Learning Python for Data Analysis and Visualization

There's another course related to python for data science from the same group with updated contents and exercises, buy that one instead.

The course contains a lot of the python packages I would like to learn or at least be aware of in my data science learning journey.

He has a more updated course called "Python for Data Science".

Good introduction into the main concepts of python for data science I completed all the lectures and I am confident that now I have a basic understanding for python and how to handle data.

It is a very good and comprehensive course that covers both the theoretical and practical sides of Python use for Data Science.

Good course to start if you are data science/data analytics enthusiast.

I could learn the basics of python from the ground, such as the development environment and the fundamentals of Stats or Data Science.

I highly recommend the course to anyone interested in Python, data science, data visualization and machine learning.

Not good to learn Python for Data Science because it gets boring after a while without an inspiring example to work along.

I put constant efforts to learn Python for data science and this course has now become one of my best references.

The course is good for getting into python for data Science and has a lot of good practical exercises.

It covers a lot of ground and gives insight into a number important aspects of working with python for data Science, including statistics, machine learning, visualisation and more.

Latter part of course covers machine learning algorithms.

overall the content was good but in machine learning the concepts were not that clear,the course is good for data analysis and visualization purpose.

Hope to see good conceptual knowledge in the upgraded course for machine learning.

The topics presented in this course are very large, from how to work with pandas and numpy to retrieve/cleanup data , to real examples of machine learning algorithms (Kaggle's Titanic, US 2012 elections, stock markets).

There are also a lot of appendix (statistics, broad review of machine learning algorithm, python 101).

The machine learning part is not what I expected, there are definitely better courses for ML, but to be fair this is not announced as such.

In a hands on manner this course covered basic through advanced features of the IPython interactive shell, NumPy, pandas, statistics, visualizations using matplotlib and seaborn, SQL, the practical application of supervised and unsupervised Machine Learning with sciKit-learn, as well as a few extras.

basic course to learn Machine learning and Python.

:) -------------------------------------------- # Data Project - Titanic (video 1) # FactorPlot # Let's first check gender #sns.factorplot('Sex',data=titanic_df) # deprecated syntax sns.factorplot(x='Sex',data=titanic_df,kind='count') # Data Project - Stock Market Analysis (video 1) # For reading stock data from yahoo #from pandas.io.data import DataReader # old pandas version from pandas_datareader import data,wb # Data Project - Stock Market Analysis (video 2) # Calculate 'rolling_mean' using Pandas pd.rolling_mean # AAPL[column_name] = pd.rolling_mean(AAPL['Adj Close'],ma) #deprecated, use: pd.Series.rolling().mean() # example: df['MA'] = df.rolling(window=5).mean() AAPL[column_name] = AAPL['Adj Close'].rolling(window=ma,min_periods=1).mean() # Data Project - Stock market Analysis (video 3) # to see actual numeric correlation between each of the plots using Seaborn #sns.corrplot(tech_returns.dropna(),annot=True) # corrplot deprecated, use heatmap sns.heatmap(tech_returns.dropna().corr(),annot=True) # Data Project - Election Analysis (video 1) #from StringIO import StringIO # Python 2 from io import StringIO # Machine Learning - Supervised Learning - Linear Regression (part 4) #X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(X,boston_df.Price) # deprecated X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X,boston_df.Price) # Machine Learning - Supervised Learning - Logistic Regression (part 1) #from sklearn.cross_validation import train_test_split # Deprecated from sklearn.model_selection import train_test_split # Machine Learning - Supervised Learning - Logistic Regression (part 4) # use zip to bring the col names and the np.transpose func to bring together the coefficients from the model coeff_df = DataFrame(zip(X.columns,np.transpose(log_model.coef_))) # TypeError: data argument can't be an iterator # Resolution: convert to a list for zip to work: coeff_df = DataFrame(list(zip(X.columns,np.transpose(log_model.coef_)))) # Web Scraping Appendix - Part 1 # Set as Beautiful Soup Object #soup = BeautifulSoup(c) #old, need to add parser soup = BeautifulSoup(c,'lxml') Great course, extremely comprehensive.

Very good introduction to data analysis and machine learning with python.It covers numpy,pandas and seaborn and provides a solid foundation for deeper dive into machine learning with python.

The machine learning concepts were a little difficult to grasp but that's only a small portion of the course.

Very comprehensive course - from introduction to Python to heavy machine learning.

Great content, excellent step by step examples.

Jose really spends time to explain in detail step by step in the lecture.

Very detailed, step by step, introduction to numpy, pandas and seaborn followed up by interesting and detailed projects.

Clear explanation and step by step demonstration Yes it is a good match.

Is the content comprehensive and informative?--Yes Does the production quality meet your expectations?--Yes Is the instructor's delivery interesting and engaging?--Yes A step by step approach will definitely save our time.

Learning step by step!!

Step by step guidelines with example and explanations.

Exercises are practical and not too hard if made you comlete them step by step.

step by step, easy to understand, a lot of example Very good, but somewhat slow!

The instructor guides you step by step and communicates clearly and effectively.

Any one coming here will have some basic programming experience, hence time would be important to finish one video assessment here... Clear instruction with step by step demonstration Good Course I think it has been well instructed but i could not find a Python 2 notebook on the jupyter Good flow and easy to understand there are too much options and instructions to start the course, i mean the set up and information resources, makes the whole thing complicated.

very interactive and informative, he knows how to teach Refers to things covered in the previous lecture that were actually not covered quite a bit Great teaching style, step by step instructions Make sure that you understand Python before taking this course, otherwise you will be lost.

