How long to learn python for data analysis reddit.
How long to learn python for data analysis reddit You’re asking to learn calculus without studying math when you say you are starting out with 0 programming knowledge and you only want to learn data analysis. for Thanks for the detailed reply that’s rly insightful. Which is used for analysis and data science, but there are other things Apr 20, 2023 · Introduction. But to really nail in the coffin, if you do some personal projects and put it on your resume, that would basically seal the deal. So I started with a strong understanding of calculus and linear algebra, an OK background in statistics and probability theory, and a basic understanding of traditional ML models like SVMs and neural networks, as well as one python class under my belt. The reason few people become ML engineers right out of uni is that good software engineering begets a lot of experience. I am hoping to complete the current courses soon and start learning Data Wrangling and Data Analysis with Python. I'm a 30 year old… Jake VanDerPlas has some great YouTube videos on data analysis with Python and his book is a fantastic resource. 1. I am learning data analyst skills online as a self-taught data analyst currently, I know about mysql and power bi. I've been learning SQL (MySQL) and want to also learn Python to have something to show for potential employers since I'd be new to this field. Learning style: Some people learn better through hands-on experience, while others prefer theoretical explanations. Thank you for categorizing the tech that you listed. If you wanted to scale beyond a single machine or hand off a pipeline to non-specialist corporate IT, they wouldn't touch Knime. Pandas is mainly used by data scientists who work with sample data to develop models. I know it's 300 hours to complete so I was curious about people's experiences before diving in. When you feel like you need complex data types, I think that is when you want to learn about OOP. I'll give you a few examples of where I would use Python to enhance my data analysis: This is a place to discuss and post about data analysis. Prior to learning Python, I was doing data analysis/clean up with Excel and SQL. I have a doubt can one be a self-taught job ready data analyst in 3 months, I am doing this full time and 3 months is the deadline set by me. It's very challenging. I dont manage databases (a little bit since my company uses google cloud stack and we get good permissions to fool around and create stuff for our own). What I liked so much about the course was the sense of immersion and teamwork (although artificial), it gave me a sense of what Data Analysis is about, what it's for, and who it's for: the stakeholders. Tidyverse way of R is super easy to learn and immediately applicable and useful for data analysis. You can use Python in the web, but for the backend (create API's etc. ), and it is commonly used. I would honestly just start watching like a multi hour video on all of those 3 and then doing research on all of the above. 64 votes, 123 comments. My ultimate goal is to use them to develop a web app or contribute to open source packages in the data analysis or machine learning space. I think splitting projects into the data processing and reduction (SAS) and analysis and optimization (python) parts is a good general approach to begin. I'll also say that I was already familar with the data, work flows and goals I needed to accomplish. Power BI, Tableau, Excel, Python, SPSS, SAS, R, Stata, orange, and many more, are all just tools used to do data analysis. But if you're asked to collect and analyze a lot of I started learning programming/Python and ML in my free time after I finished my final exams, got into a Data Science masters (1-year course here in the UK) off the back of the math/stats content in my degree, did well in the masters and got the first Data Science job I applied for at the end of the course. Often data is never in the right format you need it to be. And Open Source Society's list of classes in computer science. Here's another anecdotal story :) I started in public accounting, moved to accounting manager, and took over the system admin/development of our reporting software and then started to dive into administration over our ERP as well. Pretty solid with SQL and Excel as a result, but I’ve also been learning Python on the side to expand my skillset. It made more sense to do the whole analysis in an R or Python notebook. 3-B) Data Visualization with Python: Visual Arguments. Data Analysis with Python Here you learn about the Python libraries that are heavily used in the sciences: NumPy, Pandas, and MatPlotLib. Python will be more useful later on in a data analysis career, but it won’t replace SQL, so you’ll start out early in your career likely using a lot of SQL and Excel, then move on to use a lot of SQL and Python later on We would like to show you a description here but the site won’t allow us. 3-C) Data Visualization with Python: Seaborn. Learn python and learn it well. Shortest recommendation: SQL then Excel then R. I would say they are technically lightweight. Again, it’s all taught in videos, but the projects are much more straight-forward (the only issue is that they have to be made in Replit, which has its issues). And maybe then learn python. Loops, conditional statements. I was learning how to code last year to become a web developer, but I'm more interested in data-related professions. I have 5 years of analytical