Data-Analysis-with-Python

Section 1: Getting Started Python

1. Getting Started With Python
2. What is Python
3. Why we need Python
4. Installation with Python
5. Pip Commends in Python
6. Introducation to Python
7. Basic Data Types
8. String
9. String Methods
10. Index and Slice and Extended Slice
11. Type Casting
12. Data Structures
13. List and List Methods
14. Tuples and Tuple Methods
15. Dictionaries and Methods
16. Sets and Sets Methods
17. Operaters in Python
18. Condational Stastements
19. Looping Stastement
20. Funcation
21. Modules and Packages
22. Build in Modules
23. Os,Sys,Array,Math,Json,Request,DataTime,RegEx
24. Extenal Modules
25. Numpy,Scipy,Pandas,matplotlib,Seaborn
26. Class and Object
27. Inheritance
28. Inheritance
29. Polymorphism
30. Encapsulation
31. Abstraction
32. Instances, Constructors & Self
33. Class Attributes
34. Methods
35. Static Methods
36. Python Advanced Topics
37. Python Iterator
38. Generator
39. Closure
40. Decorators
41. Property

**Section 2: ** Basic Statistics

42. What is Statistics
44. Type of Data
    • Qualitative Data Type
      • Nominal
      • Ordinal
    • Quantitative Data Type
      • Discrete
      • Continuous
45. How Statistics is Used in Data Analysis
46. Importence of Statistics
47. escriptive Statistics
48. Inferential statistics
49. Probability Distributions
50. Confidence Intervals

Section 6: Inferential Statistics 52. Hypothesis Testing 53. Chi-Squared Tests 54. ANOVA * Hypothesis Tests * Z Statistics * Confidence Level * Significance Level * T Statistics 55. Confidence Interval

Section 7: Predictive Modeling 56. Linear Regression 57. Logistic Regression 58. Multi-variate regression model 59. Robust nonlinear regression in scipy