DSC551 Programming For Data Science Assignment Sample UiTM Malaysia

Data scientists spend a lot of time and energy on data analysis. To get the best results, you need an automated solution that can take care of all these tedious tasks for them. DSC551 Programming For Data Science will teach students how they work with programming languages like R or Python so as not only to save valuable mental space but also to increase productivity by automating simple processes such as loading large files into memory from external sources.

This course is designed for experienced programmers who want to learn how to use these languages for data science. In addition, the course will also cover the fundamentals of big data so that students know how to work with and manipulate large datasets. By the end of this course, students will be able to apply their knowledge of programming to real-world data science problems.

This course will be a comprehensive guide to the different aspects of programming that data scientists need to know to be successful. We will start with an introduction to the basics of programming, covering topics such as variables, data types, control flow, and functions. Then we will move on to more advanced concepts such as working with libraries, objects, and algorithms. By the end of this course, students will have a solid understanding of how to program in R or Python and be able to apply these skills to their data analysis projects.

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Assignment Brief 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 Python and R can be used to analyze numerical data. However, R is more commonly used for statistical analysis, while Python is more versatile and can be used for a wider range of tasks, including web development, artificial intelligence, and scientific computing. Python is also easier to learn than R.

When it comes to analyzing text-based data, both Python and R can be used. However, Python is again more versatile as it can be used for natural languages processing tasks such as sentiment analysis or text generation. Additionally, many machine learning libraries that can be used for text-based data analysis are written in Python (e.g., sci-kit-learn).

Overall, Python is a more versatile language that can be used for a wider range of tasks. If you are just getting started with data analysis, Python may be the better choice. However, if you are specifically interested in statistical analysis, R may be the better choice.

Assignment Brief 2: Construct Simple Computer Scripts Using Structured Approach

The best way to construct simple computer scripts is to use a structured approach. This means breaking the script down into small, manageable pieces and then putting those pieces together in a logical order.

One way to do this is to create a list of the tasks that the script needs to accomplish, and then break those tasks down into smaller steps. Once you have a list of the individual steps required, you can start working on putting them together in a logical order.

It’s often helpful to use comments within your code to explain what each section does. This can be extremely useful when looking back at your code at a later stage, or when sharing it with others. Taking the time to add comments now can save you a lot of time and frustration later on.

Finally, remember to save your work regularly! A power outage or sudden shutdown can easily result in lost work, so it’s important to get in the habit of saving often.

Assignment Brief 3: Demonstrate Data Analysis In R

There are many ways to demonstrate data analysis in R.

One approach is to use the built-in data sets that come with the R package. For example, the built-in data set mtcars can be used to evaluate different regression models fitted to mileage per gallon (MPG) and other vehicle characteristics. 

Another approach is to use real-world data sets. There are many sources for open data sets that can be used for this purpose. The UCI Machine Learning Repository, for instance, hosts several datasets that can be used for data analysis. quanteda’s dataset library contains a variety of political science text datasets, over 50 of which are ready to use and bundled with the quanteda package. The data sets vary in content from presidential speeches to tweets to news articles.

In this post, we will use the mtcars data set to illustrate data analysis in R. We will go through the process of loading the data set, cleaning the data, exploring the data, and modelling the data.

Assignment Brief 4: Describe Data Analysis In Python

Data Analysis in Python is a process of examining data sets to see patterns and draw conclusions from them. It involves the use of various mathematical and statistical techniques, as well as tools for data visualization.

Python is a popular programming language that is often used for data analysis because it is easy to learn and has powerful libraries for dealing with data. Pandas is a library for Python that is specifically designed for data analysis, and it includes powerful functions for manipulating and analyzing data sets.

Data analysts often use Python to clean up messy or incomplete data sets, perform statistical analyses, create graphs and charts, and predict future trends. By understanding how to use Python for data analysis, you can gain insights into the patterns and trends that are hidden in your data.

Python is a powerful programming language that is widely used in data science and analytics. It has many libraries and tools that make data analysis easy and efficient. In particular, the Pandas library is very useful for data exploration, cleaning, and analysis. The SciPy library provides many powerful tools for statistical modelling and machine learning. And the Matplotlib library can be used to create beautiful visualizations of your data.

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