Hey guys! So I work as a Data Analyst with SQL, Tableau and Excel but I would like to take the next step which is programming with python for Data Analysis.

Can anyone recommend a course or bootcamp for this? I have 0 programming experience btw. I dont want to learn things out of my scope like creating apps or scraping the web.

I want to learn something useful for my job. For example I would like to be able to predict when a user will churn, how to predict the customer lifetime value, how to do machine learning in ways that will help the company and make me more valuable. I believe I need to learn pandas,numpy, seaborn, pyspark, tensorflow, matplotlib etc.

Thanks for the help!

I work full-time and I’m continuing my informal education in Python, specifically for data analysis. 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.

From various classes and resources, there’s an extensive amount to learn, and I think people believe you can’t start using Python until you have proven with a certificate that you can write from scratch. However, is it reasonable to focus primarily on understanding and implementing data analysis libraries (like pandas, numpy, and matplotlib) instead of perfecting my broader Python knowledge? I'm wondering if, by diving into these libraries and working on my professional tasks, I will pick up the nuances of Python indirectly.

Is this a viable approach? Can one become proficient in data analysis with Python by diving directly into these libraries, or is a deeper foundational understanding of the language essential before progressing?

Strange-ish scenario here but I just got hired as an Data Analyst for a company with no experience. The boss knows this and is fine with it, he is a close friend and wants to help me start a profession. He told me to throw some data sets in chat gpt to help me and slowly learn how to use python as I go. Anyone know some good textbooks or online programs I could check out to help expedite this process? I also have no background in python LOL.

I feel like a lot of my job in data analytics is simply looking at various metrics across different dimensions — e.g: which customers/regions/ads/demographics are driving KPIs and which aren’t — and why?

I am still in the early stages of learning Python, but I feel like a lot of the stuff I’m learning can be done in Excel/SQL, albeit slower and not automated.

I would like to know — what are some types of analysis that Python can do that other tools can’t?

How do you analysts use Python in their day-to-day job?

What Python skills are most important to master for data analysis?

What’s considered ‘advanced data analysis’ (not data science AI/ML) in Python?

Roadmap for Learning Python for Data Analyst

Hi I need to learn a python for data analytics. Can anyone share roadmap for it ? After learning it for data analytics i also want to learn it for automation tasks. If any Data Analyst is here also mention that. Thanks in advance.

I am currently on a self-learning journey of Data Analytics technical skills.

I am wondering if I should keep Python at the very end of my journey. I mapped my journey as follows:

• Data Analytics, Visualization and Forecasting using Microsoft Excel (completed)

• Google Data Analytics (completed)

• Microsoft Power BI (80% completed)

• SQL with MySQL (about half completed)

• Tableau (quarter completed)

• 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. Your feedback and/or advice will help me greatly in correcting the course of my journey.

I’ve been in the data analysis and reporting field at non-tech companies for over a decade, but don’t know how to code. I’m looking to change that by learning R or Python to conduct more efficient and complex data analysis and visualization. All the Data Scientists I speak with wholeheartedly recommend R. Why then am I seeing so many articles and posts recommending Python?

Hello everyone, I recently started a new position (got a promotion) at an environmental research company and part of my new job is to do data analysis.

I did similar work for my previous position in Excel but now I need to do more complex stuff in JupyterLab and Python/SQL. More exactly we have huge databases with thousands of companies which each have hundreds of data points and are assigned scores based on various factors. I would need to analyze this data and look for outliers, or trends in a certain industry, or if we change something to our methodology what impact it would have on the scores.

A colleague of mine recommended me datacamp.com and I did some of their free courses and they seemed ok but I don't really like the subscription model as I don't have that much time to spend each day. I've also seen Angela Yu's course mentioned a lot on this sub as a good starting point but it seems a bit overkill for what I need.

Worth mentioning that I have no previous experience in programming except for semi-advance Excel formulas if that count (from my initial interactions with python they do seem a bit similar).

Which one do you recommend going for, also worth mentioning is that I have an 800 euro educational stipend so while I would like to spend as little as I can from it so I can also do other stuff price is not really that much of an issue.

Thank you all for reading and have a great day!

Hi everyone,

I’m planning to learn Python and R with the goal of using them for data analysis and management.

I believe it’s important to first get a solid understanding of the foundations of both languages before diving into the specifics of data analysis.

I would really appreciate any advice or suggestions on:

1. The best way to approach learning the foundations of Python and R.

2. Any resources or courses that you recommend for beginners to build a strong foundation.

3. Resources or courses that are specifically good for data analysis and management after mastering the basics.

Thanks in advance for your help!

