Python Data Analytics Training | Data Science Consulting and Training by Bonzanini Consulting Ltd

Python for Data analytics

Get familiar with the Python stack for Data Science, and automate tasks such as aquisition, cleaning, processing and analysis of data.

Audience

Analysts, quants, BI professionals, data scientists, software developers and researchers who are new to Python.

Standard Duration

3 days

Tailor this course

We can work with you to customise a course for your team, so you can learn exactly what you need at the right pace.

Description of the course

The Python Data Analytics syllabus is designed to help analysts, researchers, BI experts, data scientists and developers becoming fluent in the use of the Python programming language, with the purpose of automating tasks such as acquisition, cleaning and analysis of digital data.

Using interactive examples and hands-on exercises, the course starts with a 1-day introduction to the core concepts of Python programming, which provides participants with the foundations to achieve a high degree of independence and flexibility for their analysis process. The course then continues with two days focused on how Python tools can help the audience performing data wrangling and data analysis effectively.

Learning objectives

By attending this course, you will learn about:

Running Python code with Jupyter and Anaconda
Core concepts of the Python language
Loading, filtering, sorting, transforming, aggregating, analysing and plotting data with pandas
Mathematical functions and array operations with NumPy
Visualising data with matplotlib and plotly

Syllabus

Environment Set-up

The Anaconda distribution as Python Data Science platform
Overview on Python virtual environment set-up
Running code in Jupyter notebook

Python core concepts

Built-in data types in Python
Control flow statements
Defining and using custom functions
Working with dates and times
Accessing data on file (CSV, JSON, ...)

Python Data Science libraries

pandas:

Working with table-like data in pandas
Loading data from file into DataFrame objects
Data transformation and indexing
Summary statistics over DataFrame objects
Data aggregation queries (groupby() method)
Exploratory analysis of new datasets
Data visualisation over DataFrames
Join/merge operations with DataFrames
Time series operations in pandas
Working with text data in DataFrames

NumPy:

Working with NumPy arrays
Essential operations with NumPy arrays
Stats and linear algebra with NumPy

matplotlib vs plotly for Data Visualisation