Email Us   Phone : 503-259-0312   
  Home    |    Instructor-led Training    |    Self-Paced Learning    |    Online Training     


Contact Us   -   Why Choose Wintrac   -   Clients    

Courses
ADA
Adobe
Agile
AJAX
Android
Apache
AutoCAD
Big Data
Business Analysis
Business Intelligence
Business Objects
Business Skills
C++ programming
Cisco
Citrix
Cloud Computing
COBOL
Cognos
ColdFusion
COM/COM+
CompTIA
CORBA
CRM
Crystal Reports
Datawarehousing
DB2
Desktop Application Software
DNS
Embedded Systems
Google Web Toolkit (GWT)
IPhone
ITIL
Java
JBoss
LDAP
Leadership Development
Lotus
Machine learning/AI
Macintosh
Mainframe programming
Microsoft technologies
Mobile
MultiMedia and design
.NET
NetApp
Networking
New Manager Development
Object oriented analysis and design
OpenVMS
Oracle
Oracle VM
Perl
PHP
PowerBuilder
Professional Soft Skills Workshops
Project Management
Rational
Ruby
Sales Performance
SAP
SAS
Security
SharePoint
SOA
Software quality and tools
SQL Server
Sybase
Symantec
Telecommunications
Teradata
Tivoli
Tomcat
Unix/Linux/Solaris/AIX/
HP-UX
Visual Basic
Visual Foxpro
VMware
Web Development
WebLogic
WebSphere
Websphere MQ (MQSeries)
Windows programming
XML
XML Web Services
Other
Beginning Data Analysis with Python and Jupyter
Description

Data science is becoming increasingly popular with more and more industries beginning to value its importance, while recent advancements in open-source software have made the discipline accessible to a wide range of people. Python is a popular choice for most data scientists, owing to its ease of use and versatile nature.

In this course, we show how Jupyter Notebooks can be used with Python for various data-science applications. Aside from being an ideal "virtual playground" for data exploration, Jupyter Notebooks are equally suitable for creating reproducible data processing pipelines, visualizations, and prediction models.

We will look at various data modelling concepts using Jupyter Notebooks, and we will see the full power of Jupyter Notebooks as we work through this course.

Overview

This fast-paced practical single-day course focuses on solving challenges presented by data science in a manner that is simple to conceptualize and easy to implement.

This is achieved by leveraging Python libraries that offer abstractions to complicated underlying algorithms. The result is that data science becomes very approachable for beginners, a fact which is reflected by the strength and growing popularity of the Python ecosystem. In this course, we will cover every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data.

The Jupyter Notebook acts as an add-on tool - a virtual playground - that allows you to create and share live code, equations, visualizations, and text.

By touching on a variety of topics within the discipline, you'll be exposed to many interesting examples with real-world data.

Scope

This course focuses on creating reproducible data analyses using Python and Jupyter, and is intended for an audience with a background in Python. As such, we do not cover the basics of Python in this course. However, we will take a brief tour of the Jupyter interface.

Target Audience:

If you're a Python programmer stepping out into the hugely popular world of data science, opting for this course is the right way to get started.

For the best experience in this course, you should have knowledge of programming fundamentals and some experience with Python. In particular, having some familiarity with the Python libraries Pandas, Matplotlib, and scikit-learn will be useful.


Course-specific Technical Requirements

Hardware

This course will require a computer system for the instructor and one for each student. The minimum hardware requirements are as follows:
  • Processor: i5
  • Memory: 8 GB RAM
  • Hard disk: 10 GB
  • An internet connection
Software

For this course, we will use the following software:
  • Anaconda 4.3+ and Python 3.5+
  • Python libraries included with Anaconda installation:
    • matplotlib 2.1.0+
    • ipython 6.1.0+
    • requests 2.18.4+
    • beautifulsoup4 4.6.0+
    • numpy 1.13.1+
    • pandas 0.20.3+
    • scikit-learn 0.19.0+
    • seaborn 0.8.0+
    • bokeh 0.12.10+
  • Python libraries that require manual installation:
    • mlxtend
    • version_information
    • ipython-sql
    • pdir2
    • graphviz
    • Download and install all the required Python libraries
Course duration

1 Day

Course outline

Lesson 1: Jupyter Fundamentals
  • Basic Functionality and Features
  • Our First Analysis - The Boston Housing Dataset
Lesson 2: Data Cleaning and Advanced Machine Learning
  • Preparing to Train a Predictive Model
  • Training Classification Models
Lesson 3: Web Scraping and Interactive Visualizations
  • Scraping Web Page Data
Interactive Visualizations

 
About us
Contact us
Careers at Wintrac
Our Clients
Why Wintrac


Register for a free training CD-ROM drawing
Refer a client or instructor and earn $$$


Wintrac Inc.
16523 SW McGwire Ct.
Beaverton OR 97007
 
Wintrac, Inc. All rights reserved.                                                                               Site Map   |   Terms of Use   |   Privacy Policy