DSC551 Programming For Data Science Assignment Answer Malaysia

The DSC551 Programming For Data Science module is designed to provide students with the skills and knowledge necessary to program effectively for data science applications. The module covers a range of topics, including data manipulation, exploratory data analysis, and machine learning.

In addition, the module covers how to effectively use popular data science libraries, such as sci-kit-learn and pandas. By the end of the module, students will have a strong understanding of how to program for data science applications.

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We have provided a number of assignment activities. They are as follows:

Assignment Activity 1: Apply Python And R Programming To Analyze Both Numerical And Text-Based Data.

Python and R are two of the most popular programming languages for data analysis. Both languages offer a range of powerful tools for analyzing numerical data as well as text-based data.

Python is known for its ease of use, while R is favored among statisticians and data scientists for its wide range of statistical functions. However, both languages are capable of performing complex data analysis tasks.

Which language you choose depends on your particular needs and preferences. But both Python and R are excellent choices for anyone looking to analyze data effectively.

Assignment Activity 2: Construct Simple Computer Scripts Using Structured Approach.

A structured approach is best for creating computer scripts. In this way, you can be sure that your code is organized and easy to follow.

One way to structure your code is by using functions. This way, you can group related commands together and make them easier to access. Additionally, you can create reusable functions that can be called by other scripts.

Another way to organize your code is by using variables. This allows you to keep track of data values as they change throughout the script. By using meaningful variable names, you can also make your code more readable.

Finally, it’s important to comment on your code extensively. This will help others who might need to modify or understand your script understand what it’s doing and why.

Assignment Activity 3: Demonstrate Data Analysis In R.

There are many ways to demonstrate data analysis in R. I will show you one way using the iris dataset that is included with R. The iris dataset has 150 rows and 5 columns of data. The columns are sepal length, sepal width, petal length, petal width, and species.

The first thing we will do is load the iris dataset into R. We can do this by typing “iris” into the command prompt or by clicking on “Import Dataset” in the “File” menu and then selecting “iris” from the list of datasets that appear.

We can view a summary of the data by typing “summary(iris)” into the command prompt. This will give us the minimum, maximum, mean, and standard deviation for each column of data.

We can also create a scatterplot to visualize the relationship between sepal length and sepal width by typing “plot(iris$sepal.length, iris$sepal.width)” into the command prompt.

This plot shows that there is a positive relationship between sepal length and sepal width. This means that as sepal length increases, so does sepal width.

We can also color the points on the plot by species to see if there are any differences among the three species of iris. To do this, we type “plot(iris$sepal.length, iris$sepal.width, col=iris$species)” into the command prompt.

This plot shows that there are some differences among the three species of iris. The setosa species tend to have shorter sepal lengths and widths, while the Virginia species tend to have longer sepal lengths and widths.

These are just a few examples of the many ways you can analyze data in R. R is a powerful tool for data analysis and there are endless possibilities for what you can do with it.

Assignment Activity 4: Describe Data Analysis In Python.

Python is a powerful programming language that is widely used in data analysis. It has many libraries and tools that make it a perfect choice for data analysis.

Python has powerful built-in data structures, such as lists and dictionaries, that make manipulating data easy. It also has a large number of libraries, such as NumPy and Pandas, that is specifically designed for data analysis. These libraries provide functions for performing all sorts of data operations, including reading and writing data, filtering data, sorting data, calculating averages and sums, etc.

Python also has a great interactive mode which makes it easy to explore your data and try out different methods for analyzing it. Overall, Python is an excellent choice for data analysis tasks of all sizes.

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