Python for data analysis reddit.

Python for data analysis reddit However, there are four major types of analysis: Descriptive analysis uses previous data to explain what’s happened in the past. Alternatively, installing the Miniconda distribution, which just contains python and conda, is likely a better Today I think R will limit you to work only with tabular data and do "data analysis" only. 23 - Anomaly Detection. In fact, this allows for very fast dashboarding if your data fits the data model desired by PowerBI. Meanwhile, Python gives you all the freedom you could want which is ideal for more complicated statistical analysis. - No facebook or social media links. In my experience, many firms are willing to look over your lack of data analysis knowledge if you have strong foundation in the field. Everything that's new, is done in Python. I have tried learning Python previously through books (Learn Python the Hard Way) and websites like codecademy and data camp, but this course really helped me learn all the fundamentals. But the data cleaning part is much easier in python. I’ll go one further and say that once you have some SQL chops you should explore data visualization before Python. Is data cleaning with python much faster than power query? I am slightly more familiar with python, but because everyone at my company is using Power BI, i just opted to use power query. In addition, statistics is by far the most important at this level and you don’t need to understand the minutiae of the subject (which is based in measure theory and is tough Python (not yet started but I have intermediate level knowledge about Python programming) I would like some feedback from fellow redditors working in Data Analysis field whether they use Python on a regular basis. The book covers: IPython and Jupyter NumPy Data Manipulation with Pandas Data Visualization with Matplotlib Machine Learning with Scikit-Learn 1st one is Free Code Camp They have a full stack developer courses -- First, Scientific Computing with Python -- Explains python from basics Second, Data Analysis with python -- Explains Data Analysis libraries in python Third, Machine Learning with Python -- Explains TenserFlow Framework in python I want to get an introduction into Python next. I like NeoVim, but definitely not for data analysis and it'll be experience full of pain. Rules: - Comments should remain civil and courteous. I know the basics of python, but it seems like that isn't nearly enough. Remember you can combine data from different sources, for example outcomes of soccer matches and a weather dataset to create unique insights. And it has really old python version. I own the book "Python For Data Analysis" is this a good start point to learn Data Analysis? Or should I already know much information about data analysis beforehand? Python for Data Analysis is definitely the best book to start if you want to use Python. It is harder to find true R developers compared to their Python equivalent, and management doesn't want to risk a problem cropping up in a language Nov 27, 2017 · Conclusion: More Data Analysis and More Real Data. There are many sources of public data, but I always recommend seeing if there is hyper-local data from where you live. --- If you have questions or are new to Python use r/LearnPython Your frontend that handles visualization will collect the data from the backend and then you can use whatever there. The way that I use Python and SQL right now, consists of using python for dynamic and static conditions for the SQL queries, as well as using python for anything related to data analysis (e. This will get you up to speed on Python, Pandas, and some data visualization libraries in Python. Python Data Science Handbook. You basically can't do data analysis in Python without a host of pandas functions at your disposal. you can do all of this in Python but it's more of a pain to code - SAS will immediately give you your ROC plot, your betas, etc with a proc logistics for example, but you've gotta do a good deal of Jake VanDerPlas has some great YouTube videos on data analysis with Python and his book is a fantastic resource. Analytics and modeling. SPSS and Minitab can't hold a candle to it. However, lately I’ve been bit by a motivation bug and I began taking python for data science and machine learning Boot Camp. Some Probability and statistics. 2M subscribers in the Python community. What is the best way for a beginner to start developing their actual practical skills in SQL and Python for Data Analysis? From watching some videos and doing research, it would seem the main Python libraries for data analysis are Matplotlib, pandas and numpy. I have been using python for 5 years, should I learn R as a backup? Or keep doing python is enough. --- If you have questions or are new to Python use r/LearnPython - 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. Today I'm reading Intro to Statistal Learning and Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. Date: December 15, 2017 Print ISBN-13: 978-0-13-454693-3 ISBN-13: 978-0-13-454704-6 Pages in Print Edition: 410 Data model (warehouse model) in SQL, data science in Python. 