (and costs) when you need to connect with 3rd-party tools and applications.

Data science gives us the techniques to bring together extremely complex data quickly, process it, and discover patterns and trends. 

The answer to this question depends on your tool of choice. If you start with coding, it’ll be difficult. If you start with low-code, you can dive right into the concepts without needing to learn the basics of coding first.

And because low-code models the logic of programming (you build your data analysis by dragging and dropping instructions that tell the computer what to do) you can even use it as an on-ramp to eventually program, if you want to.

Below is a low-code example of connecting up the instructions to form a data science workflow.

Look for beginner courses and become part of a data science community where your peers share their use cases and discuss challenges and solutions. You’ll learn faster with easy access to explore new techniques and best practices. 

When you start, pick a project from your day-to-day tasks you want to improve. This will give you a goal to aim for.

Perhaps you want to add more metrics to your marketing campaign report, monitor company expenses from additional subsidiaries, have consumer behavior analyses at your fingertips, or get ahead of credit card fraudsters with a more sophisticated prediction model. Join webinars,  learn specializations, and read industry blogs in the field of your interest.

“My tip for people who are learning data science is to be as active as possible with the new skill, language, or software that they are trying to learn. Incorporate your learning into your daily routine, even if it’s just one hour a day,” advises Heather Lambert, Cheminformatician. “It definitely helps to be part of a community that can offer advice and support when learning something new.”

All data science projects start with data collection. You’ll need to first learn how to access data from all the relevant sources, then clean and prepare it for analysis, and finally present your insights visually in a dashboard or report.

Basic data science techniques allow you to:

• Gather and prepare data of different types from multiple sources with data collection, processing & preparation techniques
• Detect patterns, trends, and relationships in the data with data mining techniques
• Build reports to communicate findings with data visualization techniques

As you progress with learning data science, you can choose to learn more advanced techniques. This is where additional knowledge of statistics for data science is helpful.

Advanced data science techniques allow you to:

• Develop statistical and predictive models with machine learning and AI techniques
• Build interactive dashboards and reports to communicate findings

Keith McCormick, LinkedIn Learning Instructor, works with data science students in his UCI Continuing Education classes.  His tip if you’re new to data science is:

“I would recommend that during the data science journey, you devote your time and effort equally to process and understand the life cycle, concepts, and execution. For example, if you’re seeking out a program, a boot camp, or any certificate courses where the focus is exclusively on coding and the data science life cycle and process are ignored, you should find a way to address those topics because you’ll need them on the job.”

Low-code data science tools make it easy for beginners to get started with data science. One such low-code tool is KNIME Analytics Platform, which is open source and free to use. There’s no better starting point than downloading it.

You can stop imagining how you’d predict the future and start doing it.