Fundamentals of Data Ingestion with Python

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Miyuki Takao

1:13:21

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1 - Introduction

1 - CourseFiles.zip

1 - Introduction.mp4

01:41

2 - Coderpad.mp4

01:04

2 - Data Ingestion

3 - Overview of data scientists work.mp4

01:29

4 - Sources of data & types of data.mp4

03:06

5 - Data pipleline & data lake.mp4

02:08

3 - Reading Files

6 - CSV and XML.mp4

07:17

7 - Working in Parquet Avro and ORC.mp4

01:19

8 - Unstructured text and JSON.mp4

05:17

4 - Calling APIs

9 - Working with JSON.mp4

01:58

10 - Making HTTP calls.mp4

02:00

11 - Processing eventbased data.mp4

02:06

5 - Web Scraping

12 - Find API.mp4

00:45

13 - Working with Beautiful Soup.mp4

02:19

14 - Working with Scrapy.mp4

02:33

15 - Selenium and other considerations.mp4

02:50

6 - Schemas

16 - What are schemas.mp4

01:32

17 - Working with ontologies.mp4

01:08

18 - Schema validations.mp4

04:24

7 - Databases

19 - Types of databases.mp4

01:34

20 - Hosted and cost of ops.mp4

01:02

21 - Working with relational databases.mp4

03:22

22 - Working with key or value databases.mp4

02:15

23 - Document databases and Graph databases.mp4

04:44

8 - Troubleshooting Data

24 - Troubleshooting.mp4

05:56

25 - Finding Outliers.mp4

05:12

9 - Data KPIs and Process

26 - Design your data.mp4

00:56

27 - KPIs.mp4

01:06

28 - Monitoring.mp4

01:34

10 - Conclusion

29 - Conclusion.mp4

00:44

Description

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

What You'll 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 is this for?

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.

What You Need to Know?

Basic understanding of Python programming concepts

More details

Description
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.
Who this course is for:
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.

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I am an experienced programmer, computer scientist, and physicist specializing in object-oriented programming, software engineering and computer science pedagogy. I received a Ph.D. in experimental solid state physics from M.I.T. in 1994. I have designed and taught innovative introductory “objects-first” courses leveraging the power of interactivity to teach fundamental C.S. concepts and skills.

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