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Intro to Data Analysis

Take Udacity's Introduction to Data Analytics and explore a variety of datasets, posing and answering your own questions and using the Python libraries NumPy, Pandas, and Matplotlib.

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What's inside

Syllabus

Go through the data analysis process from start to finish using a dataset about Udacity student engagement.

Learn how to use NumPy and Pandas to write clean, concise, and fast data analysis code for one-dimensional data.

Learn more features of NumPy and Pandas you can use to analyze two-dimensional data.

Conduct your own analysis over a dataset, and share what you've found!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to data analysis process using an industry-standard dataset

Builds skills in data manipulation and analysis

Leverages Python libraries for data analysis

Taught by experienced Udacity instructors, recognized in their field

Requires no prerequisites, making it accessible to various learners

May be suitable for learners transitioning to data analytics roles

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Reviews summary

Intro to data analysis as part of nanodegree

This course is a strong introduction to data analysis. While it is part of a Nanodegree program, the free course by itself leaves students confident in their new data analysis skills. A solid mix of videos, quizzes, and a final project make it a well-rounded online course.

The course is suitable for beginners with basic Python knowledge.

"I don't have enough progamming training or experience ... I really believe that I have learned what i need from this wonderful course."

This course is part of the Udacity Data Analyst Nanodegree.

"Completed as part of the Data Analyst Nanodegree."

The videos are well-produced and the instructor is clear and personable.

"The videos are well-produced ... the instructor (Caroline Buckey) is clear and personable."

The course content is practical and applicable.

"Teaching fundamental analysis skill of numpy and pandas with actual data projects."

The course could use more introduction about the datasets used.

"I believe it will be more helpful, if more introduction about the dataset is given."

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 Intro to Data Analysis with these activities:

Review Python Basics

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Refresh foundational Python programming skills to enhance understanding of data analysis concepts.

Browse courses on Python

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Review online tutorials or resources on Python basics.
Practice writing Python code snippets to reinforce understanding.

Consolidate and Review Course Materials

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Organize and review course materials to enhance retention and understanding.

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Create a central repository for notes, assignments, and quizzes.
Review the materials regularly to reinforce key concepts.

Write code in Python to clean and visualize data

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Practice writing Python code to clean and visualize data, which are essential skills for data analysis.

Browse courses on Data Cleaning

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Use NumPy to clean and manipulate one-dimensional data.
Use Pandas to clean and manipulate two-dimensional data.
Create visualizations using Matplotlib.

Seven other activities

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Explore Pandas Documentation

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Deepen understanding of Pandas features and functionalities by following tutorials and examples.

Browse courses on Pandas

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Review the official Pandas documentation to learn about its core functionality.
Follow online tutorials to explore specific features, such as data cleaning and visualization.

Peer Coding and Collaboration

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Collaborate with peers to work through coding challenges and discuss data analysis concepts, fostering learning and understanding.

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Join a study group or online forum for the course.
Pair up with a peer to work on code exercises.
Discuss data analysis techniques and share insights.

Practice with Python Libraries

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Practice using these libraries in various contexts to reinforce understanding of the course concepts.

Browse courses on Python

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Write a code snippet to load and explore a dataset using NumPy.
Use Pandas to create a DataFrame and visualize the data using Matplotlib.
Perform data manipulation and analysis tasks using NumPy and Pandas.

Read 'Data Analysis with Python' by Wes McKinney

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Gain deeper insights into data analysis concepts and best practices by reading a foundational book in the field.

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

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Read and understand the key concepts and techniques presented in the book.
Apply the concepts to practical data analysis tasks.

Build a data analysis dashboard

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Create a data analysis dashboard to showcase your skills in data analysis and communication.

Browse courses on Data Visualization

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Choose a dataset and identify key metrics.
Design and build the dashboard using a data visualization tool.
Present the dashboard to stakeholders.

Data Analysis Project

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Apply course knowledge to conduct a comprehensive data analysis project, reinforcing skills and solidifying understanding.

Browse courses on Data Analysis

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Define a research question and gather a relevant dataset.
Explore, clean, and analyze the data using NumPy and Pandas.
Create visualizations and draw meaningful conclusions from the data.
Write a report to document the project and present findings.

Contribute to Data Analysis Projects

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Engage in open-source projects related to data analysis, contributing to the community and enhancing practical skills.

Browse courses on Data Analysis

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Identify open-source data analysis projects on platforms like GitHub.
Review the project documentation and contribute to issue discussions.
Submit code contributions or improvements to the project.

Career center

Learners who complete Intro to Data Analysis will develop knowledge and skills that may be useful to these careers:

Market Researcher

The process of posing and answering data-driven questions is an essential aspect of Market Research. This course, with its focus on using NumPy and Pandas, can help you build a strong foundation in this area. This is especially true for those entering the field from non-business backgrounds, as it can equip them with the tools and techniques needed to get started in Market Research.

