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⇱ Enterprise Data Wrangling with SQL and Python | Coursera

Enterprise Data Wrangling with SQL and Python

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Enterprise Data Wrangling with SQL and Python

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

There are 4 modules in this course

Welcome to Enterprise Data Wrangling with SQL and Python. 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

6 videos17 readings1 assignment

6 videos•Total 17 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
17 readings•Total 85 minutes
  • Course Introduction•2 minutes
  • Syllabus - Enterprise Data Wrangling with SQL and Python•10 minutes
  • Academic Integrity•1 minute
  • Data Wrangling Key Questions •2 minutes
  • Data Wrangling Steps•5 minutes
  • Relational Databases•2 minutes
  • Key SQL Concepts•10 minutes
  • Overview of Predefined Functions•2 minutes
  • Single-Row vs. Group Functions•10 minutes
  • Overview of Single-Row Functions•1 minute
  • Common Examples of Single-Row Functions•5 minutes
  • Uses of Single-Row Functions•4 minutes
  • Single-Row Function Example•10 minutes
  • Overview of Group Functions•1 minute
  • Common Examples of Group Functions•4 minutes
  • Uses of Group Functions•6 minutes
  • Group Function Example•10 minutes
1 assignment•Total 45 minutes
  • Assess Your Learning: Introduction to Data Wrangling and Relational Databases•45 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

22 readings1 assignment

22 readings•Total 146 minutes
  • Data Manipulation Overview•2 minutes
  • DML Core Operations•10 minutes
  • INSERT•5 minutes
  • Update•10 minutes
  • Delete•8 minutes
  • TCL Core Operations•1 minute
  • Commit•3 minutes
  • Rollback•5 minutes
  • Summary Table•10 minutes
  • Data Joining Overview•2 minutes
  • Summary Table with Notes•10 minutes
  • Data Joining Types Overview•10 minutes
  • Inner Join Overview•4 minutes
  • Inner Join Example•10 minutes
  • Join Overview•4 minutes
  • Left Join Example•10 minutes
  • Right Join Overview•4 minutes
  • Right Join Example•10 minutes
  • Full Outer Join Overview•4 minutes
  • Full Outer Join Example•10 minutes
  • Cross Join Overview•4 minutes
  • Cross Join Example•10 minutes
1 assignment•Total 45 minutes
  • Assess Your Learning: Managing and Retrieving Data with SQL•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 videos13 readings1 assignment

2 videos•Total 6 minutes
  • Data Profiling•3 minutes
  • Data Profiling Example•3 minutes
13 readings•Total 61 minutes
  • 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 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
  • Data Profiling•3 minutes
1 assignment•Total 15 minutes
  • Assess Your Learning: Data Profiling and Discovery•15 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 videos11 readings1 assignment

2 videos•Total 3 minutes
  • Data Cleansing•1 minute
  • Data Cleansing to Handle Outliers•2 minutes
11 readings•Total 29 minutes
  • Data Cleansing Overview•2 minutes
  • 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
  • Congratulations! •2 minutes
1 assignment•Total 25 minutes
  • Assess Your Learning: Data Cleansing•25 minutes

Instructor

Northeastern University
2 Courses•88 learners

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