Fundamentals of Data Ingestion with Python

Fundamentals of Data Ingestion with Python, available at $54.99, has an average rating of 4.5, with 29 lectures, based on 4 reviews, and has 965 subscribers.

You will learn about Learn How to Use Python Tools and Techniques to Get The Relevant, High-Quality Data You Need Explore the unique attributes of diverse data types and their relevance to the work of data scientists. Investigate various data serialization formats and their practical applications within Python. Define APIs and elucidate their utilization in Python, covering HTTP calls, JSON interpretation, and message queue integration. Unveil the concept of web scraping and offer insights into its methodologies and implementations. Clarify the significance of schemas, detailing their defining characteristics and their impact on operational procedures. Examine different types of databases, categorizing them based on their distinctive features. This course is ideal for individuals who are Data scientists looking to enhance their proficiency in acquiring and cleaning diverse datasets efficiently. or Data analysts transitioning into data science roles who need to expand their knowledge of data preparation. or Beginners in data science seeking a solid foundation in data handling techniques. or Professionals working with data who wish to improve their understanding of Python tools and techniques for data manipulation. It is particularly useful for Data scientists looking to enhance their proficiency in acquiring and cleaning diverse datasets efficiently. or Data analysts transitioning into data science roles who need to expand their knowledge of data preparation. or Beginners in data science seeking a solid foundation in data handling techniques. or Professionals working with data who wish to improve their understanding of Python tools and techniques for data manipulation.

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Summary

Title: Fundamentals of Data Ingestion with Python

Price: $54.99

Average Rating: 4.5

Number of Lectures: 29

Number of Published Lectures: 29

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn How to Use Python Tools and Techniques to Get The Relevant, High-Quality Data You Need

Explore the unique attributes of diverse data types and their relevance to the work of data scientists.

Investigate various data serialization formats and their practical applications within Python.

Define APIs and elucidate their utilization in Python, covering HTTP calls, JSON interpretation, and message queue integration.

Unveil the concept of web scraping and offer insights into its methodologies and implementations.

Clarify the significance of schemas, detailing their defining characteristics and their impact on operational procedures.

Examine different types of databases, categorizing them based on their distinctive features.

Who Should Attend

Data scientists looking to enhance their proficiency in acquiring and cleaning diverse datasets efficiently.

Data analysts transitioning into data science roles who need to expand their knowledge of data preparation.

Beginners in data science seeking a solid foundation in data handling techniques.

Professionals working with data who wish to improve their understanding of Python tools and techniques for data manipulation.

Target Audiences

Data scientists looking to enhance their proficiency in acquiring and cleaning diverse datasets efficiently.

Data analysts transitioning into data science roles who need to expand their knowledge of data preparation.

Beginners in data science seeking a solid foundation in data handling techniques.

Professionals working with data who wish to improve their understanding of Python tools and techniques for data manipulation.

In the realm of data science, acquiring and preparing data is often the most time-consuming aspect of any project. This comprehensive course equips you with essential Python tools and techniques to streamline the process of obtaining and refining high-quality data for your algorithms.

Throughout this course, you’ll delve into various aspects of data acquisition and cleaning, gaining hands-on experience with diverse data formats and sources. From parsing CSV, XML, and JSON files to leveraging APIs and understanding the nuances of web scraping (while emphasizing its judicious use), you’ll master the art of data retrieval.

Moreover, you’ll explore the crucial steps of data validation and cleaning, ensuring that your datasets are free from inconsistencies and errors that could compromise analysis outcomes. Through practical exercises and real-world examples, you’ll learn how to implement effective strategies for data quality assurance.

Furthermore, this course delves into the establishment and monitoring of key performance indicators (KPIs) tailored to your data pipeline. By defining and tracking relevant metrics, you’ll gain invaluable insights into the health and efficiency of your data processes, enabling you to make informed decisions and optimize performance.

Whether you’re a budding data scientist seeking foundational skills or a seasoned professional aiming to enhance your data management prowess, this course provides a comprehensive toolkit to navigate the intricacies of data acquisition and cleaning in Python effectively.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Coderpad

Chapter 2: Data Ingestion

Lecture 1: Overview of data scientists work

Lecture 2: Sources of data & types of data

Lecture 3: Data pipleline & data lake

Chapter 3: Reading Files

Lecture 1: CSV and XML

Lecture 2: Working in Parquet, Avro, and ORC

Lecture 3: Unstructured text and JSON

Chapter 4: Calling APIs

Lecture 1: Working with JSON

Lecture 2: Making HTTP calls

Lecture 3: Processing event-based data

Chapter 5: Web Scraping

Lecture 1: Find API

Lecture 2: Working with Beautiful Soup

Lecture 3: Working with Scrapy

Lecture 4: Selenium and other considerations

Chapter 6: Schemas

Lecture 1: What are schemas?

Lecture 2: Working with ontologies

Lecture 3: Schema validations

Chapter 7: Databases

Lecture 1: Types of databases

Lecture 2: Hosted and cost of ops

Lecture 3: Working with relational databases

Lecture 4: Working with key or value databases

Lecture 5: Document databases and Graph databases

Chapter 8: Troubleshooting Data

Lecture 1: Troubleshooting

Lecture 2: Finding Outliers

Chapter 9: Data KPIs and Process

Lecture 1: Design your data

Lecture 2: KPIs

Lecture 3: Monitoring

Chapter 10: Conclusion

Lecture 1: Conclusion

Instructors

Miyuki Takao
Programmer

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Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!