So far so good!

So far so good.

so far so good, easy setup and easy to follow when i follow the steps of installing jupyter i see different screens even though same search words, which was very difficult for me to install the program since i'm a beginner Good!

抓住了重点不分讲授。看视频的同时照着敲代码，学起来很快 So far so good.

Nice explanation Good a bit too basic so far but fine if I speed it up so far so good, really happy, easy to understand, and I like how the instructor does the summary.

So far So good.

很棒的讲师，Python 数据科学从入门到放弃，从SQL到删库跑路。小伙伴儿们，走起~ So far so good!

Good Explanation as far..Thankyou So far so good; the instructor speaks clearly and to the point Rocking dude :) This course has a lot of details for installing and using the versions which is good, but I want to learn something more useful than this first two lectures It' very coll to see the concern of the teacher about the class, repeating concepts in different ways to make sure that no one is left behind.

Great Course.... good material So far so good.

Clearly instruction, so far so good.

clear explanation very good So far so good!

ready to start The video quality so far is not good enough to read what's on the screen Very Gut So far so good It is quite easy to understand.

This is one of the most complete classes on Python for data analysis.

The step by step approach and detailed explanations gave me a great overview of how I can utilize Python for data analysis.

I think this lecture should be rerecorded Excellent course, highly recommended for students who would like to learn Python for Data Analysis!

I bought every course of Jose Portilla and really recommended them to anyone looking to learn a good and practical foundation in python for data analysis.

This is a Fantastic course for those people who are interested in learning Python for Data Analysis.

This course on python for data analysis was really helpful for me and I learned a lot new things out of this.

#KUDOS It is a nice online course of Python for Data Analysis.

I would recommend this course for anyone wanting to either learn python for data analysis or data analysis in general.

Go for it if you wanna learn python for data analysis from scratch.

As a data analyst coming from R, I was looking for a resource that would quickly get me up and running with Python for data analysis.

This is just copy paste of book 'Python for data analysis' without giving any credit, coupled with painfully long videos.

This course covers the entire spectrum of what an analyst needs to know in order to setup, operate and excel at using Python for data analysis.

I am abut half way through the course and looking forward to learning more :) Well-paced, broad knowledge.

Looking forward to going through the specific examples and machine learning modules.

Great job, looking forward taken more courses from Jose using older version of python Not allowed to move back and see vedios I'm understanding every task, but i wish there were more explanation behind some of them as the course gets more complicated.

Looking forward to more practical examples.

I purchased the Python Zero to Hero bootcamp from the same instructor which was excellent so I was looking forward to this one as they've helped me at work.

I’m looking forward to more tutorials in this course.

I'm looking forward to all the projects at the end of the course.

I am enjoying the course a lot and I am looking forward to finishing it.

Looking forward to learning more.

Looking forward to the data analysis stuff.

Looking forward to learning more as the lectures go on.

I highly recommend this course really loved the course(My first course on udemy btw)......a tiny suggestion i have is for you to mention more sources and some more suggestions on how to progress well(since its foolish to assume that one singular course can help you with everything) All lectures are fantastic and well guided.

I would highly recommend this course for those who are interested in data analysis.

I highly recommend this course!

For those of you wanting/needing to learn Data Analysis in Python, I highly recommend this course.

Highly recommend this course for everyone.

He never explain why is important numpy or the vast cases of the pandas power, i think that is necessary for motivation at least at the beginning of each topic need to update modules and attributes that no longer part of libraries It was an amazing course.

outdated information It was properly explained, but it relies on the no longer suppported .ix feature Yes, however I would prefer some type of easy in-between assignments (of say, max.

However, be aware that the source for some data (Yahoo and Google) no longer work.

Content is a little dated, some of the modules have changed and the code no longer works.

student just needs to be super mindful that libraries don't keep API parity with updated versions, example is seaborn no longer has corrplot but pushing for use of heatmap.

Also, Anaconda is no longer on the continuum website.

Yahoo Finance no longer uses an API but one of the lectures is still teaching this as a source of data.

A pretty good course till now.

Good, Simple till now..i need more to go..:) Very Well Pull Together Course It is clear and easy to follow!

Clear but too slow for ppl who already know a little python Till now I like it, because it short and simple.

Clear, concise, but could be a little faster I have been using python through Spyder but I didnt know about juypter till now and I wished I did.

Till now its very basic..Want to know more power of Numpy.

on track till now it looking simple to learn.. Not sure yet, still introductory lectures!

fine till now very easy to follow yes..lets go deeper It is working very nicely Yes i like it very much Methodical - Introduction.

highly recommended to anyone who wants to build their career in data science.

Highly recommended!

Still highly recommended if you want to learn data analysis Good teacher.

Although I would prefer there be more real world examples of visualizing data.

He shows no real world examples of what needs to be done and basically teaches theory until about 60% of the way through the course.

very clear instructions , like the pace and all geared up to go till the end .. :-) This course lack real life experience problem, hw problems and ORGANIZATIONS!

What can be improved is adding a reference on how the capabilities can be used in real life applications, even if mentioned as reference shortly.

It could be improved by doing more with real life data science examples rather than spending so much time in overview of the syntax.

The example is too simple and I'd prefer there are real life data exercise Really nice to follow SO far, thanks Its nice i am confident enough what i have learnt very thorough explanation!

He creates a course on data science with python and doesn't explain any of the terms or how they're actually applicable to real life.