My learning journey towards data science

  1. Mathematics - Statistics,Linear Algebra,Calculus
  2. Programming language- Python/R
  1. Data types- numerical data type(int,float) and string data type(string)
  2. keywords and appropriate variable naming guidelines
  3. Numerical data type operations- Read PEMDAS rule and modulus and floor division
  4. Arithmetic operators,Logical operators,Comparison operators
  5. Data type conversion
  6. String data type basic methods:Indexing and Slicing
  7. Basic data structures:Lists,Sets,Tuples,Dictionary
  8. List basics:Indexing,Slicing,List comprehension
  9. Tuple basics:Indexing ,Slicing
  10. Set basics: set operations
  11. Dictionary basics : getting key -value pairs and read about .get() for dictionary and its good to use .get() to access value for a key from dictionary.
  12. Practice above points and concepts from w3schools or
  13. Conditional Statements:if,elif and else statements
  14. Loops : for loop and while loop
  15. Stuff to know to use loops efficiently :usage of break,return and continue statements
  16. Practice pattern problems to improve knowledge of loops also learn about split and join methods.
  17. Exception handling :Sometimes you might run into error or there might be a exception in your data but you don’t want your code to crash there so learn about try,except and finally blocks
  18. Code reuse- Functional programming is important and it comes really handy in most of the cases
  19. lambdas- anonymous functions but lot useful especially when used with map,filter,reduce




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Kasinath Reddy

Kasinath Reddy

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