Data Visualization with Pickle, Streamlit and Matplotlib | Data Analytics | Community Webinar
Learn how to convert data into dashboards using Pickle, Streamlit, and Matplotlib
Students typically create their portfolios out of simple, clean datasets. These projects are great for learning the basics, but they allow you to circumvent the most essential data science skills. Data on the job requires extensive cleaning, exploration, and feature engineering to become usable for your models. Projects in the professional space will also require steps beyond the initial creation and evaluation of a model.
This hands-on skill-building webinar is for starting and intermediate data science students with a basic knowledge of Python. By the end of this session, you will know:
- Where and how to look for your data
- Some basic data engineering techniques to keep in mind
- How to use pandas to load, clean, and engineer your datasets
- How to use Matplotlib to plot and explore your data
- How to use Scikitlearn, and Tensorflow to model and evaluate your data
- How to use Pickle, Streamlit, and Matplotlib to turn your data into an interactive dashboard
Table of Contents:
00:00 What is data science and data science pipeline
11:00 Problem
20:00 Data
35:02 Context
48:05 Exploration
55:10 Feature Engineering
1:02:40 Preprocessing
1:08:12 Modeling
1:14:07 Optimization
1:15:53 Evaluation and Validation
1:20:38 Dashboarding
1:23:17 Conclusion
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GitHub Repo: https://github.com/AlexanderBaker44/DS_Dojo_Data_to_Dash/tree/main/datascience_dojo
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For more captivating community talks featuring renowned speakers, check out this playlist: https://youtube.com/playlist?list=PL8eNk_zTBST-EBv2LDSW9Wx_V4Gy5OPFT
For further tutorials on advanced machine learning, check out this exclusive playlist: https://youtube.com/playlist?list=PL8eNk_zTBST_SS_czCz6Do1yrUowhKBHI
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At Data Science Dojo, we believe data science is for everyone. Our data science trainings have been attended by more than 10,000 employees from over 2,500 companies globally, including many leaders in tech like Microsoft,
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Chapters (12)
What is data science and data science pipeline
11:00
Problem
20:00
Data
35:02
Context
48:05
Exploration
55:10
Feature Engineering
1:02:40
Preprocessing
1:08:12
Modeling
1:14:07
Optimization
1:15:53
Evaluation and Validation
1:20:38
Dashboarding
1:23:17
Conclusion
🎓
Tutor Explanation
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