Data Science and its Many Definitions
Hello!!! Welcome back!
A few weeks ago, we launched our 7th cohort at AI Saturdays Lagos — and we are thrilled! For those who don’t know us, AI Saturdays Lagos is an AI learning community in Nigeria that I started with my buddy, Azeez Oluwafemi in 2018. We offer free classes and have held over 100 classes on AI-related subjects so far. Before COVID-19, classes were held in person, but today, all meetings are virtual. The 7th cohort is already in the 3rd week of lectures. You should take a look at our structure — I think it’s fantastic! My team has put a lot of effort into it ❤️
Today I want to share with you the many definitions of Data Science as they unfolded in one of our classes in this cohort😅. It was exciting to read how differently our students think about data science.
To define Data Science, one of the students helped us with the definition of science itself 😌.
Science is the intellectual and practical activity encompassing the systematic study of the structure and behavior of the physical and natural world through observation and experiment.
As can be seen from the above definition, anything that claims to be science must be something that can be studied, observed, and experimented upon. To elaborate, I quote a paragraph from 50 Years Data Science from David Donoho.
There are diverse views as to what makes science, but three constituents will be judged essential by most, viz:(a1) intellectual content,(a2) organization in an understandable form,(a3) reliance upon the test of experience as the ultimate standard of validity
Now, moving on to the definitions :)
Data science is using data, by analyzing it, drawing inference from it and creating solutions through it.
Data Science is basically a field that involves the use of statistics and computation to solve or understand real-life problems.
Data science is the science of gathering data whether structured or unstructured to derive insights from it which in turn is used for making decisions.
Data Science is the science of data
To me, Data Science means, applying statistical tools to get information from data and use it for either the present or future.
Data science is the act of processing of data and visualising it into real world.
Data science is a process of turning raw data using computational and statistical methods into something useful for humans.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and applies knowledge and actionable insights from data across a broad range of application domain
Data science is the art of solving real world problems by drawing inferences from data.
I believe it best to be the managing (collection, processing, etc.) of data and manipulation of it to achieve certain purposes using whatsoever knowledge (math, programming, statistics, etc.) that would make the overall process more efficient
Data Science is the study of data to glean insight from it
Data Science to me is using huge amounts of data to make better decisions — using statistics and visuals.
I feel data science is a way of using data like statistical information to help understand and find a solution to problems that can only be solved from statistics.
Data science has to do with collection of data and bringing out meaningful and interesting information from it using some machine learning tools.
Data science is a technique used in getting insight from structured or unstructured data.
Data Science is the collection, processing of data to give insights and make predictions.
Data Science is using advanced analytics, scientific methods, statistics, maths, programming to derive insights hidden in data.
Me personally, I feel Data Science encompasses both data analysis and machine learning where data cleaning and exploration is being made to get insight from a particular problem thereby creating models that could best improve the problem in the future.
To me I think data science is a study that combines programming skills and knowledge of mathematics to extract meaningful information from data
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
Since Machine Learning is about building models to solve problems does that mean it is data science or it is a part of data science?
To make the class even more interesting, I then asked them if Machine Learning and Data Science were the same. Below are some responses shared by the students.
I feel machine learning comes under data science but data science isn’t machine learning due to the fact that it is a broader field
I would think it is because it is much more than just one thing. Machine learning is just one side to it — development of new algorithms from existing ones/data. But Data Science involves much more than that including Data processing, hypothesis, visualization etc.
I think there is machine learning in data science because ML builds algorithms to learn.
Data science makes use of machine learning tools
Data Science is not Machine Learning because it is much more, there are aspects of Data Science that does not necessarily need ML.
Emphasis is put more on stats in DS than in ML. Typical profiles working as ML engineer are from CS background, while Data Scientists can come from science and business background, using stats and business accumen
One can be a ML Engineer implementing mathematical models without understanding in depth, while Data Scientists cannot / shouldn’t make sense of data without really understanding the mathematical concepts underneath it
I think data science is the collection of the data and so on while machine learning is the use of those data to predict an outcome
Machine Learning is the application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed while DS is predictive analysis for better decision making
Data science is not machine learning. It is much more broader than that. Machine learning is a subset of data science. Although they are intertwined when it comes to making predictions through model building and use of algorithms, as machine learning uses the data generated from the outcome of a data science workflow.
Data science requires one to think outside the box as trying to find patterns within the data which requires drawing insights, after answering the questions real world situations asks. Data analysis, data mining, data engineering, data visualization are also subsets of data science because machine learning requires the result of the data gotten from these processes.
We had so much fun in class discussing Data Science and what it means to be a Data Scientist. We also looked at tools like proGender, which was developed by Ben Schmidt, a history professor from Northeastern University. The tool uses 14 million reviews from RateMyProfessor.com and counts the number of occurrences of different terms per million words for reviews of male and female professors. We queried words like ambitious, strict, etc. I have to admit, that the selection of words was definitely not random, but luckily I was able to spot our bias before it hit us.
We also looked at another tool developed by one of our alumni Chizurum Olorondu called NBA Players. The aim of the tool is to limit the time it takes for an NBA fan or curious individual to find useful facts about the current active players in NBA.
In closing, my team and I are honored to teach such diverse, curious, and incredible people. Many of whom are experts in different fields but want to expand their skills or even change careers. The sheer determination, zeal, perseverance, and willpower of our students are truly inspiring.
Teju can you take all the classes…I love your energy — by a very kind stundent
I think the student might change their mind after being blown away by my team. We really do have the best instructors😌. I look forward to working with this amazing group of people over the next 14 weeks
A big thank you to my incredible team, without whom this kind of magic would be lost in the caverns of mere imagination❤❤
Thanks for making it to the end. Check out our repository to learn more about our cohort7 structure. Are you on Spotify? What’s your favorite song? Give our Spotify playlist a listen😎 — you can also add your favorite songs to it. Would you like to contribute your article to our medium publication? Check out this doc ✨
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