experience but still decided to enroll in this course, mainly to brush up on the methods and processes of data analytics, as well as to learn R. Data scientists who learn SWE are as valid as software engineers who learn the ML/Modeling/Data/Math side of things are as valid as Data Engineers who learn more software engineering and Modeling. - Do not spam. My programme has a course in Python, to learn how to use for data analysis and stuff like that, but I've been advised by an older student to learn the basics by myself because apparently my uni doesn't really teach them and just goes straight into more complex stuff straight away. - All reddit-wide rules apply here. The reason I asked the question here is to find out if Python skill is vital for DA in RW. Mainly used SAS, SQL, a little bit R for infectious disease surveillance data, 90% of time spent on data processing, reporting, quality and visualization. Still afraid that I lost my passion for being a data analyst, I started learning Python from Data Camp courses, and I have a problem understanding from the courses! I've seen the good with the bad. 5M subscribers in the datascience community. . To answer your question, if you want to be a data analyst, start with Excel. How long would it take to learn SQL to be considered for data analyst role. (Even though NASA looked like they were using it for their space projects lol) This is a place to discuss and post about data analysis. They also have different analysis pathways available (Python, R, Microsoft BI, Excel). That being said, in a data driven organization with high data culture and learning it can be great. Or more python specifics like enumerate. For example,most python books introduce class and a bit about object oriented programming but you may not need to learn it. And yes, I also know machine learning with Python at the beginner level. Get a bunch of projects under your belt and find new ways to use your skills at work. The different courses/modules were created by seemingly dozens of different people, with no consistent teaching style and mistakes littered throughout the entire thing, both design mistakes and English mistakes. Build Connect Four Using Python (Skill check!) 3-A) Intro to Data Visualization with Python. Libraries like NumPy, Pandas, Matplotlib (among many others) are fundamental for data and statistical analysis tasks in Python. It’s impossible to say without knowing the specifics of the question and how familiar you are with the subject and available data. I learned Basic R in I would say 3 days and that would include making basic data modification, simple plots with legend and title, building models like linear models and test like chi square and anova. I had to stop myself halfway through my response about my top 3 Excel websites. Long run, R will give you a lot more value for completing the full data science life cycle, but having had to learn the basics of both almost simultaneously, I would recommend that you stick with python until you're able to take a problem, and find the full solution with just python. Right now I do not have enough time to upgrade my Python skills. One of the most widely used programming languages today, Python’s popularity is rising for all good reasons. Can anyone share roadmap for it ? After learning it for data analytics i also want to learn it for automation tasks. Learn Python 3. Excel can do a lot. 79 votes, 35 comments. 100% of the data cleansing tutorials and walkthroughs that I have found through Google, Medium/Towards Data Science, StackOverflow, and YouTube were never sufficient for my Learn Python/SQL in a data analytics / data science context and you're probably good to go with that kind of background. I'm very interested in strengthening my data analysis skills, so my choice is between studying Excel for Business or learning Python basics. Thanks in advance. Often your work is never utilized or used. In addition to learning SQL, Python, and R, I'd also suggest finding a public dataset on a topic you are really passionate about. If any Data Analyst is here also mention that. 5) Learn Intermediate Python 3: Object-Oriented Programming Python is the primary language that a lot of people use for data analysis, data cleaning, etc. Python For Everybody. But it really depends on your study habits and to what extent you want to learn Python. You may know how to break down and manipulate the data, but knowing a bit about the item the data is coming from will go a long way in knowing what data to present and why. A data analysis script probably in jupyter notebooks where you have import a dataset, perform data cleaning, data QC, basic data investigation, some analysis and manipulation, some charts, statistical test and summary analysis. The best way to get confident in any programming skill is to work on actual projects. Technical skills are great - I love learning about Python Pandas & Jupiter notebooks from other courses. If you do decide to learn some Python and SQL beforehand, I recently posted the following on a thread asking about good resources for learning Python. These two libraries are practically the key (along with things like sci-kit learn) that makes Python the tool for data analysis and machine learning. This course focused more on regression analysis (think L1 and L2 stats), but in Python. Some weeks or a few months (spread out over some time) learning the basics. So I'm finishing my data analysis class for sociologists and we've been studying spss (24), the deeper we get into it the worse it seems. “Garbage in, garbage out” as it is said, is the first rule. Do you guys have any recommendations on course for someone who wants to learn python and geared towards analytics/data? Sorry for the long Pandas is ill equipped to handle the size of data that data engineers work with. It is a beginner-friendly yet highly potential programming language used for various tasks across numerous industries, such as web development, data analysis, machine learning, and artificial intelligence. If you eventually get on the track of python, I’d say check out pandas & openpyxl. Additionally if you need to integrate legacy panda code pyspark has apply pandas functionality. Collected my own data <snip> played with publicly available data that interested me Would love to hear what you did with this data/projects, etc. Lots of programs have it combined. However, learning how to manipulate data with Matlab, R, or python will serve OP well in the long run. But learning the basics of programming can take quite some time; depending on your schedule, spending more than a year to have a solid grasp on the basics is pretty normal. But prioritize sql then maybe powerBi if you can and finally dive to python. R has more mature packages for statistical fields such as survey data analysis or design of experiments. Played with random data before I got a job. I also started learning Rust around the same time and although it is harder, it still has a good type system like F# and it is under active development and only gaining more user share. I've been programming in various languages for almost 6 years. Anyway, I like this Also had the same experience with learning curve, took 2-3 years to really get shit going fast and fetch data with confidence (mostly consuming/manipulating data for a specific use case). 5 hours each class depending on whether you took the 2 or 3 day version. Hey! We have just tried to provide answers to a few questions that beginners may have- for example - Why should they learn python, what all resources are available to them, what are the topics that they should concentrate on in the beginning and so on. I do analysis for a fleet department and a big part of getting this role was me knowing about fleet operations pretty well top to bottom. Python is the most popular in the industry while in the academe, it's R. And then slowly convert everything to python. Hello, everyone I started to learn Data Analysis from Data Camp and watched Tutorials online. I have a basic knowledge of Excel and zero about Python or programming in general. I finished the tutorial in the latter half of last year. As you learn more and get better with sql and the visualization software, it will make understanding python much easier because you will come across an actual project where you may need pandas and numpy (python libraries). Open Source Society List of classes to take to learn advanced data science. 10 votes, 11 comments. I’m in a data analyst adjacent role, but want to become more technically adept. i definitely put in more than 3+ hrs a week to study this but 3 hrs would not even be enough for me to read a whole chapter. Thanks Recently laid off. Get the Reddit app Scan this QR code to download the app now. The best way to learn data structures in Python is practice, practice, and more practice! But seriously, you might want to try a variety of resources to better absorb the concepts. I see more job descriptions wanting Tableau vs. WEEKS 1 - 12 LEARNING PYTHON FOR DATA SCIENCE (4 Hours a Day - try and do 1 project in each of those weeks that builds on each other) WEEK 1- 12 - STATISTICS AND PROBABILITY (look for a course on coursera or edx - spend at least 2 hours a day) WEEK 1-12 - INTRODUCTION TO COMPUTING FOR DATA ANALYSIS BY GEORGIA TECH (spend 2 hrs a day) 54 votes, 48 comments. If you can afford a copy of Microsoft Office, I would start at just using Excel. Basically as soon as you need a snippet of R or python, it didn't make sense to even start an analysis in knime. g. I would class JS as your king of the web, and Python as your swiss army knife. The common programming lanfuages in data analysis are python and R. When learning SQL I was using data from their DB2 database, for which the servers were often down. I liked statistics in college (though I don't When learning SQL I was using data from their DB2 database, for which the servers were often down. 4) Learn Intermediate Python 3: Functions, Namespaces, and Scope. Roadmap for Learning Python for Data Analyst Hi I need to learn a python for data analytics. I've found the resource instrumental in learning basic programming and analysis because it's more about application and project based learning. If your interest is in Machine learning, automation and/or data, then Python is the way. There was a “crash course” section and everything explained and taught was pretty clear, but I still feel like I need to get deeper into the fundamentals before I actually take on data models in python. Would love to hear any thoughts on this - Did 15 days of "100 days of code with angela" in python - Did 50% of CS50 Python Course - Did 50% of "Python for Everbody" in Coursera - Watched and followed some python videos in youtube. Get a Google Data Analytics Certificate (3 days left about to finish) Study Excel and become familiar with data analytics in Excel Learn SQL for a month or so until I feel competent enough. But if you're asked to collect and analyze a lot of Scientific