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Data Preparation (Import and Cleaning) for Python

Python can be a powerful tool for data preparation. In this course, we will quickly cover how to connect to various database types. Then, we will jump into using the pandas Python package for data preparation. We will look at examples of cleansing missing and outlying data as well as data visualizations and exploration. In addition to the pandas package, we will also look at preprocessing data for machine learning using the scikit-learn Python package. Before beginning this course, you should have a strong knowledge of Python and data approaches. Check out the **Prerequisite and Related Courses** lesson in the Introduction section for a starting point.

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Data Preparation Python Pandas Data Cleaning Data Visualization Machine Learning scikit-learn

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Teaches data preparation using Pandas for data cleansing, data visualization, and data exploration

Provides instruction for preprocessing data for machine learning techniques with scikit-learn

Designed for students with a solid understanding of Python and data concepts

Led by the respected A Cloud Guru instructors who have extensive knowledge and expertise in cloud technologies

Utilizes a mix of video lectures and hands-on exercises to enhance learning

Covers data preparation best practices and industry-standard techniques

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Reviews summary

Practical python data preparation for professionals

According to learners, this course provides a solid foundation and practical strategies for data preparation using Python. Students particularly praise the hands-on labs and clear explanations from the instructor on topics like handling missing values and outliers. The course effectively covers core Pandas applications and introduces Scikit-learn for machine learning preprocessing, making it highly applicable for real-world data challenges. However, some find the prerequisite for strong Python knowledge to be significantly demanding, potentially making it less suitable for beginners. A few intermediate learners also note that while comprehensive, it could offer more advanced techniques for complex data types or deep dives into certain topics, suggesting it serves best as a robust introduction for those with the proper foundation.

Instructor explains complex topics with great clarity.

"The instructor explains complex topics like handling missing values and outliers with such clarity."

"I found the explanations clear and the examples easy to follow."

"The instruction was clear, and the exercises reinforced the concepts well."

Skills learned are directly useful for data roles.

"This course was exactly what I needed to bridge the gap between basic Python and actual data work."

"I feel much more confident tackling data quality issues now. Definitely geared towards professionals."

"This course helped me land my first data analyst job. The skills taught here were directly applicable."

Focuses on real-world data issues and practical applications.

"I especially loved the hands-on labs with real-world datasets; they made the concepts stick."

"The hands-on practice with various data cleaning techniques was invaluable. I've been struggling with messy datasets at work, and this course provided clear, practical strategies."

"I gained practical tools and strategies that I could apply immediately to my work."

"The hands-on coding aspect is very effective."

Provides solid basics but less advanced techniques.

"I was hoping for more advanced techniques in data imputation or dealing with highly unstructured data. For someone with intermediate skills, it might not offer enough new insights."

"It covers the basics well. I just wish there was more on error handling during data import or more complex data types like nested JSONs."

"I would have liked to see more advanced topics on feature engineering for machine learning."

Requires a strong prior understanding of Python.

"I was completely lost from the start. The prerequisites are severely understated. 'Strong knowledge of Python' means you basically need to be proficient in numpy and pandas already."

"The 'strong knowledge of Python' prerequisite is no joke. I had to brush up on a few things before diving in."

"This course is not beginner-friendly at all for someone looking to learn data preparation from scratch."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Preparation (Import and Cleaning) for Python with these activities:

Read 'Python for Data Analysis' by Wes McKinney

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Gain a deeper understanding of Python's role in data analysis and data preparation by reviewing a comprehensive book on the subject.

View Python for Data Analysis: Data Wrangling with... on Amazon

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Purchase or borrow a copy of 'Python for Data Analysis'.
Read through the chapters covering data manipulation, cleaning, and preparation.

Compile a collection of useful data preparation resources

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Build a valuable repository of data preparation resources, including tools, techniques, and best practices.

Browse courses on Compilation

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Conduct research to find useful data preparation resources.
Organize and categorize the resources in a central location.

Review pandas library basics

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Review the basics of the pandas library to ensure a strong foundation for data preparation tasks in the course.

Browse courses on Pandas

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Visit the pandas documentation website and read the tutorial.
Practice creating and manipulating DataFrames using pandas.

Nine other activities

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Review Python basics

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Review the basics of Python syntax, data structures, and algorithms to prepare for this course.

Browse courses on Python

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Read through a Python tutorial or textbook.
Complete a few practice problems or exercises.

Form a study group and discuss data preparation challenges

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Enhance understanding of data preparation concepts and techniques by discussing challenges and solutions with peers.

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Identify other students taking the course and form a study group.
Meet regularly to discuss data preparation challenges, share resources, and provide support.

Follow a pandas tutorial

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Follow a tutorial or online course on pandas to learn its core features and how to use it for data preparation.

Browse courses on Pandas

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Choose a pandas tutorial or online course.
Work through the tutorial or course step-by-step.

Follow tutorials on data preprocessing with scikit-learn

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Enhance understanding of data preprocessing techniques for machine learning tasks by following guided tutorials on scikit-learn.

Browse courses on scikit-learn

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Search for tutorials on scikit-learn data preprocessing.
Complete several tutorials, practicing data scaling, feature selection, and other preprocessing methods.

