Real analysis data science reddit. Throw in a finance class or two in your free time.

Real analysis data science reddit You are likely more focused on data management and analysis, less on generic office work. Apr 5, 2016 · No, you don't need to understand measure theory and real analysis to do machine learning in data science. Members Online A lot of post here discuss switching careers INTO data science. 2 - Python — Crawling Restaurant Data Yep, first off the company that even offers this position has to be big , and it needs to work with mountains of data,and you needed to have solid statistical and actuarial skills to even know what to do . I'm interested in projects that are both challenging and relevant to the real world. I am 34 and currently pursuing Data Science and Machine Learning course from Scaler. Presently learning Data Analysis / Data Science, specifically SQL and Python but also keeping my eyes toward the future; and I am presently using MacOS and Google Sheets, but the more I read about the industry the more I see Windows-based tools being use, case in point Excel's PowerQuery (PowerQuery doesn't have all the functionality on Excel on the MacOS) and PowerBI (there's no such a thing Even though stats and compsci are said to be better bets, *you* can get away with an MS in Data Science or Data Analytics because you already have respect and rigor from the math degree. So yes analysis does play a part in industry at least in my line of work. For free I’d explore linkedin learning. I've only got a BSc but have 25 years experience in industry with many different systems, business domains and analytics approaches. The 6 data science classes are: 1: Ethics in Data Science 2. 25 years as a data science intern & later 1. Notation for math concepts. Understanding how the data is collected and what it represents is crucial. Brute force is the most basic numerical analysis method. That is when quality of data provided by the clients becomes all the more important. To learn data science for a finance career, I recommend enrolling in courses at TutorT Academy. ) This will be a point of contention because theres a broad spectrum of research philosophies that operate under the umbrella of ML. I want to do a Master's in Statistics that will prepare me for data analysis/data science jobs. ), but product analysts often have product intuition and domain knowledge that data scientists typically don't. - All reddit-wide rules apply here. The Common Admission Test (CAT) is a computer based test (CBT) for admission in a graduate management program. Merry Christmas to all celebrating it. It's easier than ever to get a grad who is strong technically - which means it's starting to become a poor way to stand-out. Pretty important for the theory. Similar to SAS. The past year I have been instructor-of-record for a business calculus class. It's not! Depends on the type of data scientist. Python Sorting and Searching Algorithms. financial analyst is different from a BI analyst, etc. Currently reading, quite good: Storytelling with data a data visualization guide for business professionals Knaflic, Cole Nussbaumer - Storytelling with data a data visualization guide for business professionals Knaflic, Cole Nussb Helen Winter - The Business Analysis Handbook_ Techniques and Questions to Deliver Better Business Outcomes In my years of working for Internet based companies, any forecasting has been very rudimentary and not involved the data science team. I use the following libraries in day to day number crunching : pandas, numpy, scikit-learn. But two pieces of good news for you: 1) statisticians tend to insist on a lot less rigor and detail than analysts and topologists do -- something that's barely a sketch of a proof in a real analysis class counts as a proof to a lot of statistics professors --- and 2) the people who actually took real analysis last year are going to forget half of it over the I'm a 2nd year software engineering student and I'm wondering if taking a numerical analysis class would be useful (it's not part of my curriculum). There are other departments at my research center that do have mechanical engineeers working on the real physical hardware. For a long time I assumed continuing higher education would provide me with that knowledge but now I feel perhaps getting some certifications and actually learning stuff that I'm more likely to use in a job would be more worthwhile than say doing academic papers. The only thing, it seems, that separates data science from these other fields is the use of machine learning. As a market, it's still not mature and there are many competing platforms from major players. Members Online Which transition makes more sense: data engineer to data scientist or data analyst to data scientist? Seconding ISL, R for Data Science, and Python Data Science Handbook - great resources. The lines between Data Science and Data Analytics are getting more an more blurred. Artistic_Row_6581 Seconding ISL, R for Data Science, and Python Data Science Handbook - great resources. It involves tasks such as data cleaning, preprocessing, and modeling to develop predictive models and make data-driven decisions. do you have explicit rating history for users); then you need to research existing approaches; then determine which approach is best suited for what you are trying to do; then you need to explore suitable metrics to define your success criteria I took general analysis in my undergrad and grad work which focused on analysis in single variable and multi variable. e. The Reddit Sentiment Analysis Data Pipeline is designed to collect live comments from Reddit using the Reddit API, pass them through Kafka