articles, and depending on the journals data policies, have replication data available for download. You’ll see that your familiarity with excel will pay off. I second this. I’ve been doing exploratory data analysis with some public datasets, but not sure what else to do beyond that. 156 votes, 50 comments. org Apr 22, 2024 · For beginners seeking a clear roadmap, our Python for Data Science course’s structured learning path will guide you through the fundamentals of Python programming and its application in data analysis. Although, I think Python needs something better than matplotlib for visualization. Whether to learn R depends on your field and how much of the work is focused on statistics vs just general developer tasks. I’ve been working as a data/reporting analyst for the past few years and don’t make an awful lot. I found Data Camp to be pretty good for quickly learning basics of some insightful data work (such as Network Analysis, Time series analysis, NLP). Check out books like "Python Algorithms" by Magnus Lie Hetland or "Data Structures and Algorithms in Python" by Michael T. Some other skills you may want to focus on include statistics, machine learning, and data visualization. I deleted and asked if they would consider learning Python instead. If you are dedicated still to learning pandas learn the basics and move on. IBM Data Analysis in Python - 3-4 weeks - At this point, I breezed through all the introductory stuff. As for Python, I'm still learning it, however I learn it from the "general" side, learning the basics of programming that is, but some long time ago I was going through Datacamp courses which focus mainly on data analysis and visualisation with use of packages you have mentioned Somebody who has programmed for 10 years in 5 different languages will learn the Python basics in like a day. I've attempted to learn data analysis through a book "Python Crash Course, 2nd Edition: A Hands-On, Project-Based 63 votes, 37 comments. It can do database stuff like SQL, it can do data analysis like R and MatLab. Then I got bored after a few years and started learning Python and data science through online courses. Also, one more thing OSSU's Data Science Degree equivalent program. I was wondering if anybody has completed the course "Data Analysis with Python" as a complete beginner and how this went. PowerBI as their data visualization tool, and see more demand for Python vs R. I learned Python and databases first. That being said, I think I still prefer Python more just because you can use it for more than just data analysis, and the data analysis packages there are super easy to use too --- pandas/numpy mostly. R was similar, although we were taught R strictly for data analysis, prediction, statistics. Spent a few months practising what I heard learned here. A space for data science professionals to engage in discussions and debates on… Hi everyone, I'm going to be starting my Physics BSc this autumn. I have used Python for some of my projects, and I have even… 643 votes, 37 comments. It's a lot easier to understand how the data is organized and what the relationships between the tables are if you are excited about what you can discover from working with it. However, learning Python helped me out a TON in production. My point here is I think Python for data analysis can be learned by anyone and is worth the slight shift in skills. It's great for learning the basics of data cleaning, pandas, and some simple summary statistics and plotting. Jan 15, 2025 · Goals and motivation: Knowing what you want to achieve with Python (e. I have been teaching myself to become a data engineer for a few months now, but I wanted to know if I am wasting my time… In addition to learning SQL, Python, and R, I'd also suggest finding a public dataset on a topic you are really passionate about. I’m trying to gauge just how in-depth I need to go into Python’s core language features before I can use it professionally for data analysis. Thanks! Both were taught in Python although neither were specific to just data analysis, but teaching programming. Anybody who takes the course as a beginner will need to learn a lot more Tableau, Excel, SQL, R or Python in order to really prepare themselves for a data analyst role. Goodrich. You will not become expert after completing these courses but you will be able to get "foot in the door" to do some sophisticated analysis on your own later. Learn how to code in Python & R for as long as it takes, enough to get me a job. code to understand a particular learn Python book that relates to your field of interest. Machine Learning Engineer - who assembles and mass produces the car Data Scientist/Modeler - who drives the car to take the customer from A to B Business Stakeholders - a customer who says I want to go from A to B Data Analyst - who tells the customer that there's a nice restaurant in B, we should check it out Learning Excel will help you a lot in data analysis (and would be the defacto starting place if you're truly wanting to do data analysis), but it won't help you much in programming. In particular, I would recommend Matlab or the free alternative Octave to OP, as it is extremely easy to learn. As an entry level analyst, you will likely work with data that is ready for analysis/visualization. There are 4 courses total in that series but you can take each individual. - Passed a Python programming class in my University I am now in the part 3 of the Google's Advanced Data Analytics "Exploratory