See salaries and explore the career path for Market Researcher

Data Engineer

For aspiring Data Engineers, this course will be a helpful starting point. Understanding data analysis, coupled with the use of NumPy and Pandas, is crucial for working with raw data and building data pipelines. This course may serve as a useful stepping stone for those seeking to enter the field

See salaries and explore the career path for Data Engineer

Data Journalist

For those interested in the field of Data Journalism, this course may be a useful stepping stone. It introduces foundational skills for data visualization and analysis, using tools such as NumPy, Pandas, and Matplotlib. Data visualization is a significant aspect of communicating insights from data effectively, and this course provides a platform for those seeking to get started with this skill.

See salaries and explore the career path for Data Journalist

Data Analyst

If you're aiming to join the field of Data Analysts, the course, Introduction to Data Analysis, could be a helpful first step towards success in this field. By introducing NumPy and Pandas, skills that are critical for Data Analysts, you'll learn how to write clean, concise, and fast data analysis code for one and two-dimensional data. These skills are integral to this field, and familiarity with them is crucial for your success in this industry

See salaries and explore the career path for Data Analyst

UX Researcher

Experience with data analysis is increasingly becoming essential for UX Researchers. Introduction to Data Analysis can help you develop the necessary skills to analyze and interpret user research data. Using tools such as NumPy and Pandas to manipulate and visualize data, this course will provide a strong foundation for conducting user research

See salaries and explore the career path for UX Researcher

Product Manager

For aspiring Product Managers, this course can be a useful resource. The ability to analyze data and draw insights is essential for understanding user needs, prioritizing features, and improving products. This course introduces NumPy and Pandas, which can be helpful for handling and visualizing product usage data.

See salaries and explore the career path for Product Manager

Business Analyst

For Business Analysts, the ability to efficiently analyze data and draw meaningful conclusions is crucial. This course provides a solid foundation in using NumPy and Pandas to analyze data for one and two dimensions. It may be useful for those seeking to enter the field or advance their current skills within it

See salaries and explore the career path for Business Analyst

Financial Analyst

For those who aim to become a Financial Analyst, this course provides an introduction to the tools and skills of the trade. Particularly helpful is its coverage of NumPy and Pandas for data analysis. Working with financial data often requires an understanding of how to write clean, concise, and fast data analysis code, and this course can help build that foundation

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

For those seeking to become Quantitative Analysts, this course can provide a solid introduction to essential tools. Familiarity with NumPy and Pandas will be beneficial considering the heavy reliance on data analysis and statistical modeling within the field. It may also assist in developing an understanding of how to approach data-driven decision-making

See salaries and explore the career path for Quantitative Analyst

Machine Learning Engineer

While not tailored specifically for Machine Learning Engineers, this course can be a useful introduction to essential data analysis tools. NumPy and Pandas are commonly used in Machine Learning, and this course will help build a foundation for understanding and manipulating data within this field

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

For Data Scientists, the course teaches the data analysis process from start to finish. Although not as thorough as a comprehensive data science curriculum would be, this course might prove helpful in building a foundation for your data science career. The process of posing and answering questions from datasets through NumPy, Pandas, and Matplotlib is core to daily functions of a Data Scientist

See salaries and explore the career path for Data Scientist

Operations Research Analyst

To become an Operations Research Analyst, this course may be a useful resource for those entering the field from a non-quantitative background. While it does not cover Operations Research specific methodologies, it will be helpful to familiarize yourself with data analysis, NumPy, and Pandas. It is critical for professionals in this field to be able to gather, analyze, and interpret data from multiple sources, and this course will prove helpful in developing those skills.

See salaries and explore the career path for Operations Research Analyst

Statistician

An Introduction to Data Analysis may be useful for those who wish to become Statisticians. Although the course does not cover advanced statistical methodologies, it would assist in building a foundation for many of the skills that Statisticians use regularly such as cleaning data, visualizing, and summarizing it. It also introduces the use of Pandas and NumPy for data analysis, skills that are not typically covered in other introductory statistics textbooks

See salaries and explore the career path for Statistician

Actuary

This course may be of assistance to those seeking to become Actuaries. While it does not delve into actuarial science, it strengthens foundational skills in data analysis. Familiarity with NumPy and Pandas, particularly for working with large datasets, is increasingly important for Actuaries in the modern workplace

See salaries and explore the career path for Actuary

Software Engineer

While it may not be a direct path, this course on Introduction to Data Analysis can be helpful for those who aim to become Software Engineers. Most modern Software Engineers work with data in one way or another. Familiarity with NumPy and Pandas, tools that enable the cleaning, manipulation, and analysis of data can certainly be an added advantage in the field

See salaries and explore the career path for Software Engineer

Reading list

We've selected 18 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 Intro to Data Analysis.

Python for Data Analysis

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Provides a comprehensive overview of data analysis with Python. It covers everything from data cleaning and wrangling to data visualization and modeling.

Intro to Data Analysis

Here's a deal for you

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Intro to data analysis as part of nanodegree

Activities

Career center

Reading list

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