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Maureen Wolfson

Take Udacity's free analytical dataset course and learn how to prepare data to ensure the efficacy of your analysis while improving fluency in Alteryx. Learn online with Udacity.

What's inside

Syllabus

Understand the most common data types. Understand the various sources of data.
Identify common types of dirty data. Make adjustments to dirty data to prepare a dataset. Identify and adjust for outliers.
Read more
Summarize, cross-tabulate, transpose, and reformat data to prepare a dataset for analysis.
Join and union data from different sources and formats.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers essential data types and sources, ensuring a strong foundation
Addresses common data quality issues and provides techniques for data preparation
Develops skills in data summarization, cross-tabulation, and reformatting, which are essential for data analysis
Provides hands-on experience with Alteryx, a leading data analytics tool
Maureen Wolfson, an experienced instructor, guides learners through the course
Offers a blend of videos, readings, and hands-on labs, enhancing the learning experience

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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 Creating an Analytical Dataset with these activities:
Review basic data types and data sources
Strengthen the foundation in data fundamentals, ensuring familiarity with different data types and sources
Browse courses on Data Types
Show steps
  • Revisit your notes or review online resources on data types
  • Identify common data sources and their characteristics
Read 'Data Preparation for Data Science' by Nathan Yau
Provide a comprehensive understanding of the principles and processes involved in data preparation for data science
View Visualize This on Amazon
Show steps
  • Read the book thoroughly, focusing on key concepts and techniques
  • Summarize the main takeaways and apply them to your own data preparation tasks
Watch a tutorial on data preparation in Alteryx
Provide a visual introduction to data preparation techniques in Alteryx and reinforce the concepts covered in the course
Show steps
  • Search for a beginner-friendly tutorial on Alteryx data preparation
  • Watch the tutorial and take notes on the key steps involved in data preparation
Five other activities
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Show all eight activities
Practice data summarizing and tabulation in Alteryx
Provide hands-on practice in using Alteryx tools for data summarization and tabulation, enhancing proficiency in data manipulation
Show steps
  • Open an Alteryx workflow and import a dataset
  • Summarize a numeric column using the Summarize tool
  • Create a crosstabulation of two categorical columns
Develop a data preparation pipeline for a specific dataset
Provide hands-on experience in designing and implementing a comprehensive data preparation pipeline, showcasing end-to-end data management skills
Show steps
  • Choose a dataset and define the data preparation goals
  • Design a data preparation pipeline using Alteryx tools
  • Implement the pipeline and evaluate its effectiveness
Create a data dashboard that visualizes data preparation results
Foster data visualization skills and solidify understanding of how to present data preparation results effectively
Show steps
  • Select relevant metrics and dimensions from the prepared data
  • Design and develop a data dashboard using appropriate visualization techniques
  • Interpret and communicate insights from the visualized data
Create a presentation on best practices for data preparation
Encourage critical thinking, analytical skills, and effective communication by synthesizing knowledge on best practices in data preparation
Show steps
  • Research different techniques and methodologies for data preparation
  • Create a presentation that visually explains the best practices and their benefits
  • Present the findings to peers or colleagues to share knowledge
Volunteer with a data science or analytics organization
Provide practical experience working with real-world data, fostering teamwork and problem-solving skills
Show steps
  • Identify organizations that align with your interests in data science or analytics
  • Contact the organizations and inquire about volunteer opportunities
  • Participate in projects and contribute to data-related tasks

