lecture_6.ipynb

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   "source": [
    "# Class 6: The Bigger Picture"
   ]
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   "source": [
    "## Guest speaker"
   ]
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   "source": [
    "### [Joshua Kravitz](https://joshuakravitz.com/)\n",
    "\n",
    "> Joshua is the Head of Technology and Data on the U.S. Senate Committee on Appropriations, overseeing efforts to streamline internal processes for improved efficiency and accuracy. Previously, he was a TechCongress Policy Fellow on the House Oversight Committee focused on IT modernization, and Deputy Data Director on Jon Ossoff's campaign for U.S. Senate. Joshua holds a B.S. in computer science and M.S. in statistics, both from Stanford University."
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   "source": [
    "#### Questions?"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "## [Final Project peer grading](https://python-public-policy.afeld.me/en/{{school_slug}}/final_project/peer_grading.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Ask Me Anything (AMA)\n",
    "\n",
    "Have slides on \"Python beyond data analysis\" as backup, but would rather talk about what you want to hear about."
   ]
  },
  {
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   "source": [
    "## Python beyond data analysis\n",
    "\n",
    "We've been focusing on using Python and pandas for data analysis. What else is Python used for?"
   ]
  },
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   "source": [
    "### Data engineering\n",
    "\n",
    "- Automation / recurring processes\n",
    "- Copying/moving/processing/publishing data, especially Big Data\n",
    "- Monitoring/alerting"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "### Web development\n",
    "\n",
    "- Building web sites that are interactive (more than just content)\n",
    "- Forms\n",
    "- Presenting data\n",
    "- Workflows, such as:\n",
    "   - Signing up for things\n",
    "   - Paying for things"
   ]
  },
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   "source": [
    "### Machine learning\n",
    "\n",
    "- Statistics, but _fancy_\n",
    "- Building models\n",
    "   - [Examples](https://tfhub.dev/)\n",
    "- Finding patterns\n",
    "- Recommendations\n",
    "- Detection"
   ]
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   "source": [
    "When people say \"artificial intelligence,\" they usually mean \"machine learning.\""
   ]
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   "source": [
    "![Diagram showing what type of machine learning may be useful, if at all](https://mitsloan.mit.edu/sites/default/files/2021-04/machine-learning-infographic_2.jpg)\n",
    "\n",
    "[Source, with more thorough explanation](https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained)"
   ]
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   "source": [
    "#### The process\n",
    "\n",
    "_High-level_\n",
    "\n",
    "1. Create a model\n",
    "   1. Gather a bunch of data for training\n",
    "   1. If supervised machine learning, label it (give it the right answers)\n",
    "   1. Segment into training and test data\n",
    "   1. Train the model against the training dataset (have it identify patterns)\n",
    "   1. Test the model against the test dataset\n",
    "1. Run against new data\n",
    "1. If reinforcement learning, model refines itself"
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   "source": [
    "You have a head start: [The fundamentals](https://python-public-policy.afeld.me/en/{{school_slug}}/syllabus.html#learning-objectives) are applicable anywhere you're using code."
   ]
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    "tags": [
     "nyu-only"
    ]
   },
   "source": [
    "## Post-{{coding_env_name}}\n",
    "\n",
    "- {{coding_env_name}} instance (and your copy of files) will be deleted\n",
    "    - Download things you want to keep, particularly edited notebooks\n",
    "- Class materials will remain available on [the site](https://python-public-policy.afeld.me/en/{{school_slug}}/) and [through GitHub](https://github.com/afeld/python-public-policy/tree/{{school_slug}})\n",
    "- [Running notebooks elsewhere](https://python-public-policy.afeld.me/en/{{school_slug}}/resources.html#jupyter-outside-this-course)"
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   "source": [
    "## [Course evaluations](https://coursefeedback.nyu.edu/nyu/)\n",
    "\n",
    "They are:\n",
    "\n",
    "- Totally anonymous\n",
    "- Not visible to me until grades are released\n",
    "- A big help. Some things I took from the past:\n",
    "    - Making assignments more rigorous\n",
    "    - Students are hungry for more\n",
    "    - People like the in-class exercises\n",
    "\n",
    "[More info.](https://www.nyu.edu/students/student-information-and-resources/registration-records-and-graduation/final-exams-and-course-evaluations/course-evaluation.html) Please complete now, if you haven't already."
   ]
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    "Thanks to the {{assistant_name}}!"
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   "source": [
    "## Thank you!\n",
    "\n",
    "Keep in touch."
   ]
  }
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