Google Data Analysis with Python

In this course, you’ll explore three exploratory data analysis (EDA) practices: cleaning, joining, and validating. You'll discover the importance of these practices for data analysis, and you’ll use Python to clean, validate, and join data. By the end of this course, you will be able to: • Apply input validation skills to a dataset with Python • Explain the importance of input validation • Demonstrate how to transform categorical data into numerical data with Python • Explain the importance of categorical versus numerical data in a dataset • Explain the importance of recognizing outliers in a dataset • Demonstrate how to identify outliers in a dataset with Python • Understand when to contact stakeholders or engineers regarding missing values • Explain the importance of ethically considering missing values • Demonstrate how to identify missing data with Python

In this course, you’ll explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on data structures that are among the most useful for data professionals: lists, tuples, dictionaries, sets, and arrays. You’ll also discover how to categorize data using data loading, cleaning, and binning. Lastly, you’ll learn about two of the most widely used and important Python tools for advanced data analysis: NumPy and pandas. By the end of this course, you will be able to: • Explain how to manipulate dataframes using techniques such as selecting and indexing, boolean masking, grouping and aggregating, and merging and joining • Describe the main features and methods of core pandas data structures such as dataframes • Describe the main features and methods of core NumPy data structures such as arrays and series • Define Python tools such as libraries, packages, modules, and global variables • Describe the main features and methods of built-in Python data structures such as lists, tuples, dictionaries, and sets

Finding stories in data using exploratory data analysis (EDA) is all about organizing and interpreting raw data. Python can help you do this quickly and effectively. In this course, you’ll learn how to use Python to perform the EDA practices of discovering and structuring. By the end of this course, you will be able to: • Identify ethical issues that may come up during the data “discovering” practice of EDA • Use Python to merge or join data based on defined criteria • Use Python to sort and/or filter data • Use relevant Python libraries for cleaning raw data • Recognize opportunities for creating hypotheses based on raw data • Recognize when and how to communicate status updates and questions to key stakeholders • Apply Python tools to examine raw data structure and format. • Use the PACE workflow to understand whether given data is adequate and applicable to a data science project • Differentiate between the common formats of raw data sources (json, tabular, etc.) and data types

In this course, you’ll discover how to call functions to perform useful actions on your data. You’ll also learn how to write conditional statements to tell the computer how to make decisions based on your instructions. And you’ll practice writing clean code that can be easily understood and reused by other data professionals. By the end of this course, you will be able to: • Explain the purpose and logic of conditional statements such as if, else, and elif • Use comparators and logical operators to compare values • List the benefits of commenting on code • Identify best practices for writing clean code such as reusability, modularity, and refactoring • Describe how to define Python functions using the def and return keywords

In this course, you'll discover the main features and benefits of the Python programming language, and how Python can help power your data analysis. Python is an object-oriented programming language based on objects that contain data and useful code. You’ll become familiar with the core concepts of object-oriented programming: object, class, method, and attribute. You’ll learn about Jupyter Notebooks, an interactive environment for coding and data work. You’ll investigate how to use variables and data types to store and organize your data; and, you'll begin practicing important coding skills. By the end of this course, you will be able to: - Explain Python fundamentals, including core Python syntax, data types (integer, float, string), and variable assignment - Define fundamental concepts like object, class, method, and attribute in object-oriented programming - Recognize the uses and benefits of Jupyter Notebook for data work and as a Python environment - Identify Python's relevance to data science and why it is an essential tool for data analysis - Perform basic mathematical calculations in Python - Use Python's inherent capabilities to explore data effectively with built-in functions and keywords - Gain knowledge of how to manage and utilize Python packages and interpreter options