--- title: Data Analysis description: This discipline involves cleaning, transforming, and modeling data to discover useful information and support decision-making. Learners will understand how to manipulate datasets, perform exploratory analysis, and extract actionable insights using structured analytical workflows. category: programming-tech subcategory: technical-skills difficulty: beginner, intermediate, advanced url: /subject/data-analysis --- # Data Analysis This discipline involves cleaning, transforming, and modeling data to discover useful information and support decision-making. Learners will understand how to manipulate datasets, perform exploratory analysis, and extract actionable insights using structured analytical workflows. ## Available Resources 3 Videos • 4 Courses • 1 Websites ## Videos ### 1. Data Analysis with Python and Pandas Tutorial First video in Corey Schafer's pandas series, walking through installing pandas and Jupyter, loading a CSV survey dataset into a DataFrame, and inspecting its shape, columns, and rows. Viewers finish able to set up a working environment for data analysis in Python. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=ZyhVh-qRZPA **Tags:** python, pandas, dataframes, jupyter-notebook ### 2. Excel for Beginners - The Complete Course Full-length freeCodeCamp course taught by Grand Canyon University professor Shad Sluiter, covering Excel from an empty workbook through formulas, functions, formatting, and charts. Viewers come away able to build and analyze basic spreadsheets for everyday data work. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=Vl0H-qTclOg **Tags:** excel, spreadsheets, excel-formulas, charts ### 3. Data Visualization Full Course - Learn Data Visualization in 7 Hours A long-form Python course covering NumPy arrays, Pandas dataframes, and plotting with Matplotlib and Seaborn. Work through it to load, clean and summarize tabular datasets, then chart the results in code. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=r-uOLxNrNk8 **Tags:** python, matplotlib, seaborn, pandas, numpy ## Websites ### 1. DataQuest Browser-based learning platform teaching data analysis through guided coding exercises and portfolio projects in Python, R, and SQL. Learners work through structured career paths covering data cleaning, visualization, statistics, and databases; a limited free tier precedes a paid subscription. **Difficulty:** Beginner | **Price:** Paid **Link:** https://www.dataquest.io **Tags:** python, sql, data-cleaning, data-visualization, interactive-coding ## Courses ### 1. Data Analysis with R Programming This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/exploratory-data-analysis **Tags:** exploratory-data-analysis, r-programming, data-visualization, ggplot2, clustering ### 2. Data Analysis with Python Learn data analysis with Python! This free course covers NumPy, Pandas, data cleaning, and visualization. Start your data science journey today! **Difficulty:** Beginner | **Price:** Free **Link:** https://www.freecodecamp.org/learn/data-analysis-with-python/ **Tags:** python, pandas, numpy, data-analysis, data-visualization ### 3. SQL for Data Science As data collection has increased exponentially, so has the need for people skilled at using and interacting with data; to be able to think critically, and provide insights to make better decisions and optimize their businesses. This is a data scientist, “part mathematician, part computer scientist, and part trend spotter” (SAS Institute, Inc.). According to Glassdoor, being a data scientist is the best job in America; with a median base salary of $110,000 and thousands of job openings at a time. The skills necessary to be a good data scientist include being able to retrieve and work with data, and to do that you need to be well versed in SQL, the standard language for communicating with database systems. This course is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You'll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. You will create new tables and be able to move data into them. You will learn common operators and how to combine the data. You will use case statements and concepts like data governance and profiling. You will discuss topics on data, and practice using real-world programming assignments. You will interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape your data for targeted analysis purposes. Although we do not have any specific prerequisites or software requirements to take this course, a simple text editor is recommended for the final project. So what are you waiting for? This is your first step in landing a job in the best occupation in the US and soon the world! **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/sql-for-data-science **Tags:** sql, data-analysis, joins, case-statements, coursera ### 4. Data Analysis with Python Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement. **Difficulty:** Beginner | **Language:** English | **Duration:** This course requires approximately two hours a week for six weeks. | **Price:** Free **Link:** https://www.coursera.org/learn/data-analysis-with-python **Tags:** courses, technology-computer-science, data-science --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/data-analysis