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Data Wrangling for Business

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Data Wrangling for Business

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Gain insight into a topic and learn the fundamentals.
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

There are 7 modules in this course

Welcome to Data Wrangling for Business. This course will cover data wrangling principles and techniques for business. Key topics include data extraction, profiling, cleansing, integration, transformation, and automating data processes for business purposes. In the course, you will apply principles and techniques using data transformation tools, programming languages, and data process automation tools. The course offers you an opportunity to learn how to embed appropriate communication mechanisms for collaboration to identify and resolve real-world data challenges revealed in datasets and business processes, creating business value in today’s disparate computing and dynamic business environment.

In this module, you will learn about the structure of relational databases and how to use SQL queries for information retrieval, focusing on single-row and group functions. In the next module, you will build upon this foundation by exploring data manipulation and data joining techniques.

What's included

7 videos16 readings1 assignment

7 videos•Total 20 minutes
  • Course Overview•3 minutes
  • Meet Your Faculty•2 minutes
  • Relational Database Data Structure•3 minutes
  • Unique Values•3 minutes
  • Constraints•5 minutes
  • Working with Text Values•2 minutes
  • TIMESTAMP•3 minutes
16 readings•Total 67 minutes
  • Course Introduction•2 minutes
  • Syllabus - Data Wrangling for Business•10 minutes
  • Academic Integrity•1 minute
  • Data Wrangling Key Questions & Steps•2 minutes
  • Relational Databases•1 minute
  • Go to Canvas•1 minute
  • Key SQL Concepts•5 minutes
  • Overview of Predefined Functions •2 minutes
  • Single-Row vs. Group Functions•10 minutes
  • Overview of Single-Row Functions•2 minutes
  • Uses of Single-Row Functions•4 minutes
  • Single-Row Function Example•10 minutes
  • Overview of Group Functions•2 minutes
  • Uses of Group Functions•4 minutes
  • Group Function Example•10 minutes
  • Go to Canvas•1 minute
1 assignment•Total 30 minutes
  • Module 1 Quiz•30 minutes

The module also highlights how effective data manipulation and joining contribute to the broader goals of data wrangling and preparation, ensuring that data is both well-organized and ready for analysis.

What's included

14 readings1 assignment

14 readings•Total 113 minutes
  • Data Manipulation Overview•3 minutes
  • INSERT•4 minutes
  • Update•5 minutes
  • Delete•10 minutes
  • TCL Core Operations•10 minutes
  • Summary Table•10 minutes
  • Data Joining Overview•10 minutes
  • Data Joining Types Overview•10 minutes
  • Inner Join•10 minutes
  • Left Join•10 minutes
  • Right Join•10 minutes
  • Full Outer Join•10 minutes
  • Cross Join•10 minutes
  • Go to Canvas•1 minute
1 assignment•Total 45 minutes
  • Module 2 Quiz•45 minutes

In this module, you will learn how to explore datasets using Python. You’ll practice techniques to inspect dataset structure (rows, columns, and data types), and detect missing, invalid, or inconsistent data. You will also learn how to generate descriptive statistics and distribution summaries, as well as interpret profiling results to guide data cleansing and improve overall data quality.

What's included

2 videos16 readings1 assignment

2 videos•Total 6 minutes
  • Data Profiling•3 minutes
  • Data Profiling Example•3 minutes
16 readings•Total 82 minutes
  • Go to Canvas•1 minute
  • Data Profiling Overview•10 minutes
  • Discovering Data Structure Overview•2 minutes
  • Rows and Columns•7 minutes
  • Data Type•7 minutes
  • Non-Null Entries•7 minutes
  • Discovering Data Structure Example•10 minutes
  • Discovering Data Content Overview•3 minutes
  • Summary Statistics•4 minutes
  • Descriptive Statistics•4 minutes
  • Frequency Distribution•4 minutes
  • Missing Values•3 minutes
  • Duplicate Data•3 minutes
  • Incorrect or Ambiguous Data•4 minutes
  • Discovering Data Content Example•10 minutes
  • Data Profiling•3 minutes
1 assignment•Total 30 minutes
  • Module 3 Quiz•30 minutes

By the end of this module, you will be able to apply practical data cleansing techniques to improve data quality and make your analysis more accurate and trustworthy.