19 - Pie chart-like Visualizations, but cooler. I have to give this a bump. --- If you have questions or are new to Python use r/LearnPython However I find it quite boring to just read a book on python and try to remember as much as I can, and the way my brain works is it learns best when doing rather than reading. Naturally I am looking at Jose Portilla's Python Bootcamp for Data Analysis, however the comments says that it is outdated and is mostly Python 2 code. . What I needed was to import a data set and figure it how to compare and clean the data, returning key ids of records with particular issues, things like that. Python - I haven't had one of these jobs, but there seems to be more focus on incorporating analysis into data pipe lines (live dashboards/applications), rather than just pure research/analysis itself. Just terrible. I am currently working on my first data analysis project which needs to be done in Python. Once the object is created, it will gather data for real-time analysis, and write to a file. Hey friends, I'm currently enrolled in a Data Analytics course which requires me to have an intermediate level of python knowledge. Some people here use SAS, and someone came to me with a Stata question once. I am just wondering the process of how you use Python for analysis? Generally, my job is to gather data from MySQL, import that into Tableau, and build charts. "Python for Data Analysis, O'Reilly" seems like a possibility. From Basics, oop, Data Analysis, Machine learning, image/video processing , Deep learning, User Interfaces, RPG game development. Unpopular opinion but you don't actually need to be an 'expert' in Python if you are gonna work in Data Science. Data analysis is a huge field ranging from medicine, insurance, finance to logistics, marketing, machine learning. Provides details and code example on how to use the Python data science stack to work with data. Big Data by Viktor Mayer-Schönberger The Elements of Statistical Learning by Trevor Hastie An Introduction to Statistical Learning by Gareth James Deep Learning with Python by François Chollet Python for Data Analysis by Wes McKinney Introduction to Machine Learning with Python by Andreas C. There’s no getting away from the fact that mathematics is at the core of data analysis, but you don’t have to be John Conway to be useful. It's mostly for different Python libraries that can be used for time series analysis, but I thought it would still be useful. I think someone else said it best "the curse of having knowledge but not being able to easily explain it" I mainly use Python for data cleaning/formatting and automation, SQL for basic data pulls/counts, and SAS for modeling and inferential statistics. Though I could be wrong Take a look at this all in one course. - No 3rd party URL shorteners The reason being is that Python is not only capable of data manipulation, aggregation, and visualization, but it can also be used to build actual applications; something SQL can't do. 1. Oct 22, 2024 · Simply, IT and the developers know Python and if you are creating data apps that will be put in the company environment, they know Python and will want the app in Python so they can support it. But that's honestly a simplification - python has so many libraries and functions beyond the scope of data analysis. If you're working with large data sets, using Python is much faster and you have access to stat/machine learning libraries. Hi r/python! I recently completed a beginner Python course on Coursera that focused on data analysis. In the Resources tab of the Quick exploratory data analysis and manipulation using tidyverse packages. Python has easier ways to do some things and SQL has it's perks too. I have access to Python and R and I am equally skilled in both, but I always find myself falling back to the beautiful Tidyverse of dplyr, stringr, pipes and friends over pandas. The typical path for getting your system set up to use python for data science is to install the Anaconda distribution. Honestly most of the code I write in python is just pure functions that I split into different modules based on what the functions do but that's This is a place to discuss and post about data analysis. there are things Excel does well than Python or SQl even though I myself I use Excel SQL , Python R , Tableau, Power BI. I'm reading a good book on data science right now. Pandas, matplotlib are IMO awful to learn data analysis. Of course there are machine learning libraries and data analytics libraries for java as well, but the dynamic typing system of python and the ease of use of python makes it a good choice, also there are so many so good libraries. Sounds daunting, right? Well, the beauty of Python for data analysis lies in its ability to streamline this entire workflow. I am fluent in r. I wanted to know what resources you recommend next for learning about numpy, pandas, matplotlib and machine learning stuff? Take a look at this all in one course. Data Mining and Statistics Do you recommend "Python for Data Analysis, Wes Mckinney, O'Reilly" or "numerical Python, Robert Johansson, Apress" or "Pandas for everyone, Daniel Y. I’ve lived on both sides at the same time and it sucked. Not only that, but Python is an actual programming language and so the concepts learned in Python are transferable to other languages, like Java or C++. One example I showed that they liked was group sales data by product and region then find the largest customer and % of sales for each. More specifically on how to use Python with SQL for Data Analysis. For basic things R is easier to learn. If you can afford a copy of Microsoft Office, I would start at just using Excel. Really like aporoach from google that teaches the thought process of analytics. 