Data Science With Python

Part A :  Python Basic  Concepts

1. Introduction to Python and its involvement with Data Science
2. Understanding Object Orientation Programming
3. Installation: Python 3.6 or later version, pip, iPython, Sublime Text Editor, Anaconda(Jupyter and Spyder)
4. Python Identifiers, Naming Conventions, Variables and Types
5. Defining Functions, Classes and Methods
6. Understanding Indentation
7. Executing sample programs in all Editors
8. Difference Between Functions and Methods
9. How to use Python Functions and Methods
10. Decision making through conditions and Loops
11. Declaring instances and Workout its accessibility
12. Understanding global and local variables in python
13. Instantiating Classes and flow of execution
14. Accessing Methods, Variables, Global variables and Functions
15. Working with self and super keywords
16. Object String representation through __str__ and __repr__
17. Constructors; Initialization; object: a base class
18. Inheritance Concept; Overriding and Overloading concept
19. Constructors with respect to inheritance
20. Understanding __name__ == ‘__main__’
21. Exceptions:
22. Overview of exception
23. Raising common causing exceptions
24. Exception Hierarchy
25. Raising exception at calling method
26. Handling exceptions through try, except, else and finally
27. Exception propagation
28. Customized Exceptions

Part B: Data Structures:

1. List: Creating, Accessing, Slicing, Manipulating lists, Built-in Functions & Methods in list, Iterating & Enumerating list data and Working with Nested lists.
2. Tuple, Set and Dictionaries (same above all operations)
3. Handling conversions of sample data with Data Structures

Part C: Regular Expressions in Python

1. Patterns, searching, Modifiers, flags
2. Working with examples to find specific strings, phone numbers, email addresses and filtering html data with regular expressions
3. File I/O
4. Working with text files and .csv
5. Reading and Writing data to the files
6. Importing required packages to work with .csv

1. Statistical thinking in Python and approach of Data Analysis
2. Fundamental statistics terms and its definitions
3. Applying basic statistics in Python with NumPy
4. Cumulative Distribution functions
5. Modelling Distributions
6. Graphical exploratory data analysis with Python
7. Probability theories:
8. Ranges, Mean, Variance, Standard Deviation and various distributions
9. Mass and Density functions
10. Kernel density estimation
11. Understanding Bayes theorem and predictions*
12. Estimation
13. Sampling distributions, bias and Exponential distributions
14. Hypothesis testing
15. Hypothesis Test
16. Testing Correlation and Proportions
17. Chi-Squared Tests
18. Errors, Power and Replication
19. NumPy: N-dimensional array operations
20. Array creations, conversions, dimensional understandings, shaping, reshaping, generating sample large datasets, Linear algebra functionalities and numerical operations etc…
21. SciPy: High-level Scientific Computing

1. Linear Algebra operations
2. Interpolation
3. Optimization and fit
4. Statistics and random numbers
5. Numerical Integration
6. Fast Fourier transforms
7. Signal processing and image manipulation

Part A :Pandas and NumPy Functionalities:

1. Introduction
2. Pandas DataFrame basics
3. Understanding data, looking at columns, rows and cells
4. Subsetting Columns, Rows with methods
5. Grouped and Aggregated Calculations

1. Frequency Means and Counts

1. Basic plot
2. Pandas Data Structures
   1. Creating your own data (Series and DataFrame)
3. Series (also called as Vector) Object operations
   1. Broadcasting and Scalar operations
4. DataFrame Broadcasting (Vectorized)
5. Making changes to Series and DataFrame

1. Adding additional Columns
   ii.   Dropping values

1. Exporting and Importing Data

Part B :  Introduction to Plotting:

1. Introduction
2. Matplotlib
3. Statistical Graphics using matplotlib
4. Univariate
5. Bivariate
6. Multivariate Data
7. Seaborn Library Plotting methodology
8. Univariate, Bivariate and Multivariate
9. Pandas Objects Plotting
10. Histogram, Density Plot, Scatterplot, Hexbin Plot and Boxplot
11. Seaborn Themes and Styles

Part C : Data Manipulation:

1. Data Assembly
2. Concatenations and Merging Multiple datasets
3. Missing Data:
4. Introduction
5. What is a NaN Value
6. Working with merged data, user input values and Re-indexing
7. Working with missing data
8. Finding and Counting missing data
9. Cleansing missing data
10. Calculations with missing data
11. Conclusion Understanding Multiple Observations (Normalization)

Part D : Data Munging:

1. Understanding Data Types
2. Converting types
3. Categorical Data
4. Convert to Category
5. Manipulating Categorical Data
6. Strings and Text Data
7. String Subsettings
8. String Methods

1. String Formatting
2. Apply and Groupby Operations:
   1. Introduction
   2. Functions
   3. Apply over a Series and DataFrame
   4. Apply- Column-wise and Row-wise operations
3. Groupby Operation:
   1. Aggregate Methods and Functions
4. The datetime Data Type:
   1. Python’s datetime Object
   2. Loading, Converting, Extracting Date components
   3. Date Calculations
   4. Datetime Methods
   5. Subsetting datetime, Date Ranges, Shifting Values, TimeZones

1. Linear Models
2. Linear and Multiple Regressions using statsmodels and sklearn
3. Generalized Linear Models
4. Logistic and Poisson Regressions using statsmodels and sklearn
5. Survival Analysis

1. Model diagnostics
   1. Residuals
   2. Comparing Multiple Models
   3. k-Fold Cross-Validation
2. Regularization
3. Clustering
   1. k-Means, Dimension Reduction with PCA (Principal Component Analysis)
   2. Hierarchical Clusterings
   3. Conclusions

Note: Keeping main objective as “Understanding” All the above topics are covered with logical and programmatic approach in Python. Also please note that Content order is NOT compulsorily followed at the time of delivering subject and knowledge.