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How can I see what courses are being launched, or what’s coming next?

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We want to make sure your voices are heard as we continue to launch new courses, so be sure to take a look at our Content Roadmap here, and vote on your favorite content!

Happy Learning!


What’s the best way to request/suggest content for the future? Considering the statistics coursework that is already developed and that in development, I was wondering if ANOVA is under consideration for inclusion…

From the original post:

I believe the best way to suggest future content is to reply to this thread. Or reach out to the DQ success team via @Mary.

Since that is from this thread, hopefully it’ll be seen. Thanks! Hopefully @Mary gets a chance to check notifications

Hey @shizzman!

I’ve passed this along to our Content team, and can let you know if/when it gets selected to be expanded on as a course.

I’ll also double check that this is a good place for suggestions :slight_smile: Thanks!

Awesome and thanks! I just figure since ANOVA is a useful tool towards comparing sample groups it would be useful to demonstrate how it can be done via programming

In machine learning course there is no scikit-learn in your list of courses.I want to learn it.please try to include that in future

We want from the learner the level of content need to improvised more because the content in the courses are below average in python.I think focus on what i’ve to make user feel more enthusiastic what i’ve said can be applied to all other topics too.

Hey @srisrinu!

We currently use SciKit in our Machine Learning courses! You can view the first mission here

Topics like tuples,sets are missing in the basic data science courses which we use more

Hi Mary!

Thanks for sharing roadmap link. I looked at it and want to leave some feedback. It is great that there are more courses in pipeline for data analysis, machine learning and supporting tools or
concepts but I am little disappointed that there are not many data engineering or NoSQL courses. I am particularly interested to learn Hadoop, Spark tools such as Pig, Hive, Hbase etc via dataquest style teaching. Let me know if that is something DQ can prioritize. Thanks.



Can we expect more courses on deep learning and NLP in future?


I would really like to see the any math related modules as I think that will be crucial for anyone who wants to fully pursue the Data Science field. I can see the Probability, Statistic Fundamentals, Conditional Probability are on the way which is exciting.

It would also be very useful to see a module on the thought process of solving problems. For example when presented with a data set and a problem how does a Data Analysis / Scientist go about asking the correct questions to solve the problem.



I would really like to see some more NLP content.

I would also like to see reinforcement learning.

Yes, I also would like to see more in deep learning, NLP or CV content.

I wish dataquest could guide us how to or set a side project to start Kaggle basic competitions such as “Titian” or “digit recogizer”. Something like basic tutorials for beginners in Kaggle.

My goal is not to get on top of those competitions but a good way to learn and think as a data scientist.

Some ideas:

  • how to organize the code: best practices of structuring a jupyter or .py file with variables involved
  • interactive charts in Python (cufflinks, plotly)
  • more on API with Python
1 Like

Here are some best practices of Python that you can read online:

  1. Read the Zen of Python
    On the python interpreter do the following:

>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea – let’s do more of those!

  1. Read Python PEP 8 style guide

  2. Read Google Python style guide
    The guide provides a good example on what not to do and what to do.

  3. My notes on Transforming Code into Beautiful Idiomatic Python
    Common examples on transforming code into idiomatic Python.

As you observed that all points mentioned above are based on style, the style is very very important in writing readable code. It will have an impact on those who use the code base. And significantly reduce overhead time to understand someone’s code.

If you follow the above mentioned points, you should be well in accordance of writing Python code with the best practices in mind.


Thanks @alvinctk! I especially liked your #4 - super useful!
What would be nice, though, is also some guidance on how to structure the projects: i.e should variables sit in separate file or inside the .py file? Should common functions be isolated into stand-alone .py files and be treated as modules (libraries) - some examples of real-world project structuring.

What you are asking for is software engineering best practices - i.e. write good code.

A book recommendation is “Code Complete” by Steve McConnell.

1 Like

I would like to have more advanced excercises and projects for Spark

Also would be nice to get courses of cloud services. Especially AWS