Data Project Fundamentals: Skills and Techniques for Success

Data Project Fundamentals: Skills and Techniques for Success, available at $64.99, has an average rating of 4.5, with 46 lectures, 1 quizzes, based on 21 reviews, and has 180 subscribers.

You will learn about Gain a comprehensive overview of the skills, thought processes and complexities that accompany a data-related task or project Discover the benefits of data profiling and unit testing Learn the techniques, challenges and approaches undertaken in each of the logical stages of a data project Learn the fundamentals of Structured Query Language with easy-to-follow examples and exercises Learn how applying the techniques in each phase of a data project leads to more robust, successful and maintainable solutions! Work with the course data using Jupyter Notebooks to back up the course concepts Appreciate the fundamentals of data modelling including Dimensional Modelling and Entity Relationship Diagrams This course is ideal for individuals who are Professionals in the early stages of a data career as a data engineer, data scientist or data analyst or Business leaders, business analysts and project managers looking for a fuller, more rounded appreciation of data projects or Software Engineers looking to gain an end-to-end overview of the life-cycle of a data project It is particularly useful for Professionals in the early stages of a data career as a data engineer, data scientist or data analyst or Business leaders, business analysts and project managers looking for a fuller, more rounded appreciation of data projects or Software Engineers looking to gain an end-to-end overview of the life-cycle of a data project.

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Summary

Title: Data Project Fundamentals: Skills and Techniques for Success

Price: $64.99

Average Rating: 4.5

Number of Lectures: 46

Number of Quizzes: 1

Number of Published Lectures: 46

Number of Published Quizzes: 1

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Gain a comprehensive overview of the skills, thought processes and complexities that accompany a data-related task or project
Discover the benefits of data profiling and unit testing
Learn the techniques, challenges and approaches undertaken in each of the logical stages of a data project
Learn the fundamentals of Structured Query Language with easy-to-follow examples and exercises
Learn how applying the techniques in each phase of a data project leads to more robust, successful and maintainable solutions!
Work with the course data using Jupyter Notebooks to back up the course concepts
Appreciate the fundamentals of data modelling including Dimensional Modelling and Entity Relationship Diagrams

Who Should Attend

Professionals in the early stages of a data career as a data engineer, data scientist or data analyst
Business leaders, business analysts and project managers looking for a fuller, more rounded appreciation of data projects
Software Engineers looking to gain an end-to-end overview of the life-cycle of a data project

Target Audiences

Professionals in the early stages of a data career as a data engineer, data scientist or data analyst
Business leaders, business analysts and project managers looking for a fuller, more rounded appreciation of data projects
Software Engineers looking to gain an end-to-end overview of the life-cycle of a data project

This course aims to highlight the key skills that will make you stand out as a rounded Data Professional. You will cover the techniques, challenges and approaches undertaken in each of the logical stages of a data project. You will cover Data Profiling; Data Mapping and Transformation; Development; Data Modelling; Data Solution Design; Software Design; Unit Testing and Troubleshooting. The course aims to follow the natural life-cycle of a project.

These topics will be demonstrated by a mixture of lectures, screencasts, quizzes and exercises. Practical retail data will bring it all to life with Python and Jupyter Notebooks used to illustrate the techniques. Although Jupyter notebooks are used, a running them in the course is that it is not important howcertain techniques are done, but that these techniques are understood and whythey form a key part of the process. So although the hands-on sections will definitely enhance the course experience they are not a prerequisite, and could also be tackled using alternative software.

Ultimately this course aims to fill the gaps between courses focused on specific programming, data analysis skills or data modelling skills. It should allow you to appreciate but also critically question your current data environment. I believe this course offers something different and will make you stand back and think before heading full-steam into a data-related task. This more complete understanding of Data Projects will make you a better, more rounded Data Professional. Enjoy!

Course Curriculum

Chapter 1: Course Introduction: Data Project Fundamentals

Lecture 1: Course Introduction

Lecture 2: Course Overview

Chapter 2: Course Data and Code Download

Lecture 1: Course Data & Code Download

Chapter 3: Introduction to Data Profiling

Lecture 1: Introduction to Data Profiling

Lecture 2: Example: Data Profiling Using Power Query and Google Sheets

Lecture 3: Exercises: Profiling Questions Against the Transactional Data

Lecture 4: Solutions: Profiling Questions Against the Transactional Data

Chapter 4: Setting Up and Using Jupyter Notebooks

Lecture 1: Introduction to Jupyter Notebooks

Lecture 2: Install Anaconda (for Jupyter)

Lecture 3: Jupyter Notebook Basics

Lecture 4: Python Basics

Chapter 5: Further Data Profiling, Data Mapping & Data Transformation

Lecture 1: Further Data Profiling

Lecture 2: Further Data Profiling Using a Jupyter Notebook

Lecture 3: Data Mapping and Transformations

Lecture 4: Exercises: Implement Further Transformations

Lecture 5: Solutions: Implement Further Transformations

Chapter 6: Development

Lecture 1: Structured Query Language (SQL): Joins

Lecture 2: SQL: Set Operators

Lecture 3: SQL: Aggregate Functions and Window Functions

Lecture 4: SQL Joins Using Pandas

Lecture 5: Exercises: SQL Grouping and Window Functions

Lecture 6: Solutions: SQL Grouping and Window Functions

Chapter 7: Data Modelling

Lecture 1: Introduction to Data Modelling

Lecture 2: Exercise: Create a Basket Table

Lecture 3: Solution: Create a Basket Table

Lecture 4: Data Modelling

Chapter 8: Data Solution Design (Data Only)

Lecture 1: Data Solution Design Part 1

Lecture 2: Example: Create the Item Fact Table

Lecture 3: Exercise: Dimension Tables

Lecture 4: Solution: Dimension Tables Exercise

Lecture 5: Data Solution Design Part 2

Lecture 6: Exercises: Star Schema Joins

Lecture 7: Solutions: Star Schema Joins

Lecture 8: Example: Dimension Snapshots

Chapter 9: Software Design

Lecture 1: Program Design Principles

Lecture 2: Example: Structuring Code

Lecture 3: Optional: Data Migration and Historical Data

Chapter 10: Testing and Troubleshooting

Lecture 1: Data Troubleshooting and Unit Testing

Lecture 2: Example: Create Test Data

Lecture 3: Example: Add Unit Tests

Lecture 4: Optional Exercise: Add Further Unit Tests

Lecture 5: Data Validation and Reconciliation

Chapter 11: Implementing Changes

Lecture 1: Implementing Changes

Lecture 2: Exercise: Implement an Offer Dimension

Lecture 3: Solution: Implement an Offer Dimension

Chapter 12: Wrap Up

Lecture 1: Congratulations!

Instructors

Jeremy Fletcher
Data Solution Expert

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3 stars: 2 votes
4 stars: 4 votes
5 stars: 15 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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