In this course, you will:

• Learn Python basics, including syntax and data types, and how to use Python as a calculator.
• Manipulate and work with lists, which are essential for handling data in Python.
• Write reusable code with functions and manage program flow using if-else statements and loops.
• Start using NumPy, a fundamental library for data science, to handle arrays and perform efficient data operations.

In this course, you will:

• Master Python dictionaries and pandas DataFrames for data manipulation.
• Practice creating and accessing datasets using dictionaries and pandas.
• Explore boolean logic for decision-making in Python.
• Learn control flow, filtering, and loops for efficient data processing.
• Apply Python skills to real-world scenarios.

In this course, you will:

• Learn to write custom functions in Python.
• Understand function parameters, return values, and default arguments.
• Gain insight into scoping and lambda functions.
• Practice error handling in function writing and apply skills to analyse Twitter DataFrames.

In this course, you will:

• Explore iterators and their role in Python.
• Master list comprehensions for efficient data manipulation.
• Apply learned techniques in a case study to solidify understanding.
• Enhance Python data science skills through practical application.

In this course, you will:

• Understand the concept of version control and its benefits for software and data projects.
• Learn to use Git to create repositories and track files.
• Explore how Git stores data through blobs, trees, and commits.
• Compare the state of a project at different points in time to see what changed, by whom, and when.
• Customize your view of a repository’s history and learn techniques to undo changes or revert files.

In this course you will:

• Understand key probability concepts like random variables, mean, variance, and conditional probability.
• Learn important theorems such as the law of large numbers and the central limit theorem.
• Apply probability principles in Python for data science and machine learning.
• Develop skills to interpret and work with uncertainty in real-world data.

In this course you will:

• Learn how to collect, analyze, and draw conclusions from data using statistical methods.
• Calculate averages, create scatterplots, and measure correlation to explore relationships in data.
• Understand probability as the foundation of statistical reasoning.
• Use Python to design studies and make data-driven decisions.

In this 2-part course you will:

• Build a foundation in statistical inference to draw clear and meaningful conclusions from data.
• Use Python tools to develop statistical thinking and effectively interpret what your data reveals.
• Expand your statistical inference skills by learning parameter estimation and hypothesis testing using real data sets.
• Apply these techniques to analyze Darwin’s finch beak measurements, gaining practical experience to tackle your own inference problems.

You will culminate all your learning and put them into practice with a real-life project.