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Python for Data Analysis

Deriving Actionable Insights from Data

Python for Data Analysis
Paid Course

Python for Data Analysis

Learn how to load, explore, transform, analyse, visualise and derive actionable insights from structured, semi-structured and unstructured data using industry-standard Python libraries for data analysis.

Course Details

This hands-on course introduces essential and industry-standard Python libraries for data analysis, namely NumPy, Pandas and Matplotlib, in order to derive actionable insights from data. You will learn how to design and build end-to-end data pipelines capable of loading, exploring, transforming, merging, analysing and visualing real-world data sets applied to real-world business problems. This course also provides a foundation for more complex data engineering, such as building distributed and real-time data pipelines, and developing data science models using statistical learning, machine learning and deep learning techniques.

Requirements

Outcomes

  • The ability to load structured data (e.g. CSV files, Microsoft Excel spreadsheets and relational database tables), semi-structured data (e.g. XML and JSON files) and unstructured data (e.g. images, audio and videos) into efficient in-memory data structures.
  • The ability to design and build end-to-end data pipelines capable of loading, merging and transforming disparate datasets, and saving post-transformed and post-modelled data into databases.
  • The ability to analyse, visualise and derive actionable insights from disparate datasets in order to solve real-world business problems (e.g. descriptive statistics, trend analysis and forecasting).
  • An intermediate-level understanding of essential and industry-standard Python libraries for data analysis, namely NumPy, Pandas and Matplotlib.
  • Foundational analytical coding knowledge from which to develop advanced data engineering pipelines, including distributed and real-time data pipelines, and data science models using advanced statistical learning, machine learning and deep learning techniques.

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