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

2. What is happening? Massive amount of data
90% of the data in the world today has been created in the last two years alone
Big Data (3 Vs):Volume Velocity Variety
Data is Everywhere and in many formats:
Structured Data
Semi structured Data
Unstructured Data
Data has been considered as assets
More opportunities to work on data platform
Turn data into Information, Decision Making and Business Value

3. What is Data Science
Data science is a field to extract insights/trends/ intelligence that supports the business leaders to make the better decision
Data Science is also a process of validating assumption model hypothesis related to business activities
Data science is a relatively new field and deeply rooted to Statistics and Decision Support System
It is a Multidisciplinary field ( Domain Knowledge, Tools & technology, Mathematics & Statistics, Programmimg languages)

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
Communicate the findings to the stakeholders
Deployment ( data as a product)
Feedback/Lesson Learned and Continuous improvement

5. Python For Data Science/Data Analysis
Python is a open source software used as Data Science tool
It is user friendly
The Code syntax is simple to read and follow.
It supports functional, object oriented, and structural programming languages

6. Python For Data Science/Data Analysis
Python Basics:
Variables and Data Types
Data Frame ( holds the data, like table in SQL Server)
Tuples ( initialized with small brackets, inmutables )
List ( collection of values, mutable),
Dictionary ( key value pair)
Operations ( comparison Mathematical and and Boolean)
Function Methods, Conditional Statement ( If Else, While Loop, For Loop)
Python Libraries
NumPy (Numerical Computation)
Pandas ( Data Analysis)
Matplotlib ( Data Visualization)
SciKit-Learn ( Machine learning Algorithms)

7. Machine Learning
It is a technique to teach the computer that use data instead of explicitly writing the code.
It is a branch of Artificial Intelligence (AI) and deeply rooted to Statistics and Mathematics
The output is never 100 accurate. Our goal is to optimize the algorithm/model
Example: Weather Forecast: 50 % Chance of rain today

8. Common Types of Machine Learning Algorithms
Supervised Learning
Classification ( Spam, No Spam)
Regression ( Forecast the Car price, Share price over time)
Decision Tree ( Will Rain Today? Yes, No)
Unsupervised Learning
Clustering ( Customer Segmentation: Gold, Silver, Bronge)
Reinforcement Learning
React to the environment ( Autonomous Car)
Natural Language Processing
Text Mining ( Twitter Data Analysis, Customer Survey Data )

9. Machine Learning Model Simple Regression
Demo
Machine Learning Model
Simple Regression

10. What Feedback do you have for me?
Question & Answer
What Feedback do you have for me?

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