FA-0783Data & AnalyticsSoftware Development

Data Analysis with Python

NumPy, pandas, Matplotlib and remote data sources

Introduction

Why this course

Use NumPy, pandas and Matplotlib to acquire, organise, extract and present data for reports. Build on existing basic Python knowledge through array operations, data-frame workflows and time-series visualisation.

Learning outcomes

Create and index NumPy arrays and apply array operations.
Work with pandas Series/DataFrames, missing data, grouping and joins.
Import and export selected data and produce Matplotlib/pandas charts.
Retrieve prepared remote datasets using a supported DataReader provider or Nasdaq Data Link interface.

Prerequisites

Basic Python programming knowledge. A compatible Python environment with the course libraries and access to prepared data. Remote providers may require an API key, suitable account or dataset entitlement; exercises use data made available for the course.

Training outline

4 modules

01Module 1 — NumPy foundation4 topics

Introduction to NumPy
NumPy Arrays
Numpy Operations
Numpy Indexing

02Module 2 — pandas data workflows8 topics

Introduction to Pandas
Series
DataFrames
Missing Data
Group By with Pandas
Merging, Joining, and Concatenating DataFrames
Pandas Common Operations
Data Input and Output

03Module 3 — Reporting and visualisation4 topics

Introduction to Visualization in Python
Matplotlib Basics
Pandas Visualization Overview
Pandas Time Series Visualization

04Module 4 — Remote data acquisition4 topics

Introduction to data sources.
pandas-datareader as a separate package; select a currently maintained provider.
Nasdaq Data Link, formerly Quandl, using its supported Python client/API.
Review retrieved schemas, authentication and provider access requirements before using data in reports.

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