Join Lillian Pierson, P.E. for an in-depth discussion in this video Next steps, part of Python for Data Science Essential Training.
- Well congratulations, you've made it.…At this point you have all you need to start…transforming raw data into tremendous value.…In this course you've learned how to us Python…to mung data and make predictions.…You've also learned how to find, extract…and visually communicate quantitative insights…that are hidden inside even the most simple data sets.…You know how to analyze networks and even make your…own data sets by scraping the web.…Now that you've made it to this point it's time to…get out there and start exploring your data.…While you're at it follow me on my data science journeys…around the world on Instagram and Twitter…my handle is @BigDataGal.…
Be sure to say hi and I hope to see you there.…
AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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