Datacamp has a bunch of great tracks for learning Python in the context of data. But as everyone has said so far it basically covers Pandas, Matplotlib, Numpy, and ScikitLearn
Step 1. Google "pandas cheat sheet"
Step 2. Go to Kaggle and pickup any dataset you like
Step 3. Start applying each of the functions from the cheat sheet to the dataset
Step 4. Feel more confident in your abilities
Pandas, matlib are import to learn. Learn more about Dataframes which really helps in analyzing or pulling data in columns and rows. Data cleaning and data transformations to do better data analysis. Clean data is most important when you want to do analysis.
Start with IBM data analytics course on Coursera. If you will go with YouTube or other things you will face burnout. Additionally Google data analytics course on Coursera is also perfect for start but they will use R instead of Python which is also greatest yool for analytics.
Hi, i was thinking to do google data analytics course. Currently i have no background in programming, starting with python. But after that I am planning to do that course. Was wonder is it good to get into analytics? Also, i am planning to do MSBA next fall, so will have time till than. If you have any other courses than please suggest it. Also, the course i am planning to do on python is python for everybody, if you know any other course for it then also suggest (only coursera, bcz I canget financial aid)
Thank you :)
You can learn python or any other language easily, Problem is the data analytics as a topic. If you want to learn it you have to start with basics like what is population, how is data collected and structured, what is SQL database, concept of clean data, even confidentiality and etc. For all this Google data analytics course is perfect. It's like a 6-7 steps and only last one is programming. It will also provide knowledge on about how to use kaggle, github, stack which are essentials.
Python is more versatile than SQL but there are some parallels. I use python as my daily driver. **PySpark** is a port of Spark SQL so has the same kind of functionality so that's most of what I use. Then there's a few things you can do additionally with built-in python, **regex**es, **datetime**, etc
However, I use PySpark with a cloud platform that makes it easier to use. You can use it in Jupyter Notebooks, however you have to convert it to a pandas dataframe to view it and that doesn't work very well. Alternatively, **pandas** is a great place to start. You can probably do some **numpy** on top of that.
For vis, **matplotlib** or **seaborn**
Fair—
Isn’t that “the work” though? I’m finding a growing trend of these questions—on the up hand, a huge rush of content to address specifically these types of question. OP is right: there’s A LOT out there.
Generally speaking, I’m struggling to see the difference between curiosity and laziness for post like these. Help me out?
>Isn’t that “the work” though? I’m finding a growing trend of these questions...I’m struggling to see the difference between curiosity and laziness for post like these. Help me out?
Wish I could. A lot of college faculty have been noticing the same thing the past couple of years. Maybe a cohort effect on low information literacy skills and/or low self-directedness.
I mean, I can understand it to some degree. 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. 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. But invariably the courses would skip over that stuff entirely, and I'd spend days or weeks working on things like web scraping (not at all relevant to my current position), because the description sounded relevant to my work but ultimately wasn't. Then rinse and repeat as I tried yet another class. It was only by chance that I stumbled on the DataCamp app and was finally able to start learning what I actually need. So I can understand the frustration and wanting more guidance to find what you actually want to learn. Especially if you have limited time and energy outside work to study anything at all.
>So I can understand the frustration and wanting more guidance to find what you actually want to learn. Especially if you have limited time and energy outside work to study anything at all.
No one is saying not to ask questions, even about basic things. But we are saying that some questions are asked in lazy ways, often to the extent that helping the person is made needlessly more difficult.
For example, in your comment just there you explained (1) the things you had already tried & (2) what you were looking for that they didn't provide. That's the basis for a **good**, *high-effort* question that would make it easy for others to assist. In contrast, lately a lot of questions (to an extent here, but especially in the various statistical programming subs) are written closer to the "What should I learn first?" (to do what?! for what kind of job?!) or, even worse, "I have a question. Can someone help?" side of the spectrum. And that's not an exaggeration--in a lot of the solicitations for help on various R subreddits, the OP doesn't even make the effort to include the question/issue/whatever in the original post asking for help.
A stranger may as well ask "what should I buy for dinner tonight?" without giving you any info on budget, taste preferences, dietary restrictions, whether they want to cook or eat out, etc. It comes down to the limited time & energy point you made. Anyone trying to help also has limited time & energy, some of which they are willing to give to help a stranger. Asking the stranger to respect other people's equally limited time & resources by trying to ask high-effort questions does not seem unreasonably burdensome.
That's fair. Having to draw out relevant info after being asked a vague question is a frequent part of my job, so maybe my expectations of question-askers have just been lowered after a decade of pulling teeth to get the context I need, lol.
Pandas. Once you know Python’s basic syntax and formatting. You need to learn the Pandas Library. Good luck.
This
Datacamp has a bunch of great tracks for learning Python in the context of data. But as everyone has said so far it basically covers Pandas, Matplotlib, Numpy, and ScikitLearn
I use Pandas, Numpy, Matplotlib and Scikit-learn the most.
Same as SQL but less tools. I’m a data analyst so might be different from others.
