1. An Introduction to Data Science using Python
Ganesh Lohani
Sr Data Analyst
Lockheed Martin

2. Meeting Agenda/Learning Outcome
What is Data Science ?
Data Science Methodology
Python Basics
Variable and Data Types
Reading Data
Selecting Filtering the Data
Data manipulation,
sorting, grouping,
Python Libraries for Data Science
NumPy (Numerical Computation)
Pandas ( Data Analysis)
Matplotlib ( Data Visualization)
SciKit-Learn ( Machine learning Algorithms)
Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Demo on Machine Learning
Classification

3. What is Data Science
The process of finding insights/trends/ intelligence from the data
A relatively new field
Deeply rooted to Statistics and Decision Support System
A Multidisciplinary field ( Domain Knowledge, Tools & technology, Mathematics & Statistics, Problem Solving Skills)

4. Data Science Methodology
Statement of the problem/Objective of the Study
Data Preparation
Feature selection
Exploratory Data Analysis
Model development
Test the Model/Hypothesis Testing
Communicate the findings to the Business Leaders
Deployment ( Data as a product)
Feedback/Lesson Learned and Continuous improvement

5. Python Basics What is Python
A high-level general-purpose programming language.
A very popular Data Science tool for data analysis, data visualization and Machine Learning tasks
It is a open source and free tool

6. Python Basics How to Download Python
Download the python from the following link
You can also download Python, and Jupytor Notebook from the following link

7. Python Basics Common Tools in Python Environment
The Python interactive console:
Also called the Python interpreter or Python shell and provides programmers with a quick way to execute commands and try out or test code without creating a file. (
Spyder: It is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It offers a unique combination of the advanced editing, analysis, debugging, and profiling. (
Jupiter Notebook: It is an open source web application that you can use to create and share works (code, equations, visualizations, Machine Learning models and texts. (

8. Most Popular Python Libraries for Data Science
Python Basics
Most Popular Python Libraries for Data Science

9. Python Basics Variable and Types
Variable is a memory location and placeholder to hold the data
Most common Python Data Types: float, int, str, List, Tuple, Dictionary

10. Python Basics Basic Operations in Python Arithmetic Operations
Addition
Subtraction
Multiplication
Division,
Modulo
Relational Operations
Euqal
Greater/Greater than
Less/less Than
Logical Operations
TRUE/FALSE
AND IN OR

11. Python Basics List A common Data type in python
Collection comma-separated values (items) between square brackets
Contain same or different types
Mutable behavior Values can add, remove, update/replace the value, slice and dice the members

12. Python Basics Tuple A common Type in Python
A tuple is very similar to List A collection of items inside the parenthesis()
Tuple is Immutable ( The value cannot be changed)
Can slice and dice add elements and Delete the entire tuple

13. Python Basics Dictionary Another common and popular type in Python
A collection of unordered data values
A dictionary holds key value pairs of data The items are separated by commas, and the whole thing is enclosed in curly braces
Keys are immutable but the values are mutable - can add modify and Delete values

14. Python Basics Function A function is a collection of reusable codes
We write the function one time and call it to solve the particular task
Two Types of Function:
System Function: max(), min(), len()
User Defined Function – created by the programmer/developer
Main Components of Function: Input, computation, output Global and local function

15. Python Basics Looping - For Loop
The for loop that is used to iterate over elements of a sequence
It is often used when we have a piece of code which we want to repeat "n" number of time.

16. Python Basics Looping - While Loop
The while loop tells the computer to do something as long as the condition is met
It's construct consists of a block of code and a condition.

17. Python Library
NumPy
It uses multidimensional arrays and matrices, as well as functions to perform the computation
Allow to perform advanced mathematical and statistical operations on the above objects
It provides vectorization of mathematical operations on arrays and matrices
many other python libraries are built on the top of NumPy library

18. Python Library Pandas It is a Data Analysis tool in Python
It adds data structures and tools ( Series and Data Frame) designed to work with table-like data (similar to table in SQL Server environment)
It provides tools for data manipulation: selecting, reshaping, merging, sorting, slicing, aggregation etc.
It also handles missing data

19. Python Library Matplotlib
It is a two- dimensional Data Plotting and Data Visualization library in Python
We can create line plots, scatter plots, barcharts, histograms, pie charts etc.

20. Python Library Seaborn
Seaborn is a Python data visualization library based on matplotlib.
It provides a high-level interface for drawing attractive and informative statistical graphics

21. Python Library SciKit-Learn
It provides machine learning algorithms: classification, regression, clustering, model validation etc.
It is built on the top of NumPy, SciPy and matplotlib

22. What is Machine Learning
Create and use models that are learned from the data
Teach the computer that use data instead of explicitly writing the code.
The goal of ML is to use existing data to develop models that can use to predict various outcome to new data set
Example:
We see and use ML algorithms in our everyday life.
Predicting whether an message is spam or not
Predicting whether the credit card transaction is fraudulent or not
Predicting the share price
Predicting who will buy the product
Clustering customers into different group based on some criteria

23. Supervised Machine Learning
The Supervised Learning Uses Unlabeled Data
The primary goal of supervised learning is to scale the scope of data and to make predictions on labeled sample data.
For Example: Will the loan be approved ? ( Yes/No)

24. Unsupervised Machine Learning
It Uses labeled Data
The goal for unsupervised learning is to model the underlying structure or pattern in the data
For Example: Who are my loyal customers ? ( Customer Segmentation based on certain criteria)

25. Common Types of Machine Learning Algorithms
Supervised Learning
Classification
Regression
Decision Tree
Unsupervised Learning
Clustering
Association
Semi Supervised Learning
Mix of Supervised and Unsupervised Learning
Natural Language Processing
Text Mining

26. Machine Learning Model Regression Classification
Demo
Machine Learning Model
Regression
Classification

27. Question & Answer
Thank you for attending the session & Have a Great Day!
What Feedback do you have for me?
Questions:
(Ganesh Lohani)

28. Useful Links https://www.python.org/downloads/