Hi everyone!

I’m completely new to Python and I'm particularly interested in using it for data analysis. I've read that Python is a powerful tool for this field, but I’m unsure where to start.

Could anyone recommend specific resources or beginner-friendly projects that focus on data analysis?
What libraries or frameworks should I prioritize learning first? I want to build a solid foundation, so any advice on how to structure my learning path would be greatly appreciated.

Thank you!

Ho everyone,

I am a data analyst with a non coding background trying to learn python. I understand the codes that already written. I solved random problems from chatgpt and other ai tools. Also doing projects on EDA. But the problem is i am not feeling that confident while writing codes, i am not able to build logics and eventually ended up loosing confidence. Any advice will really helpful for me to learn python. Thanks

Hello everyone

I want to learn the fundamentals of Python as a complete beginner, so I can use it and improve it in Data Analytics. Is there any Udemy course I should get? Would you recommend me anything else?

Thanks everyone in advance

Hey all,

I want to learn python to go into business analytics or data science, and I don't really know where to start with Python. Are there any online courses or videos you'd recommend, as well as what topics to start with and then go about.

As well as any general tips or anything to know about Python since I have very limited know6, thanks :)

Hi all, thank you for taking the time to answer this. My question is regarding where I should spend my time based on my goals:

I'm currently studying python from Angela Yu's 100days of code udemy course, but am not interested in making websites or apps as my goal is to use it for data analytics as a data/business analyst/data visualization. There is another udemy course by where python is used specifically for data analysis and visualization. Just wondering which I should focus on this instead. Both instructors are great so not looking for advice on their teaching style.

Angela Yu's Python: https://www.udemy.com/course/100-days-of-code/learn/lecture/17965120#reviews

python for data analytics: https://www.udemy.com/course/python-for-data-analysis-visualization/

Hello guys, im starting learning python with the hope of changing my career. I worked in other field which doesnt connect with data or IT. But i really like data.

Just finished a basic data analyst bootcamp of 365 team on udemy, have to say the course is amazing and makes a totally noob like me hooked.

Since i want to dwell deeper in this field, can you guy recommend courses or sources which i can improve my knowledge, ability also certificate for proper data analyst role?

I have skills in excel and i am going to learn SQL and power BI . When I try to apply for job them some companies want python and some companies don't. What should I do?

Hello everyone,

Can any data analysts or just analysts in general explain how you use python at work? I currently work as an analyst and see that a lot of the higher paying jobs in my field require python expertise. I do just fine in my role using excel for all my analyses.

I would love to hear some reasons why you guys prefer python, or are required to use python. Also, why python is better than excel for data analysis.

If you have any tips or anecdotes on how you got started with python for an analyst role that would be fantastic as well!

Introduction

Data Science is an increasingly important tool for companies looking for competitive advantage, and Data Scientist jobs are coveted and often well paid. As a result, the internet is awash with sites and Medium posts dedicated to teaching data science topics, many of which are of questionable value.

This post includes a list of resources which could help start you on the journey to being a data scientist, but focus on data analysis. This means there is little to no machine learning mentioned here, but there is a lot of focus on statistical analysis of data.

Credentials

I’m a data scientist with a maths PhD and was a quantitative analyst before that. I work in the energy industry and spend a lot of time working with generalized additive models for time series forecasting, chucking stuff at random forests, doing Bayesian inference with pymc3, and survival analysis with lifelines. I don’t use a lot of Tensorflow or PyTorch because they tend not to fit the domain of my problems well, but I revisit them every few months to pit them against our existing models.

Disclaimer

This post is purely my opinion, and in particular reflects my view that people too quickly jump to ML/DL methods when ‘traditional’ methods could do better. Obviously this is very domain-specific—you’d struggle to generate meaningful text with a linear regression.

Two final points before diving in:

• There is a lot of content between the sources below; don’t feel you have to read and understand them all by any stretch, but don’t expect to be on top of this stuff in a week or a month. Three months is probably the minimum amount of time required to get a feel for this, and more like a year to be useful to a third party

• Domain knowledge is super important; if you are interested in a particular industry, read up on that too to make yourself saleable

Learning Resources

Python Basics

Nothing here is specific to data analysis, so take a look at the r/learnpython FAQ.

