DSC551 Programming For Data Science UITM Assignment Example Malaysia

The data science course is designed for those who have an interest in the field of analytics and want to learn more about how it works. You’ll be able to start with one or two topics, but eventually you will need some experience before entering this profession – so make sure your resume lists all relevant work underling skills!

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Assignment Brief 1: Apply Python and R programming to analyze both numerical and text-based data.

Python and R are both versatile programming languages that can be used for a variety of data analysis tasks. Python is particularly well-suited for working with numerical data, while R is well-suited for working with text-based data. However, both languages can be used to analyze both types of data.

In general, Python is a more straightforward language to learn than R, but R has more features and flexibility. For most data analysis tasks, either language will work well. However, for more complex tasks or specific types of analyses, one language may be better suited than the other.

Python has become increasingly popular as a tool for data analysis. Much of this is due to the availability of powerful libraries, such as SciPy and Pandas, that make it easy to quickly carry out complex analyses on datasets. These libraries are open-source and freely available under the BSD license. They are free to download and use (subject to terms and conditions), and many Python distributions, such as Anaconda , come with these libraries pre-installed.

Python is also a general-purpose programming language that can be used for a variety of purposes beyond data analysis (e.g., web development and desktop GUIs). It is free to download and use (subject to terms and conditions) and open-source .

R is a more mature language than Python that has been in use for over 20 years. It was designed specifically with data analysis in mind, which means it comes with an extensive suite of built-in statistical and visualisation tools.

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Assignment Brief 2: Construct simple computer scripts using structured approach

A computer script is a series of commands that can be executed to achieve a desired outcome. Scripts can be written in any language, but many are written in high-level programming languages such as Python, Ruby, and JavaScript.

Scripts can be used for anything from automating simple tasks to creating complex workflows. When writing scripts, it’s important to use a structured approach that makes the code easy to read and maintain. This involves breaking the code down into small, manageable chunks and using meaningful variable names and comments to explain what each section does.

By following a structured approach when writing scripts, you’ll make your code easier to read and less likely to contain errors.

Coding style is the set of rules followed when writing code. Different styles are often used by different developers, but it’s important to use a consistent style throughout your scripts if you want them to be readable and maintainable. Using a coding style that is easy for others to understand makes the script more accessible, which in turn makes it easier to understand and maintain.

If you’re using a style guide when writing scripts, it’s important to stick to that style completely in order for your code to be easily accessible. This means that the way you indent your code, use underscores and dashes, and name variables must all match the guidelines in your style guide.

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Assignment Brief 3: Demonstrate data analysis in R

In order to answer this question, I will first need to provide some information on what R is. R is a programming language that is specifically designed for statistical analysis and graphics. It has become very popular in recent years because it makes data analysis very easy.

To demonstrate how easy it is to do data analysis in R, I will use the example of the Iris dataset. This dataset contains data on three different types of iris plants – Setosa, Versicolor, and Virginica. The data includes measurements of the length and width of the sepals and petals for each type of iris plant.

The first thing we need to do in order to analyze this data is to import it into R. We can import datasets into R using the command data(“iris”). In our case, we have data on three different types of iris plants. Each type was measured in centimeters for the length and width of the sepals and petals. The first step is to convert these measurements from centimeters to inches.

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Assignment Brief 4: Describe data analysis in Python

Python is a language that has been gaining in popularity for data analysis in recent years. It has a number of features that make it well-suited for this task, including an easy-to-use syntax, powerful libraries, and an active community.

One of the most popular libraries for data analysis in Python is Pandas. It provides high-level data structures and functions that make it easy to work with large datasets. For example, you can use Pandas to read in data from a CSV file, transform it into a desired format, and then analyze it using mathematical operations and statistical functions.

Another popular library for data analysis in Python is NumPy. It provides efficient arrays and math operations that are optimized for speed. If you want to understand the performance characteristics of NumPy, you can read this blog post .

Today we’re releasing a new benchmark that tests the performance of NumPy and Pandas when doing typical data analysis tasks. Unlike our existing microbenchmarks which focus on specific use cases, this benchmark uses real-world datasets so that it’s representative of what a typical Python data analysis workflow looks like.

We hope this benchmark will be useful for developers and researchers who want to make their NumPy or Pandas code faster, but it’s mostly intended for anyone looking to choose between these two libraries.

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