Importing & Cleaning Data with Python

Data scientists are responsible to clean, manipulate, and interpret data. Before you can do all of this, however, you need to know how to import data in Python. This course will show you how to import data from flat files such as.txt and.csv into Python. It also covers files native to other programs like Excel spreadsheets, Stata files and SAS files. This course also covers relational databases like SQLite or PostgreSQL.

Data scientists are responsible to clean, manipulate, and interpret data. Before you can do all of this, however, you need to know how to import data in Python. This course teaches you how to import data from flat files such as.txt and.csv into Python. It also covers files native to other programs like Excel spreadsheets, Stata files and SAS files. This course will help you expand your knowledge and teach you how to pull data from Application Programming Interfaces APIs. For example, the Twitter streaming API allows us to stream live Tweets.

Data cleansing is a crucial task in data science, as it ensures accurate results and prevents incorrect conclusions. Data scientists spend roughly 80% of their time cleaning and manipulating data, with only 20% dedicated to analysis. Without proper data cleaning, both machine learning and data analysis will fail.

This course on data science with Python focuses on teaching you how to identify, treat, and fix various data-cleaning problems. It covers topics such as dealing with wrong data types, ensuring data falls within the correct range, handling missing data, and managing record linking.

By taking this course, you will learn how to clean datasets using Python. It provides a comprehensive understanding of the techniques and tools necessary to clean and manipulate data effectively. The course emphasizes practical applications and provides hands-on experience in Python.

Some of the key keywords associated with this course include data science with Python training, cleaning the data in Python, Python for data analysis, Python and data science course, advanced Python for data science, and more. By enrolling in this course, you will gain expertise in utilizing Python libraries for data science and analysis.

Overall, this course is designed to equip you with the essential skills and knowledge required to effectively clean and manipulate data for accurate analysis. It is ideal for anyone interested in pursuing a career in data science or for professionals seeking to enhance their existing skills in Python and data cleaning techniques.

Although data often arrives in a format that is difficult to understand by humans, it isn't ideal for data analysis. Pandas can convert DataFrames into different formats. This course will improve your skills as an analyst and data scientist. This course will teach you how to manipulate string columns as well as nested information within a DataFrame. You will work with real-world data like book reviews, FIFA player ratings and churn analysis.

Working in Python requires that you know how to prepare your data. You must know what to do before you can get the real insights. This track will teach you how to import data from many sources including.csv and.xls. Once you have learned how to import your data, it is time to prepare your data to be analysed. As you work with real-world data like restaurant reviews, you'll learn how to deal with incorrect data types, fix missing data, and link records. The Tweepy package will allow you to access Twitter's API and scrape the internet for data. This track will give you the data prep skills that you need to clean up your data.