Join Lillian Pierson, P.E. for an in-depth discussion in this video Welcome, part of Python for Data Science Essential Training.
- Hi, I'm Lillian Pierson, and I'm a data scientist, an engineer, and the author of Data Science For Dummies. Welcome to Python for Data Science Essential Training. As you may know, my world revolves around data. Data science, analytics, data visualization, and big data, they're all important parts of the mission-critical task of generating business value from raw data. The coding, analysis, and subject-matter expertise that go into data science make it a pivotal stepping stone on the journey from data to value. I love using Python for data science because it simplifies this complex work to a few human-readable lines of code.
In this course, you're going to learn to use Python to clean data and make predictions based off of it. You're going to get a working knowledge of machine learning as well as data visualization and network analysis. You're even going to get a taste of how to use Python to scrape the internet and capture your own free range data sets. Be prepared to be catapulted into the next level of career success because the things you're about to learn will get you there. Hunker down because here we go.
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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