Python track design

[theSage21]

This track is intended as a basic starting point for learning python. We assume that the person is able to use a computer and a browser but has no idea about how to program.

Through this track they should be able to do the following at the minimum:

• How to install python / run on browser
• How to take input and print
• Simple operators
• Conditionals
• Loops
• Strings
• Lists
• A thing useful to them
  • File movements like bulk renaming photos?
  • Requests to access the internet?
  • Email validation?

While designing this track keep in mind the following things:

• Provide links to tutorials instead of explaining things.
• Each task can only have a small description. Avoid page scrolling.
• Link to official docs if possible, otherwise blog posts and videos are also ok.
• Ask them to practice using https://repl.it or https://www.codechef.com/ide instead of wasting time getting python to run on their machine.
• Each task can only have a max of 5 links to avoid overloading the participant.
• Each task must have a verifiable exercise which can prove that the participant has learned the topic.

References

• https://learnxinyminutes.com/docs/python/
• https://docs.python.org/3/
• https://repl.it
• https://www.codechef.com/ide

[aliana17]

I have picked some topics for the tasks for this track. Please suggest any changes need to be made.

• Task 1: Python Syntax
• Task 2: User input Program
• Task 3: Basic Operators
• Task 4: Control Flow
• Task 5: Loops
• Task 6: Functions
• Task 7: Strings
• Task 8: Lists
• Task 9: Using a Dictionary
• Task 10: Classes

[theSage21]

This looks good 😊.

Please do your work in a separate fork and open a pull request to master.

[theSage21]

@anchal7299 I was thinking maybe we can leave out loops, dictionaries and do them in the DSA track?

Same goes for classes. Perhaps we can do them in a OOP track? What do you think?

[aliana17]

I Agree with you

…

On 21-Apr-2020 11:35 am, "Arjoonn Sharma" ***@***.***> wrote: @anchal7299 <https://github.com/anchal7299> I was thinking maybe we can leave out loops, dictionaries and do them in the DSA track? Same goes for classes. Perhaps we can do them in a OOP track? What do you think? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#21 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AK7XDA6VJ4YRNKEEWHLHN3TRNUZTVANCNFSM4MMGADEA> .

[aliana17]

I agree with you

…

On 21-Apr-2020 11:35 am, "Arjoonn Sharma" ***@***.***> wrote: @anchal7299 <https://github.com/anchal7299> I was thinking maybe we can leave out loops, dictionaries and do them in the DSA track? Same goes for classes. Perhaps we can do them in a OOP track? What do you think? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#21 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AK7XDA6VJ4YRNKEEWHLHN3TRNUZTVANCNFSM4MMGADEA> .

[avni-srivastava]

Can't we add Numpy or Pandas ,Scipy, tensorflow any one or 2 from these on the place of loops or dictionaries as these topics will make the track look more catchy and also upgrade it.

[anubhav9199]

Task 12 Data Structures-1 {Stack, Queue}
Task 13 Data Structures-2 {Linked-List,Tree} + Recursion
Task 14 the different techniques to avoid Loops as loops give damage to the architecture of the Core
Task 15 Sorting Algorithms {Insertion, Quick, Bubble, Selection, Radix, Heap} + Searching
Task 16 String Matching Naive, KMP Algorithm + Matrix Chain Multiplication + Eucledion algorithm, Extended Eucledion algorithm
Task 17 Numpy Functions + numpy array functions
Task 18 Pandas DataFrame Functions

We can include these all so that a person gets hands on experience on algorithms use in the world. Also it gives them the thinking ability and courage to make an algorithm work without getting fear of errors

[theSage21]

Can't we add Numpy or Pandas ,Scipy, tensorflow any one or 2 from these on the place of loops or dictionaries as these topics will make the track look more catchy and also upgrade it.

Since this track is meant for people who are new to programming we can't add those to the Python track. Perhaps those would be ok if we were doing a Data science / ML track.

[theSage21]

Task 12 Data Structures-1 {Stack, Queue}
Task 13 Data Structures-2 {Linked-List,Tree} + Recursion
Task 14 the different techniques to avoid Loops as loops give damage to the architecture of the Core
Task 15 Sorting Algorithms {Insertion, Quick, Bubble, Selection, Radix, Heap} + Searching
Task 16 String Matching Naive, KMP Algorithm + Matrix Chain Multiplication + Eucledion algorithm, Extended Eucledion algorithm
Task 17 Numpy Functions + numpy array functions
Task 18 Pandas DataFrame Functions

We can include these all so that a person gets hands on experience on algorithms use in the world. Also it gives them the thinking ability and courage to make an algorithm work without getting fear of errors

Maybe you want to put some of these in the DSA track instead of the Python track? @anchal7299 can create a separate issue for that and set guidelines.

The pandas and numpy things I would put in a data science track instead of algos.

The string matching things could go in a competitive programming track maybe?