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Python for Data Science Course Outline

 (2 days)
Version 3

Overview
This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy.

Audience
Students wanting use Python in data analytics applications.

Prerequisites
Students should have taken an introductory Python course or have six months of Python programming experience.

Number of Days
2 days

Course Outline

1. IPython: An Interactive Computing and Development Environment
Python Basics
Using the Command History
Interacting with the Operating System
Software Development Tools
IPython HTML Notebook
Advanced IPython Features

2. NumPy Basics: Arrays and Vectorized Computation
The NumPy ndarray: A Multidimensional Array Object
Universal Functions: Fast Element-wise Array Functions
File Input and Output with Arrays
Linear Algebra
Random Number Generation

3. Getting Started with pandas
Introduction to pandas Data Structures
Essential Functionality
Summarizing and Computing Descriptive Statistics
Handling Missing Data
Hierarchical Indexing
Other pandas Topics

4. Data Loading, Storage, and File Formats
Reading and Writing Data in Text Format
Binary Data Formats
Interacting with HTML and Web APIs
Interacting with Databases

5. Data Wrangling: Clean, Transform, Merge, Reshape
Combining and Merging Data Sets
Reshaping and Pivoting
Data Transformation
String Manipulation

6. Plotting and Visualization
A Brief matplotlib API Primer
Plotting Functions in pandas
Plotting Maps: Visualizing Haiti Earthquake Crisis Data
Python Visualization Tool Ecosystem

7. Data Aggregation and Group Operations
GroupBy Mechanics
Data Aggregation
Group-wise Operations and Transformations
Pivot Tables and Cross-Tabulation
Cross-Tabulations: Crosstab

8. Time Series
Date and Time Data Types and Tools
Time Series Basics
Date Ranges, Frequencies, and Shifting
Time Zone Handling
Periods and Period Arithmetic
Resampling and Frequency Conversion
Time Series Plotting
Moving Window Functions

9. Financial and Economic Data Applications
Data Munging Topics
Group Transforms and Analysis

10. Advanced NumPy
ndarray Object Internals
Advanced Array Manipulation
Broadcasting
Advanced ufunc Usage
Custom ufuncs
Structured and Record Arrays
More About Sorting
NumPy Matrix Class
Advanced Array Input and Output

View outline in Word

XPYS30

Attend hands-on, instructor-led Python for Data Science training classes at ONLC's more than 300 locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

For additional training options, check out our list of Python Courses and select the one that's right for you.

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Class Dates
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Fee:  $1050

Savings options:

 15 Day Pass
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