- [Voiceover] Now that you've got a better idea…of what data science is and how it works,…you're probably looking for some of the next steps…you can take to develop your skills…and to put them into action.…The single best thing I can recommend at this point…is to get involved.…There's so many ways that you can do this.…For instance, you can go to data science events.…There are major events that happen,…such as the Strata Hadoop World.…These are put on by O'Reilly Media.…They happen several times a year…in several places around the globe.…The same is true for Predictive Analytics World:…somewhere between eight and 10 meetings a year…around the world.…
There are smaller conferences, like Extract,…which is put on by import.io,…and Visualized, which is an event for data designers,…visualizers, story tellers, and technologists.…Or there are specialized events, like PyData and useR!…Those are events for the Python and R…data science communities.…Of course, there's a number of blogs that are available.…Probably the biggest one is DataTau.…
Author
Released
7/27/2016- Assess the skills required for a career in data science.
- Evaluate different sources of data, including metrics and APIs.
- Explore data through graphs and statistics.
- Discover how data scientists use programming languages such as R, Python, and SQL.
- Assess the role of mathematics, such as algebra, in data science.
- Assess the role of applied statistics, such as confidence intervals, in data science.
- Assess the role of machine learning, such as artificial neural networks, in data science.
- Define the components of effective data visualization.
Skill Level Beginner
Duration
Views
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Introduction
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Welcome58s
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Exercise files34s
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1. What Is Data Science?
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Demand3m 54s
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Venn diagram4m 2s
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Pipeline4m 43s
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Roles3m 14s
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Team2m 14s
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2. Fields of Study
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Big data3m 20s
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Programming2m 26s
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Statistics1m 57s
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3. Ethics
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Ethical issues2m 39s
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4. Data Sources
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Metrics3m 43s
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Existing data4m 36s
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APIs4m 38s
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Scraping2m 16s
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Creating data3m 3s
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5. Data Exploration
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Exploratory graphs4m 32s
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Exploratory statistics4m 26s
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6. Programming
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Spreadsheets3m 49s
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R5m 18s
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Python4m 51s
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SQL3m 44s
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Web formats3m 53s
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7. Mathematics
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Algebra6m 22s
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Systems of equations5m 11s
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Calculus9m 50s
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Big O5m 8s
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Bayes probability8m 15s
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8. Applied Statistics
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Hypothesis6m 23s
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Confidence5m 42s
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Problems5m 30s
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Validating3m 35s
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9. Machine Learning
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Decision trees5m 22s
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Ensembles5m 15s
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k-nearest neighbors (kNN)5m 26s
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Naive Bayes classifiers5m 16s
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Artificial neural networks5m 43s
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10. Communicating
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Interpretability5m 50s
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Actionable insights4m 40s
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Reproducible research3m 27s
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Conclusion
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Next steps2m 17s
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Video: Next steps