“Big Data,” algorithms, and statistics are everywhere today. But how do you tell good data from bad? Misinformation from useful analysis? And who owns the information about our lives and decisions?
We will debate important social issues in the COURT OF DATA - where the only acceptable arguments will have to be based on data and data only.
Data 101 will help you improve your data literacy and develop a healthy skepticism about empirical claims presented in the popular media. We will explore examples of erroneous, rushed and ad hoc conclusions based on so-called “big data,” and you will get hands-on experience analyzing and using data to make persuasive arguments. You will also learn to make more informed decisions about what you find and share online. Along the way, you will learn fundamental concepts in statistics and probability and acquire basic programming skills that will benefit you in your future coursework and beyond.
This course is recommended for students from all schools and disciplines. (The course does require placement into Intermediate Algebra or above, or credit for 01:640:025.)
Data 101 can be used to meet the SAS Core Curriculum goals in 21st Century Challenges [21C], Quantitative and Formal Reasoning [QQ or QR], and Information Technology and Research [ITR].
Taneesh is one of top 5 scorers in the whole class. In addition he has won prediction challenge 2, with spectacular accuracy which provided dramatic ending on the Kaggle night!
Here is what Taneesh says about himself:
My name is Taneesh Amin and I am a Freshman (sophomore by the time you see this) graduating in 2026 with a major in Computer Science and the newly announced Data Science Major. Data101 was one of the more intellectually inspiring classes I have taken so far as a student. It really increased my love for Data Science, and I am so glad I am able to gain skills to pursue a career in it. If anyone had any interest in data science I would definitely recommend this course. If you are interested in seeing my continued data journey, I will be sharing it on my github (Taneesh04) and my Instagram (taneesh.am) where I analyze various patterns, mostly in sports using skills that have been taught to me in this class.
Rey has acheived top result (#1) in overall score in entire class (over 100%, maxing also extra credits!). Plus he placed top 5 overall in Quinlan competition for best overall prediction.
Here is what Rey says about himself and he even has something to share!
Hi, I'm a freshman pursuing majors in computer science and cognitive science as well as a minor in either mathematics or data science. I took this course to take my first steps into the field of data analysis, but I would like to stress to any incoming Data 101 students that this course is not about learning the gritty details of coding in R. Professor Imielinski focuses more on data science conceptually as being a way to think and analyze the world around us, which I think is much more useful! As a thanks for reading this far, here is a link to my notes that helped me do well in the class:
And as a final note, if your R code isn't working you probably either made a typo or missed a comma. Good luck to everyone!
Mayeesha has been on of the top 5 students in the class of 200 and also outstanding prediction challenges competitior (in the first two prediction challenges)
Here is what she says about herself and the class
Hi everyone, I’m Mayeesha! I am majoring in Cell Biology and Neuroscience and minoring in Psychology. I took Data 101 during my last semester as a graduating senior while trying to explore different career paths. I cannot stress enough how much this class has taught me about thinking analytically about the information presented to me. From realizing the role of bias in skewing samples and results to subsetting the data to find meaningful patterns, this class has shown me the importance of being critical about what I read and see on a daily basis. Despite my lack of coding experience, Professor Imielinski has been so encouraging and kind in office hours while showing me how to use R, which I feel much more comfortable with now! I look forward to using the data analysis skills I have gained in my future career and would recommend this course to anyone looking to gain a more keen understanding of how to analyze and present findings in a way that is meaningful to the audience at hand.
Melanie has achieved 2nd highest score in the class (also, like Rey, above 100 with extra credits). She also placed in top 5 of leaderboard for prediciton challenges
Here is what Melanie says about herself.
Hello! I’m a freshman majoring in mathematics and computer science. I took this class because I was interested in exploring data science and am currently considering it as a future career. I had a lot of fun figuring out the patterns in the prediction challenges and I’m thankful to Professor Imielinski for giving me the opportunity to learn and practice R! The skills I’ve learned in this class helped me to get a research position for the summer and I’m excited to apply my statistical analysis and R skills to data in the real world :)
Howard is the winner of 2023 Quinlan competition!
He got the highest combine accuracy from all competitions and won the trophe this year! In addition he placed in top 3 out of 200 students in overall score in the class. He also saved me a few times discovering early on some crippling mistakes like publishing values of prediction variable (the one which is supposed to be missing!)
Here is what Howards says about himself:
Hey there! I'm a sophomore in the class of 2025, majoring in computer science, mathematics, and possibly the newly introduced data science major. I had originally intended to devote myself entirely to artificial intelligence, but I enjoy the statistical elements of CS that this class contains. This course far exceeded my expectations and serves as an excellent foundation for data science purposes. This course teaches many fundamental basics in R for practical applications, which makes it suitable for students across a wide range of majors. I would like to thank Professor Imieliński especially, as I would most likely not be continuing data science if not for him. Thank You!