Career center

Learners who complete Creating an Analytical Dataset will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts interpret large datasets to help organizations make informed decisions. The skills and knowledge gained in the Creating an Analytical Data Set course can be applied directly to data collection, cleaning, managing, and summarizing, which are essential tasks for this role. The course also provides a strong foundation in using Alteryx, a software specifically designed for data analysis.
Data Scientist
Data Scientists develop and implement machine learning and statistical models to solve complex business problems. The data preparation techniques and principles covered in Creating an Analytical Data Set provide a foundational understanding of how to work with data effectively. Furthermore, the course's emphasis on Alteryx can be particularly beneficial for Data Scientists as it can streamline and automate many aspects of data analysis.
Statistician
Statisticians use statistical methods to analyze data and draw conclusions. The Creating an Analytical Data Set course provides a strong foundation in data preparation and analysis techniques, which are essential skills for Statisticians. Additionally, the course's emphasis on data visualization can help Statisticians communicate their findings effectively to non-technical audiences.
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve complex business problems. The Creating an Analytical Data Set course provides a foundational understanding of data preparation and analysis techniques, which are essential for this role. Additionally, the course's emphasis on Alteryx can be particularly beneficial for Machine Learning Engineers as it can be used to automate many aspects of data analysis and model development.
Business Analyst
The Creating an Analytical Data Set course can be beneficial for Business Analysts who need to use data to make informed decisions and communicate insights to stakeholders. The course provides a comprehensive overview of data types, data sources, and data cleaning techniques, all of which are essential skills for Business Analysts. Additionally, the course's emphasis on data visualization and presentation can help Business Analysts effectively communicate their findings to non-technical audiences.
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to help users understand complex information. The Creating an Analytical Data Set course provides a strong foundation in data preparation and analysis techniques, which are essential for this role. Additionally, the course's focus on data visualization and presentation can help Data Visualization Specialists design and develop effective data visualizations.
Data Engineer
Data Engineers design and build data pipelines and systems to support data analysis and reporting. The Creating an Analytical Data Set course can be useful for Data Engineers as it provides a comprehensive overview of data types, data sources, and data cleaning techniques, all of which are essential for this role. Additionally, the course's emphasis on data quality and data governance can help Data Engineers ensure that the data they are working with is accurate and reliable.
Data Architect
Data Architects design and manage data systems to support business intelligence and analytics. The Creating an Analytical Data Set course provides a comprehensive overview of data types, data sources, and data management techniques, all of which are essential skills for this role. Additionally, the course's emphasis on data quality and data governance can help Data Architects ensure that the data systems they design are reliable and efficient.
Health Data Analyst
The Creating an Analytical Data Set course may be useful for Health Data Analysts who need to work with health data. The course provides a comprehensive overview of data types, data sources, and data analysis techniques, which can be beneficial for Health Data Analysts who need to prepare and analyze health data to improve patient care and outcomes.
Database Administrator
The Creating an Analytical Data Set course may be useful for Database Administrators who need to work with data from multiple sources and formats. The course provides a comprehensive overview of data types, data sources, and data integration techniques, which can be beneficial for Database Administrators who need to design and manage databases that support data analysis and reporting.
Financial Analyst
The Creating an Analytical Data Set course may be useful for Financial Analysts who need to work with financial data. The course provides a comprehensive overview of data types, data sources, and data analysis techniques, which can be beneficial for Financial Analysts who need to prepare and analyze financial data to make investment decisions.
Quantitative Analyst
The Creating an Analytical Data Set course may be useful for Quantitative Analysts who need to prepare and analyze large datasets. The course provides a comprehensive overview of data types, data sources, and data analysis techniques, which can be beneficial for Quantitative Analysts who need to develop and implement quantitative models to solve business problems.
Actuary
The Creating an Analytical Data Set course may be useful for Actuaries who need to analyze data to assess risk. The course provides a comprehensive overview of data types, data sources, and data analysis techniques, which can be beneficial for Actuaries who need to develop and implement models to assess the risk of insurance policies.
Epidemiologist
The Creating an Analytical Data Set course may be useful for Epidemiologists who need to analyze data to understand the causes and spread of disease. The course provides a comprehensive overview of data types, data sources, and data analysis techniques, which can be beneficial for Epidemiologists who need to design and conduct epidemiological studies.
Market Researcher
The Creating an Analytical Data Set course may be useful for Market Researchers who need to collect and analyze data to understand consumer behavior. The course provides a comprehensive overview of data types, data sources, and data analysis techniques, which can be beneficial for Market Researchers who need to design and conduct market research studies.

Reading list

We've selected 21 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 Creating an Analytical Dataset.
Provides a comprehensive overview of statistical learning methods, which are used in data analytics to build predictive models. It valuable resource for data analysts who want to learn more about the statistical foundations of data analytics.
Provides a comprehensive overview of statistical learning and data mining techniques. Helpful as a reference for theoretical foundations and advanced concepts, complementing the course's focus on practical skills.
Provides a comprehensive overview of data mining concepts, techniques, and algorithms. Helpful as a reference for additional reading, complementing the course's hands-on approach with theoretical foundations.
Provides a comprehensive guide to R for data science. It covers a wide range of topics, including data loading, data cleaning, data transformation, and data visualization.
Provides a comprehensive guide to feature engineering techniques for machine learning. Helpful for understanding the importance of feature engineering in data preparation and its impact on model performance.
Provides a comprehensive guide to Python for data analysis. It covers a wide range of topics, including data loading, data cleaning, data transformation, and data visualization.
Provides a comprehensive guide to Matplotlib for data analysis. It covers a wide range of topics, including data loading, data cleaning, data transformation, and data visualization.
Provides a practical guide to Bayesian statistics, which powerful approach to data analysis that allows you to incorporate uncertainty into your models. It valuable resource for data analysts who want to learn more about Bayesian statistics.
Provides a practical guide to data preparation, which critical step in the data analytics process. It covers a wide range of topics, including data cleaning, transformation, and feature engineering.
Provides a broad overview of data preparation for data mining. Introduces data quality assessment, data cleaning, feature selection, and transformation techniques. Helpful in understanding the fundamentals of data preparation, complementing the course's focus on Alteryx.
Provides practical guidance on data preparation for analytics projects. Helpful for understanding best practices and industry standards in data preparation.
Provides a comprehensive guide to data science using Python. Helpful for understanding the fundamentals of data manipulation, analysis, and visualization, complementing the course's focus on Alteryx.
Provides a comprehensive overview of data mining techniques, including classification, regression, and clustering. It valuable resource for data analysts who want to learn more about the advanced techniques used in data mining.
Provides a practical guide to data science for beginners. It covers a wide range of topics, including data collection, data preparation, data analysis, and data visualization.
Provides an accessible introduction to data science and analytics for business professionals. Helpful for understanding the broader context and applications of data preparation in the business world.
Provides a comprehensive overview of the data analytics process, from data collection and preparation to data analysis and visualization. It valuable resource for beginners who want to learn the basics of data analytics.
Provides a practical guide to data mining using SPSS Modeler. Helpful for understanding techniques for data exploration, modeling, and deployment, while providing an alternative perspective to the course's focus on Alteryx.
Provides a practical introduction to data manipulation using R. Helpful for understanding techniques for data cleaning, transformation, and aggregation, complementing the course's focus on Alteryx.
Provides a practical introduction to exploratory data analysis using R. Helpful for understanding techniques for data exploration and visualization, complementing the course's focus on Alteryx.
Provides a gentle introduction to data analytics for beginners. It covers a wide range of topics, including data collection, data preparation, data analysis, and data visualization.

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