In general, good data science often looks to the outside observer like software engineering. It’s not enough to build something in a Jupyter notebook and be done (many claim success in “productionising” notebooks, and all are wrong); so you also need to learn about:

• Version control (git is the de facto standard, and if you understand that you’ll be able to pick another VCS easily enough. Note that IDEs such as PyCharm give a friendly interface to many commands, but you still have to know the basics.)

• Packaging

• Unit testing (I like pytest)

Data Analysis

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. 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). Unfortunately I’ve never found a good introduction to statistics with Python (there are plenty for R!), so you have to dip into a number of different resources.

All of Statistics (PDF available here)

Perhaps not all, but Larry Wasserman has written a very approachable introduction to statistics here. The link includes the few data sources given in the book, but it’s very much a textbook. At 500 pages it’s a bit daunting, so I recommend focusing on chapters 1–11 first, then the chapters on linear regression and multivariate models, which is about 200 pages total. Read along with the SciPy docs; in addition take a look at pythonfordatascience.org which calls out useful functions in SciPy and statsmodels.

OpenIntro Statistics

An alternative (and possibly a better alternative) to AoS, this textbook is available with an optional contribution, and used by a number of colleges in the U.S. I’ve not read it, but a closer look, it appears to be pretty great. As with AoS you’ll have to read along with the SciPy and statsmodels docs.

Linear Algebra Done Right

Currently available for free from Springer, this covers a lot of ground in ~300 pages. Less immediately applicable than the stats books, but definitely worth keeping for the future

Python Data Science Handbook

Jake VanderPlas is the author of the excellent altair plotting library and a pretty bright chap. This book serves as a good introduction to NumPy, Pandas, Matplotlib and Scikit-Learn, and the link includes its full text as Jupyter Notebooks, which is awesome. You needn’t bother with the Scikit-Learn chapters unless you want to jump ahead.

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 1.0.3, though certainly not enough that it’s not a very useful resource.

Data Science from Scratch

Joel Grus’s book kinda does do what I assert isn’t possible—take you from zero to data scientist hero in a relatively short text. The criticism I would level at it is that it (necessarily) doesn’t go into sufficient depth everywhere, but what it does brilliantly is implement most things from scratch (duh!) to give you a good grounding in the basics.

Anatomy of Matplotlib

This is a great video to get a better understanding of how to work with Matplotlib, which is definitely the least Pythonic library still in use by data analysts today. It’s also slightly outdated, but hugely valuable.

Introduction to Survival Analysis — lifelines docs

Great introduction to survival analysis, which will either help you look like a superstar or be completely irrelevant.

Winning with simple, even linear models

I was at this talk at PyData London a few years ago and it was the best of the conference in my opinion. Vincent makes the argument that people are too quick to leap to ML/DL methods when simpler models could do as well or if not better.

The Visual Display of Quantitative Information

If you buy one book on visualisation, it should be this. (If you buy two, it should be this an The Grammar of Graphics)

Data Science

Briefly, here’re a few resources that cover data science proper, but don’t expect to get here any time soon!

• r/datascience (includes all the other resources in this section)

• The Elements of Statistical Learning and An Introduction to Statistical Learning (the former goes into more detail on the maths than the latter)

• Pattern Recognition and Machine Learning

• Andrew Ng’s Machine Learning course

Data Sources

As mentioned before, if you’re interested in a particular industry then see if you can get data related to it. Otherwise, these are some general sources of good-quality data.

• Scikit-Learn data has some really good ‘toy’ datasets that are useful for playing around with descriptive and inferential statistics, besides the skl estimators

• data.gov.uk and data.gov have hundreds of thousands of data sets. Many of these offer a great opportunity to practice cleaning up data with pandas because they come in all shapes and sizes

• OpenIntro Statistics data sets used in this textbook

Out-of-scope

The following topics haven’t been mentioned in this post yet, because I consider them adjuncts to the main theme, but will probably be of importance:

• SQL (probably very important!)

• Big data (possibly less so, but in general the problems of big data are about finding efficient ways of doing the same stuff with… big data) inc. e.g. PySpark etc.

• Continuous integration/continuous delivery

• Docker/Kubernetes

Postscript

The original version of this post appeared ~3 weeks ago and the number of links in it got it marked as spam and it was deleted by the mods; thanks to u/novel_yet_trivial for sorting it out!

What do you need learn from Python language to perform Data analysis ? (i.e. How to begin ? As you all know Python is vast)

I'm currently in a huge Rabbit hole because I don't understand where to begin.

Any tutorial recommendations could be really helpful to start somewhere.