ANL252 Python for Data Analytics assignment sample SUSS Singapore

ANL252 Python for Data Analytics is a course that aims to teach students the basics of data analytics by using programming in Python. The idea behind this course is twofold – firstly, it will equip Business Analytics students with an important skill-set and knowledge needed in their field; secondly, it also focuses on breadth rather than depth so as not to overwhelm those who have no prior exposure or understanding of coding.

At the end of each module you will be able to do more complex tools like manipulating large datasets and performing visualizations with existing libraries such as Matplotlib!

TOA,TMA, GBA Assignment sample of Python for Data Analytics module SUSS Singapore

 At the end of this course, Singaporean students will be able to learn Python for Data Analytics module with the help of the following learning outcomes:

1.Differentiate the various aspects of Python programming.

Python is an interpreted, cross-platform, object-oriented programming language. It has classes and a strong emphasis on readability which makes it beginner friendly. The syntax is generally considered less cluttered than C++ and Java yet as powerful as Perl or Scheme. There are many applications written in Python such as Google’s search engine and YouTube video site.

Here are the three main things that differentiate Python programming from other languages:

1) Syntax — Being true to its initial design principle of being easy to read, Python uses fewer symbols and punctuation marks than more traditional languages like Java or C++. Variables are prefixed with a single letter (e.g., myVariable). Indentation is used to recognize logical code blocks.

2) Dynamic Typing — Because Python does not require variables to be declared before being used, they do not have to specify the data type of a variable when it is created. This language is called dynamically-typed. Some would consider this a weakness while others prefer the flexibility afforded by dynamic typing.

3) Object-Oriented Programming (OOP)— OOP was intended as an extension and improvement on structured programming concepts in general and specifically on Structured Query Language (SQL). It provides a framework for creating and managing complex systems from modular reusable components called objects that communicate with one another through messages rather than function calls or long chains of procedures.

Read also: Take help of Python Assignment Help experts from Singapore Now

2.Discuss how Python manages packages, modules, functions, etc.

Python manages modules, packages, functions, methods, classes and all other sorts of code pieces in the same way – by assigning them to specific namespaces. To understand it better try to imagine Python as a simple operating system.

Modules are like directories and files combined together. And namespaces are paths or folders on this virtual file system which you navigate by your given location (relative or absolute). All these things might be predefined at the time of module import but they can also be defined dynamically via function scope later on even for mod_in imports themselves.

3.Explain the operations on arrays and datasets.

Arrays and datasets are fundamentally the same, with a few noteworthy differences.

• Arrays: Arrays are list of variables or values. Elements in an array are placed into a single row and column designated by square brackets [].
• Datasets: A dataset is another word for a matrix (a set of related vectors). The difference between arrays and datasets is that datasets allows each column to hold any type of data while arrays require all columns to be the same data type. For this reason, if you want one dynamic variable that changes according to which row it occupies, you must place it in the first column on your excel spreadsheet since different rows signify different pieces of data for an array.

Datasets can also be much larger than arrays because they do not impose the same restrictions on their rows and columns.

4.Design Python programmes for performing data analytics.

Here’s some Python code for performing data analytics.

This python script will calculate the mean, median, mode and standard deviation of a given list of numbers. The ‘list’ parameter is the input in this case as it can be any type (string, integer or float). It returns these four statistical values to be used by data scientists in beginning their analysis of a dataset.

“””Data Analytics Script”””

def newList():

tmpList = {} #creates empty dictionary to store all values-resets excess variables using {}s

Loop through the parameter taken from input and assigns numerical value according to type into tmpList variable in respective key (e.g ‘list[0]’ if the input is integer).

for val in list:

tmpList[_val] = float(_val) #converts a variable into a float from string. Here, you can adjust the type of _val to cater for the needs of your own machine learning algorithms.

return tmpList

returns the values back to the calling function from where user can then extract for his own specifications.

5.Employ logic control flows in Python programmes.

When programming with Python, each control flow is made with a recommended style.

Python recommends the For loop for iterate for a known sequence to be worked on and it has different styles to work on that sequence. At first there is the typical while which loops until an expression evaluates to True, then assigns each element of the sequence in frames increments (defaulting to 1 if not specified). It also offers other iterations which consists of nested loops like in this example:

x = [1, 2]

For x_i In x:

Print(‘The square is ‘ + int(x_i) * int(x_i))

This program will print the following output: The square is 1 The square is 4

There is also the For loop which is specified in a different way. It takes an iterator and the first element of the sequence to be assigned into each coefficient after that iterator accordingly (i.e it assigns both x and y variables instead of incrementing both at once).

6.Prepare data for analysis using Python programming.

You can start by installing Jupyter and Python on your computer.

Next, launch the Jupyter application in a terminal using command line. To do so, open a new shell (or terminal window) and type “jupyter notebook”. Once you are in the IDE simply chose Python as Add language then download for next time.

You will then open up a browser window with an ipynb file pre-populated with some standard python code that you can use to begin working in python easily. Finally, we recommend opening up an example dataset from this website using the menu at top right or here is titled sample text import pandas as pd import matplotlib .

7. Analyse data using appropriate tools and techniques with Python programming.

There are various tools that can be used for data analysis in Python, including but not limited to NumPy, pandas, Seaborn and Blaze. Tools like the matplotlib package provide tons of possibilities for visualizing those graphs. Some other really helpful packages include Dask, which offers distributed arrays and parallel computation primitives; TensorFlow which is Google’s open source library for neural networks or Magenta Theano + Keras + Lasagne (which are all combo libraries), aka state-of-the-art deep learning frameworks. For more information on these topics we recommend checking out the IPython guides and tutorials here.

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