message broker, process them using Apache Spark, store the processed data in Cassandra, and visualize/compare sentiment scores of various subreddits in Grafana. Very solid. It called for heavy familiarity with econometric models. I was operating under the assumption that analyst work leads to data science work. I got in for Data Science here and Cal, and Data Theory at UCLA. That makes a lot of sense, thank you so much! Those are some great examples and really clear some things up for me. Also real analysis is used in the proof of the Universal Approximation Theorem in Machine Learning. Even regression I wouldn't put in the hands of someone who hasn't actually taken regression analysis tbh, it already has a lot of pitfalls. Greatly appreciated. To start building one, you need to explore the data you have (e. Courses from grad school include: 4 semesters of real analysis, Applied Probability, Dynamical Systems, Ordinary Differential Equations, Data Structures and Algorithms (CS course), Numerical Linear Algebra, and Numerical Linear Algebra II for Data Science. A lot of it is just processing the data, cleaning it up, data visualization, running statistical tests that are already built within any coding language you use, interpreting your findings, and creating reports/presentations on it. I have learned everything i know data analytics/science wise through my current job and through some masters courses i took related to statistics etc and data analysis. Data Science is a popular and dynamic field in the interdisciplinary sphere. csv or . And can people in the comments spot implying MCMC is the same as MC. I run data science and data engineering projects for a global IT company. So if your current focus is on data analysis, and not building production pipelines, the R is faster way to learn and get up to speed for data analysis. Calculus. They'll be a nice GPA booster. The test consists of three sections: Verbal Ability and Reading Comprehension (VARC), Data Interpretation and Logical Reasoning (DILR) and Quantitative Ability (QA). Data analyst for cybersecurity is probably a stretch if you don’t already have some IT security background. Gembala 9)"SQL and Relational Theory: How to Write Accurate SQL Code" by C. 25 years as a data scientist. Using retail sales data to estimate product category sales effects of big-box retailers on retail sales patterns including buying l It’s going to come down to a bunch of factors. I've heard that it might help for machine learning / data science, video game development and some simulation related jobs, but is it really worth it? Thanks I work in "data analytics" world which includes data engineering and data science. Not much, if any calculus. I have written a few academic papers when I was doing my master's degree, but I have no experience in writing reports in business settings. A CS degree will include a lot of technical stuff you won’t need. Visit: Best Data Science Course 6)"SQL for Data Analysis: Using SQL and Big Query for Data Analytics" by Bill McFadden. Real analysis is the study of the real number system and functions that are real-valued using "analytic" methods. There is no “standard” route of entry into public health data science, but a master’s in data science or public health would be very beneficial in this field. json. g. The Python Data Science December is completed. true. Cybersecurity data is not immediately intuitive to most people. There are two things that matter the most when trying to break into Data Science: your (hard/technical) skills and project/work experience. And it’s just making me more confused. Math is good but you'll 'waste' time learning real analysis and proof based math. As others have said, data analysis is mostly just descriptive statistics with occasional correlational analysis and perhaps some very basic hypothesis testing (e. Yes, you can pursue a data science career in finance. I've been using it for a few weeks now, and I'm absolutely blown away. Coursera and edx have some really good stuff. Recommender systems are a domain of their own. Before plunging into the intriguing world of data science I suggest if you are not familiar with these concepts to do so before jumping in. It seems to me that if you take the entire job of a data scientist and subtract the machine learning, you’re left with some combination of data analysis and data engineering. Data Science is a broader field that focuses on using statistical and computational techniques to extract insights from large and complex datasets. For my degree, I’m supposed to take one of these course sequences: real analysis 1 and 2, advanced calculus 1 and 2, and numerical analysis 1 and 2. Rules: - Comments should remain civil and courteous. Basic Python Syntax of course. I'm wondering what projects helped you land your first job or internship in the data science field. i took RA last sem as a data science major, and i remember thinking throughout the semester that it was a pretty useless subject for me to take in terms of helping with my degree, so i guess it would be even less (directly) helpful for a computing major. Members Online Rant: ML interviews just seem ridiculous these days and are all over the place A space for data science professionals to engage in discussions and debates on the subject of data science. He said when the data science hype started several years back, companies started getting funds from venture capitalists to set up data science wings . It's one of the many reasons that "data scientist" isn't an entry level gig at most places -- you need to either have that theory in formal training (eg, econometrics at University), or have worked on real problems somewhere. 