What's included

2 videos12 readings1 assignment

2 videos•Total 3 minutes
  • Data Cleansing•1 minute
  • Data Cleansing to Handle Outliers•2 minutes
12 readings•Total 32 minutes
  • Data Cleansing Overview•2 minutes
  • Go to Canvas•1 minute
  • Overview of Types of Data Issues•2 minutes
  • Missing Values•3 minutes
  • Duplicates•3 minutes
  • Inconsistent Formats•3 minutes
  • Outliers and Anomalies•3 minutes
  • Incorrect or Invalid Values•3 minutes
  • Data Types Conversions•3 minutes
  • Filtering•3 minutes
  • Data Cleansing Example•2 minutes
  • Practice: Data Profiling and Cleansing•4 minutes
1 assignment•Total 30 minutes
  • Module 4 Quiz•30 minutes

In this module, you will learn how to transform raw data into clean, structured datasets that are ready to be linked with other relevant data for enrichment. To perform these tasks, you will continue your work with Python and explore later in upcoming modules, tools like Alteryx to enhance your data preparation and enrichment workflow.

What's included

2 videos12 readings1 assignment

2 videos•Total 5 minutes
  • Data Transformation•2 minutes
  • Data Enrichment Example•3 minutes
12 readings•Total 82 minutes
  • Data Transformation Overview•1 minute
  • Converting Data Types•4 minutes
  • Converting Units of Measurement•4 minutes
  • Mapping Data Values•10 minutes
  • Splitting Data Values•10 minutes
  • Data Enrichment Overview•2 minutes
  • Data Unions•10 minutes
  • Data Joins•10 minutes
  • Derivation of New Values•10 minutes
  • Errors and Exceptions Overview•10 minutes
  • Handling Errors and Exceptions•10 minutes
  • Go to Canvas•1 minute
1 assignment•Total 30 minutes
  • Module 5 Quiz•30 minutes

This module emphasizes techniques for gathering, integrating, and transforming data from diverse sources. Hands-on exercises focus on automating data extraction from webpages and processing textual data, enabling the conversion of raw, unstructured information into structured, analyzable formats. By applying these methods, participants learn to create unified datasets that are ready for deeper analysis and the generation of meaningful insights.

What's included

1 video17 readings1 assignment

1 video•Total 2 minutes
  • Do Businesses Need to Automate Web Data Collection?•2 minutes
17 readings•Total 137 minutes
  • What is Data Integration?•3 minutes
  • What is Unstructured Text Data?•3 minutes
  • Common Transformation Techniques•4 minutes
  • Go to Canvas•1 minute
  • Web Scraping Overview•2 minutes
  • Go to Canvas•1 minute
  • Beautiful Soup Overview•3 minutes
  • Other Beautiful Soup Find Methods•3 minutes
  • Go to Canvas•1 minute
  • Beautiful Soup Function Example•90 minutes
  • Go to Canvas•1 minute
  • Transforming Unstructured Text Data•10 minutes
  • Natural Language Toolkit (NLTK)•3 minutes
  • Tokenization: Breaking Text Into Words•3 minutes
  • Removing Stopwords to Reduce Noise•4 minutes
  • Normalizing Words with Lemmatization•3 minutes
  • Result of Text Transformation•2 minutes
1 assignment•Total 30 minutes
  • Module 6 Quiz•30 minutes

In this module, you will learn how to use the industry automation tool, Alteryx, to automate the processes of data transformation and data integration. This skill will help you in your professional career to ease and expedite data processing.

What's included

5 readings1 assignment

5 readings•Total 101 minutes
  • Data Wrangling Using Automation Tools•4 minutes
  • Alteryx for Automation•4 minutes
  • Go to Canvas•1 minute
  • Example Using Alteryx •90 minutes
  • Congratulations! •2 minutes
1 assignment•Total 30 minutes
  • Module 7 Quiz•30 minutes

Instructor

Northeastern University
2 Courses•88 learners

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