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I don’t believe in “data science for all.” Just as I don’t believe everyone should be a surgeon, or an opera singer, or a pilot. I think that, like these other occupations, data science is a specialized job that requires expertise and high-level skills.
Is Data Science Hard to Learn?
Like any field, data science has its own set of challenges and can be difficult for some people. However, the difficulty level of data science largely depends on your background and prior experience with relevant topics such as statistics, programming, and machine learning.
If you already have experience in these areas, then learning data science may not be as difficult for you as compared to someone who is new to these topics. However, if you’re starting from scratch, data science can be challenging because it involves learning complex mathematical and statistical concepts, as well as programming skills to analyze and manipulate data.
To me, the campaign for “data science for everyone,” is an exaggerated course correction for the problem we do need to solve: data literacy.
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What is Data Literacy?
Data literacy refers to the ability to read, understand, analyze, and communicate data. It involves not only the technical skills required to work with data, but also the ability to understand the context in which the data was collected and the implications of the data for decision-making.
A data-literate person can use data to make informed decisions and solve problems, and can also communicate their findings effectively to others. This requires skills such as data analysis, data visualization, critical thinking, and effective communication.
Data literacy is becoming increasingly important in today’s data-driven world, as the amount of data being generated continues to grow exponentially. It is a valuable skillset for individuals in a wide range of fields, from business and finance to healthcare and government.
Is Data Literacy Easy to Learn?
Compared to the challenges of becoming a data scientist, becoming data literate and transferring into a data analyst role is a more accomplishable field for many individuals. Data Scientists are heavily involved with programming languages and libraries like R, Python, Pandas, and more. Whereas a data literate analyst may find themselves more often cleaning data tand using plug-and-play tools like Tableau or Power BI.
Our internal mission at QuantHub is to be a catalyst for the data fluency of individuals and companies all over the world. I do not believe everyone needs to be a data scientist, but I do believe everyone needs to be data fluent.
For example, there are people who become writers for a living, but elementary students are not taught to be novelists. They start with the ABCs, simple grammar, and reading. If this foundation of skills and their natural talent leads them to a Pulitzer, fantastic! But, what we’re most focused on at this elementary level is helping them navigate a world in which reading and writing is critical to their success.
Why Data Literacy is Necessary for Job Improvement
As digital transformation is changing jobs rapidly, this analogy can be applied to data skills. To be successful in the modern world, individuals need to understand how they create, consume, and use data. Exposure to these foundational skills might unlock data curiosities and help launch some individuals into the data science or data engineering field, but the exposure helps everyone else navigate a data-rich world with safety and confidence.
In fact, that’s why our first, universal data literacy unit is called, “data citizen.” Of course, data skills will help an individual be more successful in the digital workplace, but this level is also designed to help individuals be more successful in a digital world.
The funny thing is data literacy for all eventually does tangentially mean data science for all. As individuals gain data skills, they are enabling data science projects to be more successful, have language to talk more clearly with advanced analytics teams, and, in general, are more aware of the capabilities and solutions available to them through AI. That sounds awesome, doesn’t it?
Let’s get there one step at a time. Well, make that 10 minutes at a time.