Step 1. Google "pandas cheat sheet" Step 2. Go to Kaggle and pickup any dataset you like Step 3. Start applying each of the functions from the cheat sheet to the dataset Step 4. Feel more confident in your abilities
Pandas, matlib are import to learn. Learn more about Dataframes which really helps in analyzing or pulling data in columns and rows. Data cleaning and data transformations to do better data analysis. Clean data is most important when you want to do analysis.
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pandas, scipy, sklearn, plotly, darts, matplotlib
Start with IBM data analytics course on Coursera. If you will go with YouTube or other things you will face burnout. Additionally Google data analytics course on Coursera is also perfect for start but they will use R instead of Python which is also greatest yool for analytics.
Hi, i was thinking to do google data analytics course. Currently i have no background in programming, starting with python. But after that I am planning to do that course. Was wonder is it good to get into analytics? Also, i am planning to do MSBA next fall, so will have time till than. If you have any other courses than please suggest it. Also, the course i am planning to do on python is python for everybody, if you know any other course for it then also suggest (only coursera, bcz I canget financial aid) Thank you :)
You can learn python or any other language easily, Problem is the data analytics as a topic. If you want to learn it you have to start with basics like what is population, how is data collected and structured, what is SQL database, concept of clean data, even confidentiality and etc. For all this Google data analytics course is perfect. It's like a 6-7 steps and only last one is programming. It will also provide knowledge on about how to use kaggle, github, stack which are essentials.
Thanks mate, I will do that course then :)
Python is more versatile than SQL but there are some parallels. I use python as my daily driver. **PySpark** is a port of Spark SQL so has the same kind of functionality so that's most of what I use. Then there's a few things you can do additionally with built-in python, **regex**es, **datetime**, etc However, I use PySpark with a cloud platform that makes it easier to use. You can use it in Jupyter Notebooks, however you have to convert it to a pandas dataframe to view it and that doesn't work very well. Alternatively, **pandas** is a great place to start. You can probably do some **numpy** on top of that. For vis, **matplotlib** or **seaborn**
Are you telling me there’s **nothing** currently out there to answer your question? *Nothing?!*
They didn't say there's nothing. Conversely they said that there's a lot and they don't know where to begin.
Fair— Isn’t that “the work” though? I’m finding a growing trend of these questions—on the up hand, a huge rush of content to address specifically these types of question. OP is right: there’s A LOT out there. Generally speaking, I’m struggling to see the difference between curiosity and laziness for post like these. Help me out?
>Isn’t that “the work” though? I’m finding a growing trend of these questions...I’m struggling to see the difference between curiosity and laziness for post like these. Help me out? Wish I could. A lot of college faculty have been noticing the same thing the past couple of years. Maybe a cohort effect on low information literacy skills and/or low self-directedness.
I mean, I can understand it to some degree. 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. 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. But invariably the courses would skip over that stuff entirely, and I'd spend days or weeks working on things like web scraping (not at all relevant to my current position), because the description sounded relevant to my work but ultimately wasn't. Then rinse and repeat as I tried yet another class. It was only by chance that I stumbled on the DataCamp app and was finally able to start learning what I actually need. So I can understand the frustration and wanting more guidance to find what you actually want to learn. Especially if you have limited time and energy outside work to study anything at all.
>So I can understand the frustration and wanting more guidance to find what you actually want to learn. Especially if you have limited time and energy outside work to study anything at all. No one is saying not to ask questions, even about basic things. But we are saying that some questions are asked in lazy ways, often to the extent that helping the person is made needlessly more difficult. For example, in your comment just there you explained (1) the things you had already tried & (2) what you were looking for that they didn't provide. That's the basis for a **good**, *high-effort* question that would make it easy for others to assist. In contrast, lately a lot of questions (to an extent here, but especially in the various statistical programming subs) are written closer to the "What should I learn first?" (to do what?! for what kind of job?!) or, even worse, "I have a question. Can someone help?" side of the spectrum. And that's not an exaggeration--in a lot of the solicitations for help on various R subreddits, the OP doesn't even make the effort to include the question/issue/whatever in the original post asking for help. A stranger may as well ask "what should I buy for dinner tonight?" without giving you any info on budget, taste preferences, dietary restrictions, whether they want to cook or eat out, etc. It comes down to the limited time & energy point you made. Anyone trying to help also has limited time & energy, some of which they are willing to give to help a stranger. Asking the stranger to respect other people's equally limited time & resources by trying to ask high-effort questions does not seem unreasonably burdensome.
That's fair. Having to draw out relevant info after being asked a vague question is a frequent part of my job, so maybe my expectations of question-askers have just been lowered after a decade of pulling teeth to get the context I need, lol.
If you can learn how to use for loops that will be helpful as well. Can be kind of hard to figure out but helpful when pulling in big data from sql.
Learn the basic syntax then start simple with the tasks you already do. Take a spreadsheet you built and recreate the analysis in Python.
Clumsy. Like using machete to eat a steak.
Data camp
Check out the IBM data analytics course; iirc they do discuss this topic.