Course Description
Python is a general-purpose programming language that is becoming more and more popular for doing data
science. Companies worldwide are using Python to harvest insights from their data and get a competitive
edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our
Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data
science tools to start your own analyses
 
Data Science and Machine Learning
This course will enable you to gain the skills and knowledge that you need to successfully carry-out real-
world data science and machine learning projects.
 The first part of the course covers data analysis and visualization. You will be working on real datasets
using Python’s Numpy, Pandas, Matplotlib and Seaborn libraries.
 
The second part of the course focuses on machine learning. We will be covering both supervised and
unsupervised learning. We will be working on case studies from a wide range of verticals including finance,
heath-care, real estate, sales, and marketing. Some of the algorithms that will be discussed include Linear
Regression, Logistic Regression,. This course is the foundation for Deep Learning courses in this
specialization.

Course Content
Introduction
The Python Environment
 Starting Python
 Using the interpreter
 Running a Python script
 Python scripts on Unix/Windows
 Editors and IDEs
Getting Started
 Using variables
 Built-in functions
 Strings
 Numbers
 Converting among types
 Writing to the screen
 Command-line parameters
Flow Control
 About flow control
 White space
 Conditional expressions
 Relational and Boolean operators
 While loops
 Alternate loop exits
Lists and Tuples
 About sequences
 Lists and list methods
 Tuples
 Indexing and slicing
 Iterating through a sequence
 Sequence functions, keywords, and operators
 List comprehensions
 Nested sequences
Dictionaries Sets and Dictionary
 About dictionaries
 Creating dictionaries
 Iterating through a dictionary
 About sets
 Creating sets
 Working with sets
Working with Files
 File overview

 The with statement
 Opening a text file
 Reading a text file
 Writing to a text file
Functions
 About sequences
 Function parameters
 Global variables
 Global scope
 Returning values
 Sorting data
Using Modules
 The import statement
 Module search path
 Zipped libraries
 Creating Modules
 Function and Module aliases

Course Introduction
Overview of Data Analysis, Data Visualization, and Machine Learning
Environment Set-Up
Jupyter Notebook Installation
 Python for Data Analysis – NumPy
 Numpy Arrays
 Numpy Array Indexing
 Numpy Operations
Python for Data Analysis – Pandas
 Series
 Missing Data
 Group by
 Merging Joining and Concatenating
 Operations
 Data Input and Output

Python for Data Visualization – Matplotlib
Data Visualization with Matplotlib
Python for Data Visualization – Seaborn
 Distribution Plots
 Categorical Plots
 Matrix Plots
 Regression Plots
 Grid
 Style and Color

Introduction to Machine Learning
 What is machine learning?
 Supervised Learning
 Unsupervised Learning
 Machine Learning with Python
Linear Regression
 Model Representation
 Cost Function
 Gradient Descent
 Gradient Descent for Linear Regression
 Linear Regression with Python
 Linear Regression Project
Logistic Regression
 Classification
 Hypothesis Representation
 Decision Boundary
 Cost function and Gradient Descent
 Logistic Regression with Python
 Logistic Regression Project