After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Connect to an SQLite database using SQLAlchemy, execute SELECT queries, and load query results into pandas DataFrames
- Evaluate SQL JOIN operations that merge related tables and confirm that the combined data are correctly loaded into a DataFrame.
- Recognize the file structures, delimiters, and headers that characterize flat files and hierarchical HDF5 formats
- Use appropriate Python functions and libraries for importing plain text, CSV, Excel, SAS, Stata, MATLAB, HDF5, and pickled data files
- Use NumPy to load homogeneous numeric data, use pandas to load tabular data, and convert between NumPy arrays and pandas DataFrames when needed.
Prerequisites
Curriculum
Course outline
1
Introduction and flat files
In this chapter, you'll learn how to import data into Python from all types of flat files, which are a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas—you will learn how to use these packages to import flat files and customize your imports.
- Welcome to the course!50 XP
- Importing entire text files100 XP
- Importing text files line by line100 XP
- The importance of flat files in data science50 XP
- Pop quiz: what exactly are flat files?50 XP
- Why we like flat files and the Zen of Python50 XP
- Importing flat files using NumPy50 XP
- Using NumPy to import flat files100 XP
- Customizing your NumPy import100 XP
- Importing different datatypes100 XP
- Importing flat files using pandas50 XP
- Using pandas to import flat files as DataFrames (1)100 XP
- Using pandas to import flat files as DataFrames (2)100 XP
- Customizing your pandas import100 XP
- Final thoughts on data import50 XP
2
Importing data from other file types
You've learned how to import flat files, but there are many other file types you will potentially have to work with as a data scientist. In this chapter, you'll learn how to import data into Python from a wide array of important file types. These include pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files.
3
Working with relational databases in Python
In this chapter, you'll learn how to extract meaningful data from relational databases, an essential skill for any data scientist. You will learn about relational models, how to create SQL queries, how to filter and order your SQL records, and how to perform advanced queries by joining database tables.
Introduction to Importing Data in Python
Course
Complete