3, though certainly not enough that it’s not a very useful resource. The Google DA in coursera is more about asking literal questions, not practical at all, feels like it has made for the interviews, I would recommend IBM Data Analysis, or John Hopkins DS specialization, btw Data Analytics at FCC has nothing to do with html or any previous course I also would recommend some good books, wes mckinney, Joel Grus, or Jake Vanderplas, all of em are good even better Generally Python's role in data analysis is on the data cleaning, data reformatting, data aggregation, and data visualization steps. 20 - Can an AI be a data scientist? 21 - Interactive Weather Analysis. If you watch exploit databases you will know why. Calculus. Im currently taking google’s in coursera and python for data science in data camp. View community ranking In the Top 5% of largest communities on Reddit. - No 3rd party URL shorteners Is Python a secure platform for performing data analysis on a company-wide ethernet dataserver? The words "free and open source" scare some people into thinking anybody at all will access our data if we use Python to analyze it. They're also very good and classical books to learn. IMHO, JMP is hands down the best point and click statistical analysis software. S. to make things easier and in one place. R and Python are doing great with ETL, it's easy, performant and fun. I think it's worth checking out! News & discussion on Data Engineering topics, including but not limited to: data pipelines, databases, data formats, storage, data modeling, data governance So i worked through the first half of my first python book (Python Crashcourse from Eric Matthes) and I am currently in a section there about introduction to data science. numpy is a level deeper which is just array based numerical data (pandas is built using numpy). 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. There are 4 courses total in that series but you can take each individual. Numeric Computation and Statistical Data Analysis on the Java Platform. Sometimes, a few analysis or automation are done in Excel, because I hate controlling Excel using Python and it is easier and quicker to make those adjustments on the excel file. I am a Python dev and data scientist at a F100 company and i have 2 pieces of advice First - Pluralsight has good Python courses both for data science-y stuff and general Python development. Data Science. Python serves as a fault tolerant SQL workflow execution tool for building data models AND a data science tool for ML and such. Similar to SAS. My background is in data science so I don’t really need to know how to do data science just how to code it in python/ generally used packages and functions to do the work. It covers everything related to python in depth. Julia is already eating into Python a lot in scientific modelling (particularly physics) but maybe less so into R and Python for data analysis/stats. I use VS Code for everything. Data Analysis. Google Colab is nice place to test something or run if you have potato PC at the moment. With Python you can work with tabular data AND NLP/CV/AI. Unlike tutorials, real life data isn't all readily available in SQL if you're getting your data from multiple sources like excel files or APIs, it's better to automate them in python so that you don't have to go through the process of cleaning data everytime. Think like a Data Scientist. I'm skilled in SQL and VBA and have had some experience with JAVA, HTML and PHP (So I'm at least familiar with the basics of programming). Everything is literally from scratch, with tons of hands on projects. I decided to then take his ML/DS bootcamp. I'm fairly certain the answer is "yes. , calculating the average of data points, multimodal distribution detection, trend analysis algorithms, and so on). Data analysis is a huge topic and requires extensive study to master. --- If you have questions or are new to Python use r/LearnPython I am a data scientist and have a pipeline that usually consists of SQL DB ->>> slide deck of insights. The %in% operator (or df. Linear Algebra. I was wondering if you guys had any ideas for data analysis python projects that are good for someone of my competency. Basic Python Syntax of course. Imagine tackling a massive data analysis project where a mountain of information needs to be cleaned, organized, analyzed, and transformed into actionable insights. Finally, another option is to attend meetups and conferences. Source: I manage a data engineering team. You want to learn basic concepts of working with data before you move to more advanced topics that require the use of different tools. Python for Data Analysis is definitely the best book to start if you want to use Python. Here is a list of free sources you can check out to learn data science specifically data analyst skills. If you need something data-intensive AND fast, like low-latency processing, then