24 real-life Data Science projects using Python You will learn about Python & Data Science a lot It is beginner-friendly & free Below is the full list of projects. Numerical analysis looks at algorithms which can be used to approximate the solution. Data Science and Data Analytics are two related but distinct fields. I agree with @spacechannel_ below: the IBM Data Science Professional Certificate starts from a basic level, but their Final Projects in Courses 4 through 9 are quite substantial, covering: Data Science with Python, SQL / MySQL, Data Visualization, Machine Learning, and the final Capstone Project. Till now, I have mostly worked on projects from POC to market test / backtest. Some Probability and statistics. I'm finishing up Oregon State University's MS in Data Analytics, which is basically a computational stats degree with a computer science core. 7)"SQL for Data Science: A Beginner's Guide" by Jay Alammar 8)"SQL Analytics: A Guide to Analyzing Your Data with SQL" by Ryan L. Also most stats programs are taught completely in R and a lot of Data Science teams use Python. The Real Housewives of Atlanta A space for data science professionals to engage in discussions and debates on the subject of data science. It can be interesting and preferred by some. Real analysis feeds into measure theory which is the mathematical underpinning of probability. Go for the Data Science masters. 1 - Python — Analyze Your Own Netflix Data. In that case OP can arrange tutoring and support (now) for their repeat of real analysis. Throw in a finance class or two in your free time. SAS - Social science, Government, Pharma, Finance, more programming skilled is required. Math of Data science (a deeper dive into vector calc and stats) 3. Data science is more about understanding business by spending lot of time through the messy data and then if required, make a predictive model to solve a particular problem. I've taken the typical intro to probability, statistics, regression, data collection, data analysis courses and I've passed all of them (some like regression and data collection I did well since it was more applied, but intro to probability theory and such I had a bit of trouble) but I still have this feeling I don't know enough. Data scientist with PhD and 5+ years of industry experience. There were a lot of specific factors--it was sort of overdetermined--but to be honest I always wanted to work for myself. Data analysts need to understand and analyze the problems being addressed. heteroskedastic data, or serially correlated data). Data Science could be good but you'll get all of that and more in Aero/industrial E while operating at a higher level in a more competative environment. Bayesian Data Analysis and Doing Bayesian Data Analysis were eye-openers for me, plus McElreath's book on Bayesian inference and R. Data science attracted technical people into a role that is more non-technical. My public health data science colleagues come from a range of academic backgrounds including: medicine, psychology, economics, and geography. I’m in the tech world, so what I see as the difference: Depends on what you mean by serious data science. A space for data science professionals to engage in discussions and debates on the subject of data science. My current ASUS laptop is going on 7 years old and runs extremely slow. This data is usually so large that it requires help of computers to process and generate results. This is what the real analysis class will focus on. R - Data science, Statistics, Academia, more programmer skilled required. However, I have realised that their course curriculum is pretty bad. Want to be able to use Power BI, Tableau, SQL, Python, and R on it. 4. the big Oh, big Theta and big Omega arguments are very similar Data cleansing is a very big task and DOES need automation. Artistic_Row_6581 A space for data science professionals to engage in discussions and debates on the subject of data science. I'm reading a good book on data science right now. - Do not post personal information. Should I learn real analysis and measure theory? I don't want to be too academic MATLAB is really good at linear algebra, but in the real data science requires a lot more than that, so having capabilities in other areas benefits data science. For example Python data science users benefit from Python web developers since they can more easily extract data from online datasets, and can have tools like Jupyter or hvplot that Even in data science research, many studies are working on "the best model". There are use cases and snippets available online as well. The strong/weak law of large numbers, for example, demonstrates the subtle differences between form Data science community, I'm here to tell you about a new platform that's going to revolutionize the way you learn data science: DataWars. Depends on where you are (e. I have not had a chance to push the model into production. Masters isn't required for data science, but without relevant internship experience or a portfolio, it could be tough. "Real" data science DEPENDS on clean reliable data; it is NOT like taking a class where the data is provided. Java script is used in ML engineering / ml ops but not for the data analysis part. Political science is the scientific study of politics. On the other hand, I am doing a data science masters right now. My best subject in my undergraduate was abstract algebra (number theory being close behind). Data science is increasingly being used in the finance industry for tasks such as risk management, fraud detection, algorithmic trading, and customer analytics. however, i developed a list of schools—wisconsin, UC davis, penn state, ohio