Data analysis is one of the fastest growing fields, and Python is one of the best tools to solve these problems. In this course, Getting Started with Data Analysis Using Python, you'll learn how to use Python to collect, clean, analyze, and persist data. First, you'll discover techniques including persisting data with csv files, pickle files, and databases, along with the ins and outs of basic SQL and Sqlite command line. Next, you'll delve into data analysis and how to use common data structures, such as lists, dictionaries, tuples, and sets. Additionally, you'll learn how to use these structures and apply these skills to widely available stock market data. Finally, you'll explore pygal, a Python library for data visualization. When you're finished with this course, you'll have the necessary knowledge to efficiently build stunning charts and graphs utilizing data analysis in Python.
Terry has over 15 years’ experience in software development from business applications to embedded systems. He has worked at a variety of government agencies and private firms including: California Senate, Office Legislative Counsel, and Space Systems Loral. In his spare time, Terry enjoys hiking and spending time with his family.
Course Overview Hi, my name's Terry Toy. Welcome to Getting Started with Data Analysis using Python. I've been programming for over 15 years, and Python is a great language to learn. So if you're an Excel power user, or perhaps new to data analysis, I welcome you to this course. In this course, we'll learn not only about Python, but specifically how to apply it to data analysis. We'll learn how to collect, clean, persist, analyze, and how to create stunning data visualizations. The core of this course is analyzing data, and we'll learn about data structures such as lists, dictionaries, tuples, and sets, and we'll apply these structures to market analysis, because there's so much information about the stock market. And regarding to persisting data, we're going to learn how to work with CSV files and SQLite, a relational database. We'll learn how to insert and collect data, and store it all within a database, then how to retrieve that data, whether from a CSV file or a SQLite database, and to build our data structures, our lists, our dictionaries, our tuples, and then use that to analyze our data, and then build stunning visualizations. So I'm excited; I hope you'll join me in this course. Let's learn about Python and data analysis.
Persisting Data in Databases and Files Welcome back to our introduction to data analysis using Python. In this module, we're going to focus on Persisting Data. Persisting data is the ability to store data, and then to recall it and use it over again. Common methods are with a database, but we're also going to look at files, and in a particular case, of pickle files. So in this module, we're going to focus mostly on the database, and we'll be using sqlite3. SQLite is a file-based database system, and it's very powerful, and we use all the basic SQL commands to access it. We'll also learn about files, and specifically pickle files, as well as how to organize our data in data objects. So the first thing we're going to learn about, or some of the core topics we're going to learn about in this module include, first we're going to learn about sqlite3. That's the database engine we'll be using, it's readily available with Python, and it's very powerful. We'll learn about the DB Browser for SQLite. This is a graphical user interface to the SQLite. It allows us to quickly create tables, update data, view data, and many other powerful things, all in a graphical user interface. We'll also learn about CRUD, that's Create, Read, Update, and Delete. And we'll use Python code to manage the data. We'll create basic code in Python to execute SQL commands. Pickle files are another way to persist data. In this case, we're persisting complex or serialized data structures, so the dictionaries, lists, tuples that we learned about previously, we can actually store that as a pickle file, rather than extracting the data or retrieving the data and rebuilding those structures. We'll learn about data objects, which is another way to organize our data. We'll also look at the SQLite command-line, which is another tool instead of the graphical user interface to access the data stored within our SQLite database. Finally, to apply context to these topics, we'll build out our stock market application. So let's go ahead and get started. We're going to start with SQLite. This is our database engine, and one of the fundamental ways of persisting data.