Python Course Lecture 9b Review.ipynb

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    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')"
   ]
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   "source": [
    "# Introduction to Scientific Programming in Python\n",
    "\n",
    "## Lecture 9b Review and Discussion"
   ]
  },
  {
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   "source": [
    "# Review of Topics Covered\n",
    "After 13 Hours of Lecture Time and 7 Homework Assignments, What did we learn?"
   ]
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   "source": [
    "## Core Python\n",
    "  - What is Python?\n",
    "  - Built-In Types and Their Methods: int, float, str, bool, tuple, list, dict, set\n",
    "  - Iteration, Loops (For and While), and Branching (if-else)\n",
    "  - Operators: Math (+, -, /, \\*) and Logical (==, !=, >, <, and, or, &, |)\n",
    "  - Mutability and Python's ID storage model\n",
    "  - Ordered vs Labeled Collections\n",
    "  - List Comprehensions and Conditional Assignment\n",
    "  - Importing Packages and Modules\n",
    "  - Function Creation and Functional Programming\n",
    "  - Setting Files and Directories\n",
    "  - Errors and Debugging"
   ]
  },
  {
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   "source": [
    "## Scientific Python Stack\n",
    "  - NumPy Arrays and Matrices\n",
    "  - NumPy Math Functions\n",
    "  - Pandas DataFrames and Tabular Data\n",
    "  - Plotting in Matplotlib and Pandas\n",
    "  - Scipy.stats, .signal, .io, .ndimage, and scikit-image\n",
    "  - Indexing, Slicing, and Masking Operations\n",
    "  - Split-Apply-Combine and Join/Merge Operations\n",
    "  - Data File Formats and Relational Databases\n",
    "  - IPython, Jupyter Notebook, and Interactive Widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Course Goals--Have We Met Them? (1)\n",
    "  - be familiar with core programming concepts and approaches to solving problems.\n",
    "    - Can you select a good data type to hold your data in?\n",
    "    - Do you understand what a function is? What an object is?\n",
    "    - Can you picture in your head the steps you need to do to get from loading data to making a plot?\n",
    "    - Can you use loops and make functions to reduce the amount of redundant code?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Course Goals--Have We Met Them?  (2)\n",
    "  - be familiar with the core tools in python for performing scientific analysis.\n",
    "    - Can you start Python on your computer?\n",
    "    - Can you create scripts or notebooks to save your code?\n",
    "    - Can you import NumPy, Pandas, Matplotlib? Do you understand what they are for?\n",
    "    - Can you load your data?\n",
    "    - Do you know where to look for analysis functions to analyze your data?\n",
    "    - Can you plot your results?"
   ]
  },
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   "source": [
    "## Course Goals--Have We Met Them?  (3)\n",
    "  - be comfortable discovering and using new python packages beyond the core tools.\n",
    "    - Do you know how to find and read documentation?\n",
    "    - Do you know how to install new packages? \n",
    "    - Can you import packages and get functions inside the packages?\n",
    "    - Do you understand the following Python patterns:\n",
    "      - Object Construction from a Class\n",
    "      - Iteration\n",
    "      - Apply(fun)\n",
    "      \n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Programming Tasks for Scientists: What Did We Cover?\n",
    "  - (Data Collection)\n",
    "  - **Data Management**\n",
    "  - **Data Munging**\n",
    "  - Signal Processing\n",
    "  - **Data Visualization**\n",
    "  - Statistical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Class Discussion and Evaluation\n",
    "  - Course Format\n",
    "  - Course Length\n",
    "  - Homework / Final Project\n",
    "  - Topics for Intermediate-Level Course  "
   ]
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   "source": [
    "## Discussion: Course Format (90-min lectures in Jupyter)\n",
    "  - What were the pros and cons of this format?\n",
    "  - Were the Jupyter Notebook Slides helpful?\n",
    "  - Would a more interactive session (less content, but more time spend on small problems in-class) be more effective?\n",
    "  - Pros/Cons of Jupyter Notebook?"
   ]
  },
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   "source": [
    "## Discussion Questions: Course Length\n",
    "  - How did missing a course affect your ability to enjoy and keep up with the course?\n",
    "  - Would shorter courses (4-5 weeks) or workshops (2-3 Full Days) be an improvement here?"
   ]
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55ZdfSKFQ0KRJk+jcuXN06dIl2rRpE126dEnndSpJScXhWe3LP02X4iDLMs2cOZNOnjxJ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e8bbd3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attendance_fig, ax = plt.subplots(ncols=2)\n",
    "\n",
    "class_attendence = [20, 17, 10, 13, 10, 10, 10, 9]; \n",
    "ax[0].plot(class_attendence); \n",
    "ax[0].set_ylim(0, 21); \n",
    "ax[0].set_xlabel('Lecture Num')\n",
    "ax[0].set_ylabel('N Attended') \n",
    "ax[0].set_title('Attendence in Each Class')\n",
    "\n",
    "perc_attended = [1, 1, 3, 6, 3, 8, 8, 4, 6, 3, 6, 4, 8, 1, 5, 8, 4, 7, 6, 8, 1]\n",
    "ax[1].hist(perc_attended, bins=4)\n",
    "ax[1].set_xlabel('N Lectures Attended')\n",
    "ax[1].set_ylabel('Freq')\n",
    "ax[1].set_title('N Lectures Attended\\nby Each Student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OHDmC8+fPY/LkyZgyZQrOnj2LPn36ID4+HuPHj8f58+fRp08fnWOKXv0gb1wG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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e8bc1eeb8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Discussion Questions: Homework\n",
    "  - Number of Homework Assignments: 7\n",
    "  - Was the homework valuable to you?  Which assignments helped the most?\n",
    "  - Did not completing the homework make the next lecture(s) more challenging, or did you not notice an effect?\n",
    "  - How much time did the homework assignments usually take to complete?\n",
    "  - How challenging was the homework?  Were the tutoring sessions useful to you?\n",
    "  - Was the Final Project useful to you?"
   ]
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    "## Discussion Questions: Intermediate Course Topics\n",
    "  - Object-Oriented Programming\n",
    "  - Version Control with Git \n",
    "  - Unit Testing\n",
    "  - Statistical Model Fitting with statsmodels\n",
    "  - Machine Learning with scikit-learn\n",
    "  - Workflow Automation with doit\n",
    "  - Intermediate Python Features (decorators, context managers)\n",
    "  - Parallel Processing and Cluster Computing\n",
    "  - Creating Packages and Sharing your Code \n",
    "  - Data Security and Scientific Data Management Laws in Germany"
   ]
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    "# Thank You for Your Participation!\n",
    "I very much enjoyed teaching this class, and I look forward to improving the course and teaching it again!\n",
    "\n",
    "## Happy Programming, Pythonistas!"
   ]
  }
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