state, georgetown, arizona, washington, and notre dame—whose admissions website data science typically means people who can do all that analysts can do I see what you're getting at, but phrased this way it's incorrect. Date. Then i wanted to try transpo and luckily the job i got involves 50% data analytics/science so i wrapped up my masters in transportation engineering and have loved it since. On a different note - How to Win Friends and Influence People + similar. Just collecting, storing, and making basic data accessible is a more important hurdle to pass than modelling "Real" data science is a niche market The fruits of it are being commoditised in products. This brings up the need for computer language to write programs that will accomplish data processing tasks. Research is learned when writing and publishing papers. I was very good in Real and Complex Analysis, but I was not good at any sort of Applied Math. Should I take real analysis/advanced calc in my Stats MS? It’s required to take distribution theory, inference theory, probability theory, and stochastic processes. I still have very little actual knowledge of data analysis/data science. Fun fact — I interviewed for a Data Science position at Facebook and when I got to the actual stage of talking with recruiting and the hiring manager, they described a data analytics job. I see data science as a discipline which makes use of data to answer business questions and provide data driven solutions. Major would be secondary if your resume is strong on the first two areas. Jan 3, 2012 · I was recommended by a T. ) if I was going to pursue an MFE and especially if I was going to apply to an MFE program fresh out of undergrad. I might consider getting a PhD in Statistics after getting a Master's. So you can’t judge a job by title alone. I'm sure there will be some gaps. Members Online I understand most data science models, but not the math behind it and I struggle to explain them My question to you is: 1) Do you apply OOP on a daily basis in your data science work, 2). It's also crucial to understand the business problem. A subreddit to discuss political science. However, as the years passed, these data science departments failed to generate revenue as expected. What one company calls a Data Analyst another would call a Data Scientist, and they might call their Data Scientists something else like Machine Learning Scientist. 0 due to recently discovered vulnerability. They first taught us Data Analysis for almost one year (Python-Numpy, Pandas, Matplotlib and Seaborn), SQL, Tableau and Hypothesis testing and I don't think these skills are enough. Just like you're doing now, it is some sort of data analysis, excepting that you don't get to say your opinion or take a data-based decision, that would be the missing step so far). In the broader mathematical world, analytical solutions are either rare or not computationally worth calculating. Crew last year (recently bankrupt). Also if your focus is data analysis, R is more sophisticated and better vetted by real statisticians, than the packages and functions of the same names in Python. I heard that, in reality, data scientists are doing something different. For data science work, it's also useful to learn jupyter notebook environment as you can write code, see output and build reports in html all at one place. Have you thought about designing the database you pull your data from? I'm a full stack developer looking to get into data science and one of my seniors recommended I actually design a database instead of using a . one thing it has helped with is the intuition with computational complexity. I am well-versed in Python and a little bit in R, which was mostly used for statistical analysis rather than exploratory data analysis. I've been applying to a ton of Data Science and Analysis jobs but out of maybe 100 applications I've gotten just two phone interviews that went nowhere. Much more of the day to day work in data science has to do with moving massive amounts of information around, using roll up data like in RedShift, or using other libraries specific to the kinds of data you are dealing with. Most people in the US and Canada can get a free access through their public library. Python for Data Science and Machine Learning Bootcamp Did it myself and was pretty good. It deals with systems of governance and power, and the analysis of political activities, political thought, political behavior, and associated constitutions and laws. It is a new area for most businesses. Most data scientists are abstracted away from the math/theory of ML unless they're working on the absolute cutting edge. The thing is if take the real analysis sequence l'd have to drop several data science courses that feel would be more applicable to industry. You are probably right, I don't see why Data Analysts should be such great experts in SQL when the role is 80% cleaning and the rest analysis and producing reports. My department basically gives two choices for new hires, become a data analyst or code software for the data analysts to use. I saw an ad for a data science position with the clothes company J. J. Python is great for data science work. Analysis is mostly used in ML to prove convergence. Apr 22, 2020 · 10 Subreddits You MUST Join on Reddit if you are a Data Science, Machine Learning, Deep Learning or Artificial Intelligence Enthusiast. It's the reason why we are at r/datascience and not r/statistics and why the job title is data science and you're doing machine learning etc. The answer to "how does it work" in data science is empirical. And why you're paid 120k/y while a statistician at a government agency (or at the university is lucky to break 50k. There are many, many tools to do data analysis and which one you use depends totally on the needs of the client/company. There are two classes of real analysis, the first class covers the first half of Baby Rudin, and the second class covers the second half. To be hired as an entry level data scientist with no advanced degree, I think it's almost a prerequisite that your degree is in one of Math/CS/Stats/Data Science but that some exceptions could be made for Econ/Physics/Biology if you have modeling experience in that specific domain. Data science literally requires phd based skills taught in a university. 