Java makes sense and there is a space for that in the data engineering field. The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. Python doesn't force a programming style like Java does with OOP. Synopsis. Second - please learn git I work with analysts who don't know it, or barely know it, and it's infuriating to collaborate on any level with them. Python is not the best language for backend services. Cracking the Coding Interview: 189 Programming Questions and Solutions. An example of the cleaning step would be like if you had a column with a bunch of dates in various formats (e. But I'm having trouble finding a decent course or bootcamp in Python that focuses primarily on using SQL for Data Analytics or Machine Learning. As a member of our community, you'll enjoy: 📚 Easy-to-understand explanations of business analysis concepts, without the jargon. To answer your question, if you want to be a data analyst, start with Excel. The data manipulation and visualization are better IMO. - 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" Business Intelligence is the process of utilizing organizational data, technology, analytics, and the knowledge of subject matter experts to create data-driven decisions via dashboards, reports, alerts, and ad-hoc analysis. In this example, we have Apple, the British Pound against the U. I currently am using Atom and pressing CMD+I every time I need to run my code all of my output is coming out on a small built in terminal at the bottom of the Atom Here's a quick review of PDSHB, a great book to learn Python for data related tasks. Looking for recommendations on good books to learn python in the context of data science. A Python Data Analysis. - No 3rd party URL shorteners Python for Data Analysis and the pandas docs Which of these you prefer is largely a matter of preferring one medium over another, but PfDA’s second edition is already slightly outdated for pandas 1. Even if you are getting stuck ask ChatGPT. , 2021-8-1, 8/1/21, Aug. Anything that you can do in MATLAB, you can do in python - with the caveat that you have to find the correct library to install/import + there is not as robust documentation like there is for MATLAB. i would reccommend to complete and quickly move to growing your portfolio with additional projects. Python Sorting and Searching Algorithms. And in my ongoing research project, we are using C++ for the main part because it is really a performance instance (NP-hard) and Python for the post-processing part. - All reddit-wide rules apply here. 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. Pandas for Everyone: Python Data Analysis, First Edition By: Daniel Y. The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics. Notation for math concepts. Your feedback and/or advice will help me greatly in correcting the course of my journey. R and Python are much easier and quicker to work with for analysis. 0 which was released after this course. - No 3rd party URL shorteners This is typically fine for and for dashboards that will be presented to non-technical users, which is typically the usecase. Rules: - Career-focused questions belong in r/DataAnalysisCareers - Comments should remain civil and courteous. Data model (warehouse model) in SQL, data science in Python. Kirill has beginner-friendly data science courses on Udemy. 18 - Supply Chain Control Tower. First half is your typical data analysis packages like pandas, seaborn and matplotlib. 22 - Bokeh Data Visualizations. If your task is to do data analysis, and only data analysis, then R is definitely going to be a good choice. He argues you need 3 languages: a glue language to stitch things together (python), a statistics language (R), and a high performance language for writing new tools (used to be C, but now Rust). Welcome to the Business Analysis Hub. Once you have that you need some numerical data analysis libraries, as others have mentioned, pandas and numpy are the goto here. 8 '21), you might manipulate the dates into a single standard format. Performing Data Analysis Using Python. Java has a large number of libraries for data science, and technically, Java programs compiled to bitecode are faster than native Python modules. 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. Second part is focused on data science using sci kit learn, nltk and keras. However, as soon as you need to start building a more complex program that does things besides just data analysis (for example, a framework to automate your data analysis tasks, hook it into a REST API, configure it with user access It features a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package. This is a place to discuss and post about data analysis. SQL and Python are both valuable tools for data analytics, but they serve different purposes and can be used in conjunction to perform comprehensive data analysis. The reason being is that Python is not only capable of data manipulation, aggregation, and visualization, but it can also be used to build actual applications; something SQL can't do. I'm looking for websites or platforms where I can practice and apply what I've learned. Good luck with that working with M. You can also use Java to do data science. Core Statistics. I'm totally new to Python, have a basic knowledge of SQL and R language though. Excel isn't a data analysis tool. If you need to do data analysis or ml on it then again we pull the data either directly from databases or through services. Python, on the other hand, tends to take over when it comes to building production models. We would like to show you a description here but the site won’t allow us. I am a Data Analyst for reference. R can be useful if you want to go deeper into statistical analysis (e. That course is severely outdated and uses a very old unstable (<1. The official Python community for Reddit! Stay up to date with the latest news, packages, and meta… Just stumbled upon this GitHub repo - Awesome Time Series in Python. 