ANL252 Python For Data Analytics is a module that will teach you how to use Python programming language to analyze data. This module is designed for students who want to learn how to use Python for data analysis, and who have some prior experience with programming. The module will cover topics such as data wrangling, data visualization, and machine learning. By the end of the module, you will be able to use Python to effectively analyze data. If you are interested in learning how to use Python for data analytics, then this is the module for you.

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In this section, we discuss some assignment activities. These are:

Assignment Activity 1: Differentiate the various aspects of Python programming.

Python is a high-level, interpreted, general-purpose programming language, created on December 3, 1989, by Guido van Rossum, with a design philosophy entitled, “There’s only one way to do it, and that’s why it works.”

It is an elegant and concise language that encourages programmers to think logically and express their ideas clearly. Python has been called the “Swiss Army knife” of programming languages because of its wide range of features. Some of these features include garbage collection, dynamic typing, modularity, extensive standard libraries, and an easy-to-read syntax.

Python is a versatile language that can be used for developing a wide range of applications, from simple scripts to complex web applications. Python is also popular in scientific and numeric computing because of its support for libraries like NumPy and SciPy.

Assignment Activity 2: Discuss how Python manages packages, modules, functions, etc.

Python manages packages, modules, and functions through something called a Project Interpreter. The Project Interpreter is responsible for handling all the work that your code does.

Every time you create a new project, you need to specify which interpreter you want to use for that project. By default, PyCharm uses the same interpreter as the one used in your system path.

Once you have selected an interpreter, you can then add any packages, modules, or functions that are needed for your project.

Packages are collections of modules that you can use in your projects. Modules are pieces of code that perform specific tasks. Functions are everything else 136ƒ python– it just so happens that classes and objects (and thus, instances) are functions.

In order to use a package, module, or function in your code, you first need to import it. Importing is simply the process of making something available in your current namespace.

Assignment Activity 3: Explain the operations on arrays and datasets.

Operations on arrays and datasets are usually performed using NumPy, which is a Python library for scientific computing. NumPy provides an efficient way to store and manipulate data that is often used in scientific and numeric applications.

NumPy arrays are similar to regular Python lists, but they have some important differences. First, NumPy arrays are of a fixed size, meaning that you can’t add or remove elements from them. Second, NumPy arrays are multidimensional, meaning that they can have more than one dimension. Finally, NumPy arrays are homogeneous, meaning that all the elements in an array must be of the same data type.

There are a number of ways to create NumPy arrays. The most common is to use the array() function, which takes a Python list as an argument and returns a NumPy array.

Once you have a NumPy array, you can perform various operations on it, such as indexing, slicing, reshaping, and calculating statistics.

• Indexing is the process of accessing individual elements in an array. This can be done using the square brackets ([]) operator.
• Slicing is the process of extracting a subset of an array. This can be done by specifying two indices, separated by a colon (:), inside the square brackets.
• Reshaping is the process of changing the shape of an array. This can be done using the reshape() function.
• Calculating statistics is the process of calculating various measures of central tendencies, such as the mean, median, and mode. This can be done using the mean(), median(), and mode() functions.

Assignment Activity 4: Design Python programs for performing data analytics.

There are many different Python programs you could use for data analytics. One popular option is the Pandas library, which allows you to easily manipulate and analyze data in a variety of formats.

Another option is the NumPy library, which provides efficient and optimized operations on numerical arrays. This can be particularly useful for speeding up certain data-intensive tasks.

Ultimately, the best Python program for data analytics will depend on your specific needs and preferences. Be sure to explore different options and find one that suits your needs best.

Assignment Activity 5: Employ logic control flows in Python programs.

Logic control flows are a way of organizing code in Python programs. There are three basic types of logic control flow sequence, selection, and iteration.

Sequence control flow means that the code is executed in order, one line at a time. Selection control flow means that the code is executed based on a condition. Iteration control flow means that the code is executed multiple times, usually with some kind of looping construct.

Python provides several different ways to create these three basic types of logic control flows.

• The most common way to create a sequence is with the simple for loop: for an item in sequence: # do something with an item
• The most common way to create a selection is with the if statement: if condition: # do something
• The most common way to create an iteration is with the while loop: while condition: # do something

Assignment Activity 6: Prepare data for analysis using Python programming.

There are many ways to prepare data for analysis using Python programming. One way is to use the Pandas library to read data from a CSV file and then manipulate it as needed. For example, you could use the Pandas library to calculate summary statistics or create new columns based on calculations from other columns.

Another way to prepare data for analysis is to use the NumPy library to convert it into a format that can be used by scientific or numerical functions. For example, you might use NumPy to convert your data into an array so that it can be fed into a machine-learning algorithm.

Ultimately, the best way to prepare your data will depend on what sorts of analyses you plan on doing and what libraries you plan on using. Be sure to explore different options and find one that suits your needs best.

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

There are many different libraries and tools that you can use to help you with this task. Some of the most popular ones include pandas, NumPy, matplotlib, and sci-kit-learn. Each of these has its own strengths and weaknesses, so it’s important to pick the right one for your particular needs.

One of the most important things to do when analyzing data is to explore it first. This means getting a feel for what the data looks like, what kinds of patterns exist within it, and so on. This can be done using a wide variety of techniques, but some of the most popular ones include visual methods like histograms and scatter plots.

Once you have a good understanding of the data, you can start to perform more formal analyses. This might involve fitting models to the data or running statistical tests. Again, there are many different libraries and tools that you can use for this task. Some of the most popular ones include scipy, statsmodels, and seaborn.

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