Computing Course • Jillur Quddus

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.

Python for Data Analysis

Python for Data Analysis

Jillur Quddus • Founder & Chief Data Scientist • 1st Sep 2020

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Overview

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.

Course Modules

  • 1. The Power of Data
  • 2. NumPy Part 1 - Managing Numbers
  • 3. NumPy Part 2 - Advanced Functions
  • 4. NumPy Part 3 - Descriptive Statistics
  • 5. Pandas Part 1 - The Basics
  • 6. Pandas Part 2 - Loading Data
  • 7. Pandas Part 3 - Transforming Data
  • 8. Matplotlib Part 1 - Graphs and Plots
  • 9. Matplotlib Part 2 - Advanced Plots
  • 10. Real-World Projects

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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Jillur Quddus
Jillur Quddus
Founder & Chief Data Scientist