There's a saying in the data science community that's been around for a while, and it goes: "A data scientist is better than any computer scientist at statistics, and better than any statistician at computer programming." This encapsulates the general skills of most data scientists, as well as the history of the field.

Data science combines computer programming with statistics, and some even call data science applied statistics. Conversely, some statisticians think data science is only statistics. So, while we might say data science dates back to the roots of statistics in the 19th century, the roots of modern data science actually begin around the year 2000. At this time, the internet was beginning to bloom, and with it, the advent of big data. The amount of data generated from the web resulted in the new field of data science being born.

A brief timeline of key historical data science events is as follows:

• 1962: John Tukey writes The Future of Data Analysis, where he envisions a new field for learning insights from data
• 1977: Tukey publishes the book Exploratory Data Analysis, which is a key part of data science today
• 1991: Guido Van Rossum publishes the Python programming language online for the first time, which goes on to become the top data science language used at the time of writing
• 1993: The R programming language is publicly released, which goes on to become the second most-used data science general-purpose language
• 1996: The International Federation of Classification Societies holds a conference titled "Data Science, Classification and Related Methods" – possibly the first time "data science" was used to refer to something similar to modern data science
• 1997: Jeff Wu proposes renaming statistics "data science" in an inauguration lecture at the University of Michigan
• 2001: William Cleveland publishes a paper describing a new field, "data science," which expands on data analysis
• 2008: Jeff Hammerbacher and DJ Patil use the term "data scientist" in job postings after trying to come up with a good job title for their work
• 2010: Kaggle.com launches as an online data science community and data science competition website
• 2010s: Universities begin offering masters and bachelor's degrees in data science; data science job postings explode to new heights year after year; big breakthroughs are made in deep learning; the number of data science software libraries and publications burgeons.
• 2012: Harvard Business Review publishes the notorious article entitled Data Scientist: The Sexiest Job of the 21st Century, which adds fuel to the data science fire.
• 2015: DJ Patil becomes the chief data scientist of the US for two years.
• 2015: TensorFlow (a deep learning and machine learning library) is released.
• 2018: Google releases cloud AutoML, democratizing a new automatic technique for machine learning and data science.
• 2020: Amazon SageMaker Studio is released, which is a cloud tool for building, training, deploying, and analyzing machine learning models.

We can make a few observations from this timeline. For one, the idea of data science was around for several decades before it became wildly popular. People foresaw that future society would need something like data science, but it wasn't until the amount of digital data became so widespread and easily accessible that data science could actually be used productively. We also note that the two most widely used programming languages in data science, Python and R, existed for 15 years before the field of data science existed in earnest, after which they rapidly took off in use as data science languages.

There is another trend happening in data science, which is the rise of data science competitions. The first online data science competition organization was Kaggle.com in 2010. Since then, they have been acquired by Google and continue to grow. Kaggle offers cash prizes for machine learning competitions (often 10k USD or more), and also has a large community of data science practitioners and learners. Several other websites have appeared and run data science competitions, often with cash prizes as well. Looking at other people's code (especially the winners' code if available) can be a good way to learn new data science techniques and tricks. Here are most of the current websites with data science competitions:

• Kaggle
• Analytics Vidhya
• HackerRank
• DrivenData (focused on social justice)
• AIcrowd
• CodaLab
• Topcoder
• Zindi
• Tianchi
• Several other specialized competitions, like Microsoft's COCO

Shortly after Kaggle was launched in 2010, universities started offering master's and then bachelor's degrees in data science. At the same time, a plethora of online resources and books have been released, teaching data science in a variety of ways.

As we can see, in the late 2010s and early 2020s, some aspects of data science started to become automated. This scares people who think data science might become fully automated soon. While some aspects of data science can be automated, it is still necessary to have someone with the data science know-how in order to properly use automated data science systems. It's also useful to have the skills to do data science from scratch by writing code, which offers ultimate flexibility. A data scientist is also still needed for a data science project in order to understand business requirements, implement data science products in production, and communicate the results of data science work to others.

Automated data science tools include automatic machine learning (AutoML) through Google Cloud, Amazon's AWS, Azure, H2O, and more. With AutoML, we can screen several machine learning models quickly in order to optimize predictive performance. Automated data cleaning is also being developed. At the same time that this automation is happening, we are also seeing a desire by companies to build "data literacy" among their employees. This "data literacy" means understanding some basic statistics and data science techniques, such as utilizing modern digital data and tools to benefit the organization by converting data into information. Practically speaking, this means we can take data from an Excel spreadsheet or database and create statistical visualizations and machine learning models to extract meaning from the data. In more advanced cases, this can mean creating predictive machine learning models that are used to guide decision making or can be sold to customers.

As we move into the future with data science, we will likely see an expansion of the toolsets available and automation of mundane work. We also anticipate organizations will increasingly expect their employees to have "data literacy" skills, including basic data science knowledge and techniques.

This should help organizations make better data-driven decisions, improve their bottom lines, and be able to utilize their data more effectively.