Python for Data Analysts
£ 479.00
In this course, you’ll learn to code with Python. Python is a general-purpose programming language used for a variety of purposes, including web, software, and game development, artificial intelligence, machine learning, and data analysis.
This course is for those who are interested in learning the Python programming language specifically for applications in data analysis and data science.
The course includes a series of video lectures combined with a variety of conceptual and hands-on activities to help you develop the skills in Python to write commands, manipulate and analyse data, interpret results, and visualise data effectively.
The course begins with fundamental programming concepts, such as functions and statements. We then cover several of the Python libraries most relevant for data management and manipulation (i.e., NumPy, Pandas, Seaborn, and Matplotlib).
You’ll also learn about extracting, transforming, and loading data (ETL) techniques and operations, as well as what is known as “data wrangling”.
The course is divided into four modules and includes knowledge tests at the end of each section.
Module 1: Introduction to Programming with Python
The aim of the course is for you to develop a strong foundation in the Python programming language that you can implement in the field of data analysis.
Requirements
Strong critical-thinking and problem-solving skills, a strong background in mathematics (e.g., advanced algebra), and some experience with coding.
Career path
To become an effective data analyst, you’ll need several programming languages in your toolkit. Python is among the most sought-after skills among data analysts.
This course is for those who are interested in learning the Python programming language specifically for applications in data analysis and data science.
The course includes a series of video lectures combined with a variety of conceptual and hands-on activities to help you develop the skills in Python to write commands, manipulate and analyse data, interpret results, and visualise data effectively.
The course begins with fundamental programming concepts, such as functions and statements. We then cover several of the Python libraries most relevant for data management and manipulation (i.e., NumPy, Pandas, Seaborn, and Matplotlib).
You’ll also learn about extracting, transforming, and loading data (ETL) techniques and operations, as well as what is known as “data wrangling”.
The course is divided into four modules and includes knowledge tests at the end of each section.
Module 1: Introduction to Programming with Python
- Getting Started with Python;
- Performing Operations with Complex Data Types;
- Working with “If” Statements, Loops, and Comprehensions;
- Defining, Configuring, and Invoking Functions;
- Leveraging Functions with Lambdas, Generators, Closures, and Decorators.
- Introduction to NumPy for Multi-dimensional Data;
- Advanced Operations with NumPy Arrays;
- Manipulating and Analysing Data in Pandas;
- Dataframes;
- Data Wrangling with Pandas;
- Data Visualisation Using Seaborn
- Petl: Introduction;
- Basic Data Transformations;
- Advanced Extractions and Transformations
- Using Python to Compute and Visualise Statistics;
- Applied Inferential Statistics
The aim of the course is for you to develop a strong foundation in the Python programming language that you can implement in the field of data analysis.
Requirements
Strong critical-thinking and problem-solving skills, a strong background in mathematics (e.g., advanced algebra), and some experience with coding.
Career path
To become an effective data analyst, you’ll need several programming languages in your toolkit. Python is among the most sought-after skills among data analysts.
Category Data Analysis