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. I’d echo previous comments saying that R is a better route than Python if your focus is data analysis and stats. However, although Python is a longer time commitment to learn it is much more versatile than SPSS with a kinder layout and syntax than R. I made it to about 200 pages in that book before giving up. Müller Here are some reasons people might prefer Python to Excel: - Python has better support for large data sets - Python is vastly superior when it comes to predictive analytics and advanced data modeling, hell, even basic data modelling; ever tried to do even simple linear regression in Excel? The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. you can do all of this in Python but it's more of a pain to code - SAS will immediately give you your ROC plot, your betas, etc with a proc logistics for example, but you've gotta do a good deal of This is a place to discuss and post about data analysis. It was my first book in the area and still one of the bests. After taking the course and doing some practice problems, I feel comfortable enough to know how to program in Python. 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. The extensions available allow for managing almost every workload necessary from Docker/Kubernetes management to cloud VM IDE and everything in between (I've need to do JS work on some projects and it's nice having everything in one IDE). (2) Don't use R and Python for data visualization as crossfiltering (a killer feature of PBI) is not working, despite of the great libraries of both. Any recommendations for beginner-friendly sites with coding challenges and exercises specifically tailored for data analysis in Python? Thanks! Both libraries are essential, but you'll use pandas all the time if you're doing data analysis. " Python and R, which one is better? I want to do cheminformatics. I would say they are technically lightweight. No one is talking about Ibis, but I've found it incredible! It has a well-thought out data frame API on the frontend, but then has a pluggable backend to many compute engines to actually do the computation, typically powerful SQL engines. Though I could be wrong The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. You don’t need Python for data analysis. This is not a generic 'business' subreddit and off topic posts will be marked as spam. I use it with duckdb on my laptop to handle data larger than memory that pandas can't handle 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. Some good options include "Python for Data Science Quick Start" by Packt Publishing and "R for Data Science" by Hadley Wickham and Garrett Grolemund. x. Also, Python is better for re-usability. Common examples include identifying sales trends or your customers’ behaviors. 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. Our friendly Reddit community is here to make the exciting field of business analysis accessible to everyone. Currently, I have a bottle neck where data takes 5-10min to load at each power query step. Thank you all very much in advance!!! We would like to show you a description here but the site won’t allow us. Nov 28, 2024 · Java is overkill for data analysis. 0. Know the line and enforce for successful product. - Do not spam. It's the most extensive library for building dataframes, reading in data from csv or Excel, etc. Python for Data Science and Machine Learning Bootcamp Did it myself and was pretty good. Chen, Addison-Wesley", or something else ? I already have the books "Python crash course, Eric Matthes" and "Learning SQL, Alan Beaulieu". g. y) of scikit-learn in the latter part of the course and therefore has a lot of depreciated code as scikit-learn went through a number of refinements in its API for version 1. In Python 3: Power BI, Tableau, Excel, Python, SPSS, SAS, R, Stata, orange, and many more, are all just tools used to do data analysis. Not saying that python stat packages are bad but R stat packages This is a very interested case. That reflects in job interviews too, answering technical questions in whatever tool you know. - No 3rd party URL shorteners SPSS is good for typical data sets and analyses if that is what you will need. The SAS language is absolute garbage, but SAS (the c SQL. I do a lot of microscopy as well and started learning R for my data analysis but started learning Python on the side. Chen Publisher: Addison-Wesley Professional Pub. I went through automate the boring stuff and think python and didn't like how data analysis book seemed verbose and didn't use real world