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Resume Transcript Auto-Scroll
Author
Barton Poulson
Released
7/27/2016
Get a comprehensive overview of modern data science: the practice of obtaining, exploring, modeling, and interpreting data. Barton Poulson explores disciplines such as programming, statistics, mathematics, machine learning, data analysis, visualization, and big data. He explains why data scientists are now in such demand, and the skills required to succeed in different jobs. He shows how to obtain data from legitimate open-source repositories via web APIs and page scraping, and introduces specific technologies (R, Python, and SQL) and techniques (support vector machines and random forests) for analysis. By the end of the course, you should better understand data science's role in making meaningful insights from the complex and large sets of data all around us.
Topics include:
- 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
3h 6m
Duration
3,275,374
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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