Data Analysis with Python" Since then, I have completed a 3-year big-data/NLP research internship, co-authored one publication in NLP/transportation analysis, and have completed a few other data projects using R, Tableau, and Java-- all of which I gained some good experience in data analysis/wrangling. Weirdly absent functions like the ability to calculate multiple percentages at once (the closest there is are crosstabs), clunky recoding, ugly graphs and so many problems with actual statistical calculation that we just use Exel. I have been thinking about getting into data analysis and was wondering a range of about how long it would take me to become employable studying 30-40 hours per week? I am looking to either go to college for 2 years for something else other than data analysis or doing self teaching mixed with possibly a bootcamp. . Right now I’m working with the SEC database and entails webscraping, parsing data, outputting data to TXT/CSV files, using Pandas Dataframes to structure the data, and then I use Power Query with Power BI/Excel to visualize everything. Rules: - Career-focused questions belong in r/DataAnalysisCareers - Comments should remain civil and courteous. I am a fresher got graduated last year BTech mechanical want to switch to data field. If you want to be a programmer, start with Python. How long does it take to learn Python: a rough estimate. I didn't study CS, except for some intro class as I was finishing school, but I know at my uni they have DS+A as one class and it was usually a 2 or 3 day per week affair, either 1-1. , web development, data analysis, automation) will help you focus and stay motivated. As for learning resources, "Python for Data Analysis" by Wes McKinney is an excellent primer for data analysis in 349 votes, 70 comments. Honestly you should learn python but that’s tier 3 in the learning specs. Skills in sql is also a huge advantage at least intermediate level. This will get you up to speed on Python, Pandas, and some data visualization libraries in Python. I would say learn python to solve problems you have rather than spend time learning its libs or syntax. Figure out a project that is novel to you so that you can apply those new skills to. By completing this course, you'll gain the knowledge and practical skills required to confidently embark on your data analysis journey. Harvard through edx has a free set of classes called cs50, there's the main one then there's specific SQL and python ones. When I learned Python last year, I learned the most from Mosh Hamedani. In my opinion, the purest way to try and learn finite element analysis is by manually creating bulk data files for a nastran solver and manually post processing their results as punch files in matlab or your programming language of choice. Its good for problem solving and a thorough understanding can help you a lot in the long run. This is where I was able to learn how to use python to clean/manipulate data in excel. I left a math PhD program to become a data scientist. As for learning resources, "Python for Data Analysis" by Wes McKinney is an excellent primer for data analysis in To start, it's great that you're already learning Python as it is one of the most commonly used languages in data science. If you want more out of your tools, like any kind of statistical analysis (regression, machine learning, feature importance, predictions, etc) you can use Python libraries like scikit-learn, stats-models, or others. It makes analyzing data a joy. There are many resources available online for learning data science, including online courses, tutorials, and blogs. 95% of what people envision as "FEM work" is literally just pre-processing a solid mesh to fit inside a This is a place to discuss and post about data analysis. Often you have to redo things when there are not clear directions. It is, by far, the best introduction to Pandas out there. Based on these I took a number of courses from the University of Michigan in Data Science, and Python, one called Applied Data Science with Python (it's a multi course specialization), the second course in that series is Applied plotting, charting and data representation in Python. Scope creep is real. There will be tons of different tools and skills to learn depending and what type of quantitative analysis you want to do. That being said, Python is by no means lacking in terms of data analysis or statistical capabilities. I work full-time and I’m continuing my informal education in Python, specifically for data analysis. Assuming I reserve 1-2 hours a day for learning, how long would it take to become competent in SQL and python? Just enough to meet the requirements for a junior role. This is a place to discuss and post about data analysis. After doing the majority of Mosh's 6-hr Python youtube tutorial, and taking a short detour to Corey Schafer's python OOP series and learn a little MySQL, I came across freeCodeCamp's Data Analysis with Python curriculum while trying to avoid going back to LeetCode to get my butt handed to me. OSSU's Computer Science Degree equivalent program. This also means that the data is already in a ready format for analysis (and the replication script starts with this data set). Hey there! So I'm becoming more aware of my lack of skills and want to really dive into data analytics. The learning pathways are just multiple courses strung together - not necessarily coordinated - I have done the ones on Python For Data Science, Data Analytics in Power BI, Introduction