Attend a workshop on advanced data preparation techniques

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Expand knowledge and skills in data preparation by attending a workshop led by industry experts.

Browse courses on Advanced Data Analysis

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Research and find workshops on advanced data preparation techniques.
Register and attend the workshop.

Practice data cleaning with pandas

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Complete practice exercises or coding challenges that focus on cleaning and preparing data with pandas.

Browse courses on Data Cleaning

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Find a set of practice problems or exercises on data cleaning.
Work through the exercises, focusing on applying pandas techniques.

Solve coding exercises on data preparation

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Reinforce understanding of data preparation techniques by solving coding exercises that simulate real-world scenarios.

Browse courses on Data Preparation

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Find online coding platforms or resources that provide data preparation exercises.
Attempt to solve several exercises, focusing on applying pandas and scikit-learn for data cleaning and preprocessing tasks.

Project: Build a simple data pipeline

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Develop a small project that involves connecting to a database, cleaning and preparing data with pandas, and visualizing it.

Browse courses on Data Pipeline

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Choose a simple dataset and database to work with.
Develop a data pipeline that connects to the database, cleans the data, and prepares it for analysis.
Create visualizations to explore and present the prepared data.

Develop a data preparation pipeline for a specific dataset

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Apply the concepts learned in the course to a practical project by creating a data preparation pipeline for a dataset of your choice.

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Select a dataset that requires data preparation.
Design and implement a data preparation pipeline using pandas and scikit-learn.
Document the pipeline and share it with others.

Career center

Learners who complete Data Preparation (Import and Cleaning) for Python will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts explore and analyze raw data, helping organizations understand their operations more clearly. They work closely with management to advise them about how to optimize performance and increase revenue. The Data Preparation (Import and Cleaning) for Python course will provide you with the skills needed to effectively organize and clean data before analyzing it, which is an important step in the data analysis process. This course may help you build a foundation for a successful career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Data Engineer

Data Engineers design, build, and maintain data infrastructure. They work closely with Data Analysts and other stakeholders to collect, transform, and store data. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential tasks for a Data Engineer. This course may help you build a foundation for a successful career as a Data Engineer.

See salaries and explore the career path for Data Engineer

Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They work closely with management to advise them about how to optimize performance and increase revenue. The Data Preparation (Import and Cleaning) for Python course will provide you with the skills needed to effectively organize and clean data before analyzing it, which is an important step in the data science process. This course may help you build a foundation for a successful career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. They work closely with Data Scientists and other stakeholders to collect, transform, and store data. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential tasks for a Machine Learning Engineer. This course may help you build a foundation for a successful career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

Quantitative Analysts use mathematics, statistics, and computer science to develop financial models. They work closely with portfolio managers and other stakeholders to make investment decisions. The Data Preparation (Import and Cleaning) for Python course will provide you with the skills needed to effectively organize and clean data before analyzing it, which is an important step in the quantitative analysis process. This course may help you build a foundation for a successful career as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

Software Engineers design, build, and maintain software systems. They work closely with stakeholders such as product managers, designers, and customers to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Software Engineer. This course may help you get started in a career as a Software Engineer.

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Database Administrator

Database Administrators design, build, and maintain databases. They work closely with stakeholders such as developers, analysts, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Database Administrator. This course may help you get started in a career as a Database Administrator.

See salaries and explore the career path for Database Administrator

Data Architect

Data Architects design and manage data architectures. They work closely with stakeholders such as business analysts, developers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Data Architect. This course may help you get started in a career as a Data Architect.

See salaries and explore the career path for Data Architect

Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make better decisions. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Business Intelligence Analyst. This course may help you get started in a career as a Business Intelligence Analyst.

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Data Visualization Specialist

Data Visualization Specialists create visual representations of data. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Data Visualization Specialist. This course may help you get started in a career as a Data Visualization Specialist.

See salaries and explore the career path for Data Visualization Specialist

Financial Analyst

Financial Analysts use data to help businesses make better financial decisions. They work closely with stakeholders such as executives, managers, and investors to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Financial Analyst. This course may help you get started in a career as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts use data to help businesses make better marketing decisions. They work closely with stakeholders such as executives, managers, and customers to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Marketing Analyst. This course may help you get started in a career as a Marketing Analyst.

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Operations Research Analyst

Operations Research Analysts use data to help businesses make better operational decisions. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for an Operations Research Analyst. This course may help you get started in a career as an Operations Research Analyst.

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Risk Analyst

Risk Analysts use data to help businesses identify and manage risks. They work closely with stakeholders such as executives, managers, and regulators to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Risk Analyst. This course may help you get started in a career as a Risk Analyst.

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Statistician

Statisticians use data to help businesses make better decisions. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Statistician. This course may help you get started in a career as a Statistician.

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Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Preparation (Import and Cleaning) for Python.

Python Data Science Handbook

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Provides a comprehensive guide to data science using Python, covering essential tools and techniques for data preparation, analysis, and visualization. It offers a solid foundation for understanding the concepts and methods used in this course.

Data Preparation (Import and Cleaning) for Python

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Reviews summary

Practical python data preparation for professionals

Activities

Career center

Reading list

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