Enterprise Data Wrangling with SQL and Python

Enterprise Data Wrangling with SQL and Python

Instructor: Tareq Nasralah

Access provided by Lockheed Martin

4 modules

Gain insight into a topic and learn the fundamentals.

8 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

8 hours to complete

Flexible schedule

Learn at your own pace

Details to know

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Assessments

4 assignments

Taught in English

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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 videosTotal 17 minutes

Meet Your Faculty2 minutes
Relational Database Data Structure3 minutes
Unique Values 3 minutes
Constraints5 minutes
Working with Text Values2 minutes
TIMESTAMP 3 minutes

17 readingsTotal 85 minutes

Course Introduction2 minutes
Syllabus - Enterprise Data Wrangling with SQL and Python10 minutes
Academic Integrity1 minute
Data Wrangling Key Questions 2 minutes
Data Wrangling Steps5 minutes
Relational Databases2 minutes
Key SQL Concepts10 minutes
Overview of Predefined Functions2 minutes
Single-Row vs. Group Functions10 minutes
Overview of Single-Row Functions1 minute
Common Examples of Single-Row Functions5 minutes
Uses of Single-Row Functions4 minutes
Single-Row Function Example10 minutes
Overview of Group Functions1 minute
Common Examples of Group Functions4 minutes
Uses of Group Functions6 minutes
Group Function Example10 minutes

1 assignmentTotal 45 minutes

Assess Your Learning: Introduction to Data Wrangling and Relational Databases45 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 readingsTotal 146 minutes

Data Manipulation Overview2 minutes
DML Core Operations10 minutes
INSERT5 minutes
Update10 minutes
Delete8 minutes
TCL Core Operations1 minute
Commit3 minutes
Rollback5 minutes
Summary Table10 minutes
Data Joining Overview2 minutes
Summary Table with Notes10 minutes
Data Joining Types Overview10 minutes
Inner Join Overview4 minutes
Inner Join Example10 minutes
Join Overview4 minutes
Left Join Example10 minutes
Right Join Overview4 minutes
Right Join Example10 minutes
Full Outer Join Overview4 minutes
Full Outer Join Example10 minutes
Cross Join Overview4 minutes
Cross Join Example10 minutes

1 assignmentTotal 45 minutes

Assess Your Learning: Managing and Retrieving Data with SQL45 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 videosTotal 6 minutes

Data Profiling3 minutes
Data Profiling Example3 minutes

13 readingsTotal 61 minutes

Data Profiling Overview10 minutes
Discovering Data Structure Overview2 minutes
Rows and Columns7 minutes
Data Type7 minutes
Non-Null Entries7 minutes
Discovering Data Content Overview3 minutes
Summary Statistics4 minutes
Descriptive Statistics4 minutes
Frequency Distribution4 minutes
Missing Values3 minutes
Duplicate Data3 minutes
Incorrect or Ambiguous Data4 minutes
Data Profiling3 minutes

1 assignmentTotal 15 minutes

Assess Your Learning: Data Profiling and Discovery15 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.