96 votes, 25 comments. I spent some time learning the difference between the two and which skills I need to acquire to become a data analyst (with the plan to ultimately progress to more advanced skills for data science), only to look at job descriptions to see what they’re asking for and finding the lines being very blurred. I am currently doing an MS in IO psychology. Data analysis and data science are related fields, but they have some differences in terms of scope, methods, and skill sets. to take real analysis (Course would cover: real analysis; real numbers, point set topology in Euclidean space, functions, continuity. But, that's normal. all the before AI came along and basically made data science as a job a lot less relevant The problem is, a lot of high end science is becoming sophisticated data analysis in disguise. I knew some other independent consultants and I noticed that they had made their transition to freelance at around the ten-year mark in their careers, which is when I wound up doing it as well. , nothing 514 votes, 66 comments. Data Science isn't only a largely exploitable profession, but it’s also one of the most economical career options. I have been doing research on getting a new laptop which I can use for Data Analysis at home. However, it'd be hard to for you to read academic papers (eg: kernel methods) if you don't have the knowledge. I have just completed Great Learning x MIT's Data Science and Machine Learning: Making Data-Driven Decisions program and here's my 2 cents: Pros: Covers foundational to advanced topics in data science (Python, Probability, Statistics, Machine Learning, Deep Learning etc. I tried both. Second part is focused on data science using sci kit learn, nltk and keras. To summarize, Data Theory is very math heavy (real analysis, statistics, etc) and less coding (only a few courses in R and Python for the most part) and it is designed for you to go to graduate school. Here's a brief overview of the differences between the two: Scope: Data analysis focuses on analyzing, interpreting, and visualizing data to extract useful insights and make data-driven decisions. Was it your own choice or were you motivated by your team to use OOP? I am asking this question to get an impression what is the current usage of OOP in data science, and therefore would very much appreciate comments on your experiences. Even when content isn't directly helpful, it will be helpful to be at the level of mathematical maturity that real analysis teaches. - Do not spam. 2. Cause deadlines and expectations are always going to be high. Members Online For R users: Recommended upgrading your R version to 4. Most data scientists are applied data scientists and use existing algorithms. In the corporate world, would you see network analysis as a job function within data science for certain projects or would you see it becoming a job itself where companies hire network analyst to only do network analysis for them? What you said at the end is what I have been noticing. university) in data science, I'm wondering where can I learn the real-world practice of data science. 5 years for undergrad is really quick, I bet you could crush a masters in a year with some planning. Even if you're looking for non-Bayesian approaches, there's a ton to learn and appreciate. So would the analysis courses help me get in or should I focus more on my programming skills that could use later on? Julia data science language R data science language C++ computation language Lacks just java and Scala and then we have the full DS language community together. Using severe storm weather data to measure economic impacts on consumer sentiment and retail behavior over a 5-year post event window. A few responsibilities of my job include data mining/analysis, predictive modelling and writing reports. . I have a data analysis/data science profession. On the other end, data science is a research role. Another possibility: perhaps the department already has a plan for what to do with people who can't pass real analysis. The base classes for the data science degree here at ASU includes 1 statistics class, calc 1-3, basic programming in Java, object oriented programming in Java and 6 data science classes. These involve limits, which involve taking things to infinity, which use analysis. Especially if you want to make any sort of causal inference it starts to get very easy to make mistakes with endogeneity, simultaneous causality, measurement errors in X or Y, marginal vs partial effects, moderation and mediation, any type of sampling or I'm looking for a job or internship in the data science/analytics field. Tableau is a data visualization tool which can incorporate other aspects of data science as needed, such as using python scripts or R scripts to do data manipulation, or even some bit of model building on the fly. If you're complaining about it being hard to find data suitable for analysis, then what you're usually talking about is that the data is messy. I might add that I am assuming data science jobs that deal with "big data". Some in my teams have PhDs, some have MSc, some have BSc and do industry training like AWS certification. Over 94% of data scientists in 2019 had a PhD or masters, with the remaining few having a direct DS degree that teaches these skills with less years of course work. Linear Algebra. With DataWars Live Labs, you can: I have been working in data science in the retail industry for almost 3 years, the first 1. If you are more of applied data scientist, it's more just statistics, programming, data experience, and general data science skills. There's also a toned down real analysis class for those without good enough grades to take the main real analysis stream. It unnaturally talks about taking a scientific approach to managing, recycling, and interpreting huge data conditions. I have become increasingly interested in data science and machine learning. Apr 5, 2016 · Some people say data scientists don't necessarily need to know real analysis and measure theory, but for others, real analysis and measure theory are very important for the undersdanding of kernel methods, stochastic processes etc. Analytics and modeling. Depends what you want to do. But they aren’t free. Data science Consulting is a different ball game altogether when compared to being an inhouse data science team at a product based company. But data science seems to be much more heavy on the programming / engineering side of things, which I don’t enjoy very much. For bioinformatics, you will certainly need a masters or PhD. First half is your typical data analysis packages like pandas, seaborn and matplotlib. i understand real analysis is an extremely common and vital prerequisite for the PhD, and i understand that taking it would definitely help me if i was lucky enough to pursue a PhD. Companies went on a hiring spree with this money to hire data science professionals. I'm quite comfortable with scikit-learn and PyTorch. In terms of practical on the job value, it depends. Since I have already been trained through formal education (ie. Best case might be if there was an option to retake real analysis next semester while continuing to make progress on the other coursework. Hi all, I’m a rising junior double majoring in mathematical economics and data science. So if you studied real numbers with things such as cauchy sequences, convergence, completion, sup and inf, and proof-based calculus, then you've done Real Analysis (there are also many levels to Real Analysis, so you may have to Even regression I wouldn't put in the hands of someone who hasn't actually taken regression analysis tbh, it already has a lot of pitfalls. This is a place to discuss and post about data analysis. Most of the data science projects will not see the end of the tunnel. You should be able to pick the math necessary for that pretty easily as you don't need anything more than 8th grade math to understand (i. ML is now routinely used to speed up synchrotron data analysis, and optimize experimental parameters, and speed up multiscale modelling. Those programs are an intersection of stats & computer science and focus on the knowledge you’ll need for an advanced analytics or DS role. Software Engineering or Data engineering path is more stable and satisfying. Yeah. You can also use Monte Carlo simulations when you are comparing a series of different models performance, on average, with a lot of data you simulated (fake data) that has a particular problem (e. During this time you should be able to refine your soft skills and get a solid understanding of the non-data aspects of the business and how these connect to the technical skills you have, and how to drive value to the business through the application of technology and data science. - No facebook or social media links. Data analysis is extremely boring to me. Feb 24, 2024 · Thus if you are analyzing a function that computes or optimizes a function by producing sequences of points, you need real analysis. You don't need it for most practical applications in the same way you don't need to understand lens optics to take a good picture: most of the time you can follow the rules, but having a firm foundation in the theory helps you recognize when and why you sometimes have edge cases and helps avoid However, the actual data analysis part makes me feel like I’m only utilizing 10% of my programming skill set. A. I've been in the data game too long to get excited about the overly intellectual and academic data science-y articles these days so my own preference is for blogs that help show analytics being used in the real business world. I was hoping some of you who ended up in DS might lend me some advice. Working successfully on a team can be EXCELLENT EXPERIENCE. Recent took up learning numerical analysis and more specifically numerical linear analysis in order to solve over determined equations with minimized errors. And, being able to demonstrate an ability to perform analysis on messy data is the 100% most useful, most likely to help land you a job skill that you could demonstrate. It's very hard to do meaningful work if you have no grounding in a domain-specific theory (eg, some social or physical science). It's the most immersive and hands-on way to learn data science that I've ever experienced. , t-tests on two means). here’s a free Most of the data sources I've come across in my previous master's program or on Kaggle are too clean or somewhat incomplete that don't really mimic the real world (such as having 100s of columns, which were probably cleaned out but might have something useful or just other nuances in the data that may have been removed with cleaning). kvx zbtp ymdfh kgxeq ykdo qhyrtr duxcm zsxwp yrjg vcrwcg