examples. isin() for with pandas) with filtering. After googling for a couple days on how to start learning data analysis I couldn't find a good start point. An intermediate level of understanding plus some fluidity working with lists, dictionaries, exception handling and functional programming will be enough to keep you covered. While this is a valid way to get started, it installs lots of extra packages and software that are unnecessary. For data analysis and data science, I would just pick the one you are most comfortable with. In addition, you can still use the Python language to "talk" to Java libraries, so your knowledge of Python can be very useful. Because Python is more popular for ML and pushing models into production, people tend to focus on that and also use it for data cleaning, analysis, etc. Excel is just a completely different tool that people use for basic data analysis. So the bulk of analysis and automation is on Python, and Excel is the shareable file. SQL, a data viz tool, and sometimes Excel are good enough for the majority of roles, however Python is nice to learn because you can do more “data sciency” things like machine learning, regression analysis, and orchestrating data pipelines. It's a spreadsheet application. analyze(mydata) And you have finished whatever you were doing. , significance testing, effect sizes, survival analysis, panel data). I spent a lot of time working hard on online data analysis/science with Python courses in my spare time trying to learn what I need for my own job to move us from Excel to Python. 24 - Passive Income Analysis With Python This needs to be the top comment. 7 Best Python Courses for Data Analysis | Learn Python for Data Analysis. Any recommendations for free courses I can begin to learn Python for Data Analysis? One of my RL friends is a bioinformatician (a particular kind of data science) working in genetics, which is pretty big data. The choice between SQL and Python depends on the specific tasks you need to accomplish: SQL (Structured Query Language): This is a place to discuss and post about data analysis. Would like to ask if data cleaning with python would help with this? That said, it's a human constructed design philosophy for code, and not necessarily how you will be writing "data analysis" programs. - No 3rd party URL shorteners I tend to agree with you that Julia is the contender that could sweep away both R and Python (and matlab). Python is more versatile than R. Apr 22, 2024 · The Data Analysis Workflow in Python. I am used to using R and am a bit underwhelmed with the interface options for Python. This could be done with Excel but R was way faster. I normally would say focus on R but given how fast Python-based bio image analysis pipelines are growing, I’d recommend sticking to Python. Being able to pull data and tell a visual story with it to senior leaders is an enormously valuable skill for your career that many people just aren’t that good at. In python you do basically: import memelibrary as meme meme. My company lets analysts use any tool they want. If you want to be a programmer, start with Python. But that could well come in time as more packages are developed and/or become well known. Stick with Python if you think you will do some serious programming (like ML ops/engineering) and especially if you want to work in tech. Well, seems like you guys don't manipulate data there a lot, for example how do you clean most complicated raw data. Python for Data Analysis teaches only the rudimentary mechanics on how to use a few of the pandas commands and does very little actual data analysis. Last but not the least, you didn't mention what field you are in. Tableau is just another data visualization & analysis tool like Power BI Python is a programming language, it has many usage but in this context, you can probably replace power query and power BI with Python altogether so Power Query/BI is like low code tool for us accountant. Hey all! Python noobie here, but recently my job has wanted me to incoporate more Python into my projects. The reason I ask is because I found the pandas docs too dense and unforgiving, while at the same time I wish to avoid accidentally getting in to advanced topics since I have limited time. Then, expand your analysis or project based on interesting data you find. Dollar, the Consumer Price Index, and Unemployment rate. - No 3rd party URL shorteners Above we can easily spin up a variety of data collectors. pandas is for structured tabular data, kind of like data you get in excel. 💡 Practical tips and techniques to sharpen your analytical skills. But it's reeeeeeally slow. Definitely avoid Sublime, NeoVim and Atom. - Do not post personal information. Provides methods for transforming data from these APIs into normalized features that're readily useable for analysis, strategy development, and AI/ML Provides methods and CLIs for aggregating the raw or transformed data into a local SQLite database for custom tickers, custom economic data series, etc. It also has some tutorials, data sets, one free course, etc. You can use Python with PowerBI too for much more customised report. R - Data science, Statistics, Academia, more programmer skilled required. hfifn gpyq arfun dazntng nwg dwvv qenbhrr ybhqd oaonnt dsk