Python for Data Science: Panda, NumPy and Matplotlib

Description

This a two-day course that 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. This course will review the essential Python libraries:

NumPy
Pandas
Matplotlib
IPython
SciPy

Students wanting use Python in data analytics applications.

Prerequisites

It is recommended that students that are interested in this course have:

Introductory Python course/s
Six months of Python programming experience.

What’s included?

  • Authorized Courseware
  • Intensive Hands on Skills Development with an Experienced Subject Matter Expert
  • Hands on practice on real Servers and extended lab support 1.800.482.3172
  • Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
  • Academy Code of Honor: Test Pass Guarantee
  • Optional: Package for Hotel Accommodations, Lunch and Transportation

With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.

Methods

  • Instructor Led (the best training format we offer)
  • Live Online Classroom – Online Instructor Led
  • Self-Paced Video

Speak to an Admissions Representative for complete details

Curriculum

Module 1: Building Blocks

Working with Python
Numpy Ndarrays
Slicing and indexing
Scalar Operations
Shape Shifting
Descriptive Statistics
Array Operations
Multiple Dimensions
Array Creation Options
Data Types
Getting Numpy-Specific Help
Overview of Data Visualization/Presentation tools

Module 2: Overview of Pandas

Working with Pandas in an IDE
Enhancements from Ndarray Objects
Series Objects
Pandas in 2-D
Pandas in 3-D

Module 3: Data Acquisition N

Dealing with missing Data and Outliers
Slicing, Dicing, and Re-Indexing
Data description/Analysis Tools

Module 4: Data Visualization

Module 5: DataTime-Like Objects

Basic time series Operations
Introspecting Time Series
Tools for Holidays, Business Day, Etc. .
Comparing and combining Data from different Series
Time shifting and time “Window” Operations

Module 6: Pandas Database OPS

Comparison of SQL Operations and Pandas Methods
Creating Pivot Tables and Cross-Tabulations
Aggregating data across different tables
Creating complex queries with intermediate Data frame Objects

Module 7: Pandas + Machine Learning tools

What is LDA?
Getting to know the data
Exploring data integrity
Applying the LDA Model
Do you want a doggie bag?
Quick recap of the analysis

Review A: Text-only debugging

The PDB debug library
Other text-based tools
Integrating logging with debugging

Review B: Introduction to Bayesian Analysis

A simple real-life example
The Bayesian approach
Reality checking

Enrolled