I believe I can fly (Extract London 2015)

•

3 likes•1,967 views

The document discusses the process of data science. It begins by defining the typical steps in a data science project as identifying a problem/business question, collecting and cleaning data, performing exploratory data analysis, using algorithms and machine learning, reporting answers/minimum viable products, and getting feedback to review results. It then lists "inconvenient truths" about data science, such as data never being clean and most time being spent on preparation. Finally, it provides an example of using import.io and MonkeyLearn tools for text analysis.

Technology

I believe I can fly
Easy Data Science
Ignacio Elola
@ignacio_elola

summary
data science
tools and data
example: text analysis with import.io and MonkeyLearn

what we mean when we talk about data
science?

what we mean when we talk about data
science?
Problem to solve /
Business question

what we mean when we talk about data
science?
Problem to solve /
Business question
Data Collection & Data
cleaning

what we mean when we talk about data
science?
Problem to solve /
Business question
Data Collection & Data
cleaning
EDA

what we mean when we talk about data
science?
Problem to solve /
Business question
Data Collection & Data
cleaning
EDAAlgorithms & ML

what we mean when we talk about data
science?
Problem to solve /
Business question
Data Collection & Data
cleaning
EDAAlgorithms & ML
Reporting: Answer /
MVP

The Inconvenient Truth About Data Science
1. Data is never clean.
2. You will spend most of your time cleaning and preparing data.
3. 95% of tasks do not require deep learning.
4. In 90% of cases generalized linear regression will do the trick.
5. Big Data is just a tool.
6. You should embrace the Bayesian approach.
7. No one cares how you did it.
8. Academia and business are two different worlds.
9. Presentation is key - be a master of Power Point.
10. All models are false, but some are useful.
11. There is no fully automated Data Science. You need to get your hands dirty.
Kamil Bartocha (http://www.kbartocha.com/)

what we mean when we talk about data
science?
data -> action

Example: doing data science with
import.io and MonkeyLearn

Example: doing data science with
import.io and MonkeyLearn
https://github.com/ignacioelola/web-text-analyzer

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As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.

Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack

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“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...

Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/ Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit. In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing. van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.

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Neo4j

Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.

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I believe I can fly (Extract London 2015)

1. I believe I can fly Easy Data Science Ignacio Elola @ignacio_elola

2. summary data science tools and data example: text analysis with import.io and MonkeyLearn

3. what we mean when we talk about data science?

4. what we mean when we talk about data science? Problem to solve / Business question

5. what we mean when we talk about data science? Problem to solve / Business question Data Collection & Data cleaning

6. what we mean when we talk about data science? Problem to solve / Business question Data Collection & Data cleaning EDA

7. what we mean when we talk about data science? Problem to solve / Business question Data Collection & Data cleaning EDAAlgorithms & ML

8. what we mean when we talk about data science? Problem to solve / Business question Data Collection & Data cleaning EDAAlgorithms & ML Reporting: Answer / MVP

9. what we mean when we talk about data science? Problem to solve / Business question Data Collection & Data cleaning EDAAlgorithms & ML Reporting: Answer / MVP Get feedback / Review

10. what we mean when we talk about data science? Problem to solve / Business question Data Collection & Data cleaning EDAAlgorithms & ML Reporting: Answer / MVP Get feedback / Review

11. what we mean when we talk about data science? Problem to solve / Business question Data Collection & Data cleaning EDAAlgorithms & ML Reporting: Answer / MVP Get feedback / Review

12. The Inconvenient Truth About Data Science 1. Data is never clean. 2. You will spend most of your time cleaning and preparing data. 3. 95% of tasks do not require deep learning. 4. In 90% of cases generalized linear regression will do the trick. 5. Big Data is just a tool. 6. You should embrace the Bayesian approach. 7. No one cares how you did it. 8. Academia and business are two different worlds. 9. Presentation is key - be a master of Power Point. 10. All models are false, but some are useful. 11. There is no fully automated Data Science. You need to get your hands dirty. Kamil Bartocha (http://www.kbartocha.com/)

13. what we mean when we talk about data science? data -> action

14. how?

15. tools

16. tools

17. tools

18. tools

19. tools

20. tools

21. tools

22. why import

23. why import

24. why import