Statistical data modelling

This is one of my data science career journey documentations. It will be about my second semester of studying Master of Data Science(MDS). Similar to the previous part, this article will provide a review of the core data science units and provide some advice for individuals who are interesting in studying postgraduate Data Science degree.

Before I started reviewing my second semester, I would like to thank you to all the readers who just pass by and read my previous article. Also, for those who have not read my previous reviews here is the link to my first review. Please, check that out:

If you guys have read it, now let’s start my second-semester review.

After I have finished my first semester studying all the fundamental units including the following: Python, Databases, Computer architecture and network, and Mathematics. Now, it is the real beginning of learning data science core units.

All of the core units for my master’s course are including the following:

For this semester, I enrolled in the first four units. This is my review of these units:

As the name suggested, this unit covers the basic information regarding data science starting from the emerge of data science to the overall workflow of data science(data collection, data wrangling, data analysis and exploration, model building, deployment, data governance, and data management). Also, this course provides some practical assignments such as using R for data cleaning and data analysis, using bash for data exploration, and making a proposal for a data science project. In my opinion, the unit is well structure. The course provides a good foundation for a higher-level data science unit.

At first, I thought this unit will be more about the application of statistics with the coding but I was wrong. The unit is heavy-focus on statistics, a little bit of coding, and some basic machine learning algorithms. The topics in this unit include the following:

This course is one of the hardest units in the master’s degree mentioned by the lecturer himself. The structure of this course is divided into the following:

I have a hard time studying this subject because it is focusing on math instead of coding. Not only do I have to learn a new topic, but I have to apply what I have learned from the last semester to solve some statistics questions. For example, I have to apply calculus to find the probability of continuous distribution data type and one identifying the maximum likelihood. The rest of the unit is the pure statistics and the machine learning stuff which I quite interesting. Moreover, this unit is the main fundamental for most of the elective units such as machine learning, applied data analysis, data analysis for semi-structured data, and deep learning. Without passing this unit, you cannot study those earlier mentioned units, so it make me feel stressed out during the beginning of the semester because I failed the mid-term exam. However, with my effort and less sleep time, I manage to pass the unit.

This is one of the unexpected dark horse units. At first, I thought it would not be that difficult because I am confident in my coding skills for a certain level, but it actually pretty difficult (easier compared to statistics haha). The unit is mainly focusing on using python to pre-process the data, clean data, use basic exploratory data analysis on the data, and integrate the data from various sources. Based on the following, all the students have to learn the following contents in order to complete the earlier mention tasks:

With all of the following learning content, it makes me understand data wrangling process in data science projects. I realized that the most important process in a data science project is the wrangling part.

Another thing I forget to mention, this unit has no exams. As a result, it makes the assignment more difficult.

This unit covers mostly how to use the visualisation tools or techniques for presenting insight from the data. it is mainly focusing on an explanation of the start of using data visualisation in a data science project to how the human visual-perception system is affected by the up-front visualisation. By learning this unit, I have a better idea of how to choose a suitable visualisation when providing insight for a data science project. For example, when you want to identify the trends, time-series, or prediction pattern the line graph is more suitable than the bar chart. Another example is the differences in using 2D and 3D visualisation, it is possible that if the data does not process any high dimensional variables, making a 3D visualisation is unnecessary because it influences our visual-perception cell on the audience's eyes when presenting the visualisation.

This unit gives students a chance to choose their own project of interest, and explore and visualise the insight of the project into any format such as a dashboard or web app. Similar to the previous unit, this unit does not have an exam. However, they provide some coding exercises and quizzes instead.

The tools for completing this units assignment include the following:

The project topic I have chosen for this class is “The trends of Data Science job posts in different countries” (In the future, I might write an article about this project).

Overall, this semester is way more difficult compared to the previous semester but I think I have gained more knowledge in data science. All of the units are well-structured. The only difficulty is that they ask the student to do more assignments. The merits of giving more assignments mean fewer exams, so I think this suits me better than giving me a final exam for all of the units. Also, I think I have learned more effectively compared to previous semester and my grade improve. Lastly, this semester's units make me confident in working with data and it makes me realise that I need to continue learning statistics and some data structure as a preparation for next semester.

The next part will be the final part of my masters’ review. It will be published once I finish the last two or three semesters. I hope this article is useful for the readers and thank you for your support.

Acknowledgment:

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