to Machine Learning and I am now doing the one on R for Data Science. Or check it out in the app stores [Python for Data Analysis, Python for Machine Learning, etc. I have a certain grasp on calculus and statistics, even if my university courses aren't heavily focused on these subjects. For example, I learned Python from Mosh’s channel - enough to get grounded - then moved on to learn Pandas and processed a lot of data to turn unstructured CSV data into tables for a database I also had to figure out how to design. A little modeling in logistic regression and survival analysis. For data scientists popular starter projects are financial analysis of housing prices for your area or stocks. Learn the things that excel can’t do so well. Python for Data Analysis: Pandas & NumPy. I also recommend learning SQL. Sometimes I get questions that I can answer in 15-30 minutes with 1 SQL query. - No 3rd party URL shorteners Python also has more mature deep learning and text analysis packages. a) 100 Days of Code: The Complete Python Pro Bootcamp for 2022 b) 2022 Complete Python Bootcamp From Zero to Hero in Python Please someone guide me in what will be the best option for me to learn Python and its fundamentals in less time. Other classes are cloud computing with spark and then an intro to bioinformatics one which is really mainly just biology (This one is a no brained for me). We would like to show you a description here but the site won’t allow us. To automate the analysis of the data I decided to dedicate a bit of time to learning python but I'm not sure where to start since there are so many sources. Looking for a career change. If you want to be a data engineer, you're going to need to need this more. I also was working with Tableau on data visualization. Scientific articles, and depending on the journals data policies, have replication data available for download. 5) Learn Intermediate Python 3: Object-Oriented Programming This is a place to discuss and post about data analysis. I have been learning Python for the last 3-4 years as well (no professional experience here). You want to learn basic concepts of working with data before you move to more advanced topics that require the use of different tools. - Do not post personal information. Pray that he researchers have an R-script and a log-file. Also am taking a IBM Intro to Data Science course on Coursera too. And these few tips can really make a huge difference in a student's life when he is trying to make an informed - Did 15 days of "100 days of code with angela" in python - Did 50% of CS50 Python Course - Did 50% of "Python for Everbody" in Coursera - Watched and followed some python videos in youtube. Also, depending on field, AWS, Salesforce, Google Analytics Tags, etc. I’m trying to get ideas. Python is a good beginner all-purpose programming language, you can build anything with it, including doing data analysis. 349 votes, 70 comments. Last month, my focus was on Python and now I'm learning about SQL. Prior to that, I was trying to make a career switch into Data Analysis. 45 votes, 16 comments. So if you get answers like "I learned it in x days" it doesn't See full list on coursera. Also had stats classes before learning R. You’ll be able to run queries using python and do data visualization and a lot of cool stuff. If I really need to understand the ins and outs of a set of data and want to make high quality visualizations, R is a top notch language. I took the Google Data Analytics and Excel for Data Analysis by Macquarie University (Power Query, Power Pivot, DAX, Power BI), took the first course of SQL for Data Science by UC and did the exercises for SQL on hackerrank, w3resource, sqlzoo and sqlbolt. I'm so glad! :D Actually, it didn't take me that long altogether. Apologies if this is a dumb question or not the right subreddit for this. So trying to answer which one "best" is a tough answer but there's no clear cut "winner", but all 4 are going to be useful skills for a long time. Lots of data analysis will assume you have some basics for python and programming. I'm really early in my learning to program journey (20ish hours of YouTube learning) and want to learn Python specifically for it's data analysis abilities. Data analysis begins with data cleansing, and data cleansing is the daily grind for most data scientists. I have a fairly good knowledge of Excel and have been learning power query recently. Python is great and it is easy. Anyway, both classes were about 30 hours each of in-class lectures plus roughly 100 hours each of working on assignments outside of class. This is real life. R is not python, but they have many of the same strengths and weaknesses in a data analysis and modeling context. Kirill has beginner-friendly data science courses on Udemy. The python one is pure programming, you might be better focusing on jupyter notebook and pandas in python for data analysis I felt productive in Python in about 6 months, working about 1 hour per day most days of the week. You must learn sql first to get the data the. - No facebook or social media links. Descriptive stats to understand basic ways to look at data. Learn a visualisation tool like Power BI or tableau. Rules: - Comments should remain civil and courteous. As someone whose pretty familiar with python, but not so much Excel, I would learn Excel first. true. There are data related libraries for Python like pandas, numpy, etc. This is a career change for me. vbogerxxpzzrfqvlsjcndwpholbyhhpkvwyqmjlttebymovxkh