Week 12 - Revision.ipynb

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   "source": [
    "# Week 12: Revision"
   ]
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   "metadata": {},
   "source": [
    "Last few weeks, we have looked at: \n",
    "* Python programming\n",
    "* Reading and writing files \n",
    "* Orbject oriented programming\n",
    "* EDA\n",
    "* Machine learning\n",
    "* Text analysis\n",
    "* Network analysis\n",
    "\n",
    "This week is to look back what we have done. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedual Programming"
   ]
  },
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   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> Keep your habit to use comments. In Python it starts with \"#\". \n",
    "</div>"
   ]
  },
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   "metadata": {},
   "source": [
    "If and else: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "if this: \n",
    "    print('Do something here')\n",
    "elif this: \n",
    "    print('Do something here')\n",
    "else: \n",
    "    print('Do something here')\n",
    "```"
   ]
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   "metadata": {},
   "source": [
    "Counter loop: "
   ]
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   "metadata": {},
   "source": [
    "```python\n",
    "i = 0    # Initiate counter\n",
    "while i < n:     # While loop\n",
    "    print('Do something here')\n",
    "    i += 1   # Increase counter\n",
    "```"
   ]
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   "metadata": {},
   "source": [
    "Event loop:"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "while True: \n",
    "    if stopping_condition: \n",
    "        break\n",
    "    pass # Do something\n",
    "```"
   ]
  },
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   "metadata": {},
   "source": [
    "For loop: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "for i in range(0, n): \n",
    "    print('Do something here')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading (writing) files: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "with open(\"filename\", \"r\") as f: \n",
    "    f.readlines()\n",
    "    f.read()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* \"r\" is read mode. \n",
    "* \"w\" is write mode. \n",
    "* \"a\" is append mode. "
   ]
  },
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   "metadata": {},
   "source": [
    "## Object-oriented Programming"
   ]
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   "source": [
    "Everything in Python is an object. We create class to capture the attribute of objects. "
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   "metadata": {},
   "source": [
    "```python\n",
    "class ClassName: \n",
    "    def __init__(self, args): \n",
    "        self.attribute = args \n",
    "        \n",
    "    def instance_methods_here(self): \n",
    "        print('Do something here')\n",
    "    \n",
    "    def static_methods_here(other): \n",
    "        print('Do something here')\n",
    "```"
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   "metadata": {},
   "source": [
    "Subclass: "
   ]
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   "metadata": {},
   "source": [
    "```python\n",
    "class ChildClass(ClassName): \n",
    "    # ClassName is the super class\n",
    "    def __init__(self, args): \n",
    "        ClassName.__init__(self)\n",
    "        self.attribute = args \n",
    "        \n",
    "    def instance_methods_here(self): \n",
    "        print('Do something here')\n",
    "\n",
    "    def static_methods_here(other): \n",
    "        print('Do something here')\n",
    "```"
   ]
  },
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   "metadata": {},
   "source": [
    "## Data Science"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<img src=\"fig/DataScProcess.png\" width=\"450\"/>"
   ]
  },
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   "metadata": {},
   "source": [
    "## Text Analytics"
   ]
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   "metadata": {},
   "source": [
    "### General process\n",
    "\n",
    "1. Import data\n",
    "2. Tokenisation - Converting words or characters into individual tokens for analysis.\n",
    "3. Preprocessing\n",
    "    * Stopping word removal\n",
    "    * Stemming/ Lemmatisation\n",
    "    * n-grams\n",
    "4. Generate the bag-of-words/ text-document matrix\n",
    "5. Train our model (Bayesian/ LDA)\n",
    "6. Report our results"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Analytics"
   ]
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   "source": [
    "* __Node__ represents the data, __link__ represents the relation\n",
    "* Quantities\n",
    "    * __Degree__ The number of links connected to a node.\n",
    "    * __Distance__ We treat each link has length .\n",
    "    * __Diameter__ This is the longest shortest path from a node to another in one network/ graph.\n",
    "    * __Complete__ graph All nodes are directly linked one another.\n",
    "    * __Path__ All viable sequences between two nodes.\n",
    "    * __Cycle__ A composite path that will run back to the same node without other nodes bypassed twice (or more).\n",
    "    * __Closeness__ This is to measure how near are all nodes. For example, if all nodes are directly linked, the network is obviously closed together.\n",
    "    * __Betweenness__ Imagine the links have flow inside them. This measures how often the link is occupied by measure the number of shortest paths passing through the link.\n",
    "    * __Assortativity__ This is to find out if people are prefered to attached to others. This metric measures the degrees of each node and measure how they correlated with their neighbours.\n",
    "    * __PageRank__ Famous for the ranking search results by Google. One intuition is that imagine a person randomly pass through different pages, linked by hyperlinks. The page's popularity is measured by how many in-links to the page itself."
   ]
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   "cell_type": "markdown",
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   "source": [
    "### General process\n",
    "\n",
    "1. Import data (optional)\n",
    "2. Generate nodes and links\n",
    "3. Visualise\n",
    "4. Generate quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ending"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This course provides an idea of what data science is about. Meanwhile we have not touched all the parts in the course, you can look back to see where you have not finished later. The world of machine learning is always expanding and make sure to touch base with new technologies that is not covered in these materials. "
   ]
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