Update scipy-basic

sub-module -> sub-package (same as SciPy documentation)

chapters/data-analytics/scipy-basic.ipynb

@@ -15,15 +15,15 @@
     "id": "lZMyAdqhL9hY"
    },
    "source": [
-    "[SciPy](https://www.scipy.org/) is the core library for scientific computing in Python. It provides many user-friendly and efficient numerical routines, such as numerical integration, interpolation, optimization, linear algebra, and **statistics**. These routines are composed as task-specific sub-modules in `SciPy`, such as `scipy.cluster` for vector quantization/ Kmeans, `scipy.linalg` for linear algebra routines. All `SciPy` sub-modules depend on `NumPy`, but are mostly independent of each other. \n",
+    "[SciPy](https://www.scipy.org/) is the core library for scientific computing in Python. It provides many user-friendly and efficient numerical routines, such as numerical integration, interpolation, optimization, linear algebra, and **statistics**. These routines are composed as task-specific subpackages in `SciPy`, such as `scipy.cluster` for vector quantization/ Kmeans, `scipy.linalg` for linear algebra routines. All `SciPy` subpackages depend on `NumPy`, but are mostly independent of each other. \n",
     "\n",
     "`scipy.stats` module contains a large number of summary and frequency statistics, probability distributions, correlation functions, statistical tests, kernel density estimation, quasi-Monte Carlo functionality, and so on. \n",
     "\n",
     "In this tutorial, we will cover:\n",
     "* `scipy.stats`: Statistics, Distributions, Statistical Tests and Correlations\n",
     "* Extreme Value Analysis\n",
     "\n",
-    "The standard way of importing NumPy and one SciPy sub-module is:"
+    "The standard way of importing NumPy and one SciPy sub-package is:"
    ]
   },
   {
@@ -938,7 +938,7 @@
     "\\end{align}\n",
     "$\n",
     "\n",
-    "where $\\mu, \\sigma, \\xi$ are respectively GEV's location `loc`, scale `scale`, and shape `c` parameters. Obviously, there are no explicit solutions to these equations when we plugin the sample L-moments. Luckily, we could resort to the function solver `scipy.optimize.fsolve` in the optimization sub-module of SciPy to get numerical solutions."
+    "where $\\mu, \\sigma, \\xi$ are respectively GEV's location `loc`, scale `scale`, and shape `c` parameters. Obviously, there are no explicit solutions to these equations when we plugin the sample L-moments. Luckily, we could resort to the function solver `scipy.optimize.fsolve` in the optimization sub-package of SciPy to get numerical solutions."
    ]
   },
   {
@@ -1326,7 +1326,7 @@
    "source": [
     "## References\n",
     "+ This tutorial was edited based on [Python Statistics Fundamentals](https://realpython.com/python-statistics/), [Scipy Lecture Notes](https://scipy-lectures.org/), [royalosyin's guide to carry out EVA](https://github.com/royalosyin/A-Beginner-Guide-to-Carry-out-Extreme-Value-Analysis-with-Codes-in-Python) and [OpenHydrology's lmoments3 repository](https://github.com/OpenHydrology/lmoments3).\n",
-    "+ Only the `scipy.stats` sub-module is introduced here. If you wish to get a quick glimpse on other sub-modules of SciPy, you could refer to [scipy-lectures](https://scipy-lectures.org/intro/scipy.html)."
+    "+ Only the `scipy.stats` sub-package is introduced here. If you wish to get a quick glimpse on other subpackages of SciPy, you could refer to [scipy-lectures](https://scipy-lectures.org/intro/scipy.html)."
    ]
   }
  ],