Data Science is one of the fastest-growing career fields, evident from the increasing number of data-based jobs. According to a report from Glassdoor, data-driven jobs are particularly popular among Gen Z.
If you are a newcomer seeking a job in the dynamic field of data science, it is crucial to stay updated and adapt to the industry trends. But how do you distinguish yourself from the competition?
The simple answer is to build a strong portfolio.
Showcasing your enthusiasm for solving existing problems that can lead to breakthroughs in the industry is the ideal approach to take. Projects demonstrate your understanding of business problems, your data-driven problem-solving approach, and your technical skills.
As big tech has resumed hiring for the data scientist roles, all the data scientists should be working to refine their resume and secure the job you desire in 2023, we have compiled a list of 5 trending data science projects that deserve a spot on your portfolio.
Whether you want to prepare for the Google Data Scientist interview, as Alphabet seems to have resumed hiring – or any other firm, these projects can be a great addition to your resume.
A Project on Detecting Parkinson’s Disease
Parkinson’s disease is a condition that primarily affects older individuals and leads to a loss of control over body movements. Its symptoms range from hand tremors and body stiffness to difficulties in walking. The disease progresses through five stages, with stage 1 having minimal impact on daily activities and stage 5 severely limiting day-to-day functioning. Unfortunately, many people suffer due to the late detection of the disease.
This is where data science can help us provide improved healthcare services to these individuals. By using Python as the coding language and employing XGBoost, we can detect Parkinson’s disease and identify patients who are at risk of developing the disease or show signs of future susceptibility.
XGBoost is an open-source software library that supports multiple programming languages like C++, R, Python, Java, Julia, and more.
If you’re looking for some useful datasets, check out the UCI ML Parkinson’s dataset.
A Project on Exploratory Data Analysis
Exploratory Data Analysis—also known as EDA—is the process of understanding and interpreting data through thorough investigation. It involves discovering patterns, identifying trends, detecting anomalies, and testing hypotheses. The findings are then presented using statistics and visualizations, such as graphs and charts.
Let’s consider an example—Imagine you are planning a trip to a new city and want to explore the best places to visit. You gather information from travel websites, read reviews, and analyze tourist attractions in the area. By examining this data, you can make informed decisions about which places to prioritize during your trip. This process of analyzing and interpreting the available information is an example of conducting exploratory data analysis.
If you are seeking useful datasets for EDA, Python users may find the Matplotlib library helpful, while R users might want to explore ggplot2. These libraries provide tools and functionalities for creating visualizations that can help you in the exploration and presentation of data.
A Project on Data Cleaning
As a data analyst, a significant portion of your time will be dedicated to data cleaning. Real-world data is often messy, requiring thorough organization and cleanup before further processing. Cleaning data can be a tedious task, primarily due to the sheer volume of information that data scientists must handle. This process may involve removing irrelevant columns, which necessitates extensive research to understand the purpose of each column in the dataset and assess its future importance.
Another challenge frequently encountered is dealing with missing values. While identifying missing values is a straightforward task, addressing them can be frustrating. Completely discarding records with missing values is not always the optimal solution; instead, intelligent handling of these missing values is necessary.
Demonstrating proficiency in data cleaning is highly advantageous when seeking employment. To start, select a few datasets that require thorough cleaning. Once you have made your selections, equip yourself with the appropriate tools. If you prefer Python, consider utilizing the Pandas library. Alternatively, if you prefer R, take advantage of dplyr.
A Project on Movie/TV Series Recommendation
This is an exciting data science project that is sure to attract a lot of attention!
Considering the binge-watching norms in today’s climate, recommendation systems have gained immense popularity. If you have ever used streaming platforms like Netflix, Hulu, or Prime Video, you may have noticed how these platforms understand your viewing preferences and provide recommendations for similar movies or TV series that you might enjoy.
A movie recommendation program helps individuals discover more content that aligns with their interests. It creates a personalized list tailored to each person’s preferences. These recommendations can be based on browsing history, similar viewing patterns from people with comparable demographics or traits, and more.
To implement this project, you can gather feedback from viewers who have already watched a movie and categorize their responses. You can utilize the R programming language to develop this movie recommender system. If you’re looking for useful packages, consider exploring erlab, ggplot2, data.table, and reshape2.
A Project on Interactive Data Visualization
The final project on our list is Interactive Data Visualization. This data science project focuses on creating graphical elements such as dashboards, maps, and charts to present information in a visually engaging way.
The power of visual representation lies in its ability to simplify complex data and enhance understanding. A well-designed visual can simplify even complex data, while a poorly designed one can confuse even the simplest concepts.
In the field of data science, it is crucial to prioritize the user experience. Images have a captivating effect on users, surpassing the impact of long blocks of text. By incorporating interactive data visualizations, more people can accurately interpret and leverage the information at hand.
As businesses increasingly recognize the importance of Interactive Data Visualization for informed decision-making, choosing this project will undoubtedly attract attention. Python users can take advantage of Dash by Plotly, a fantastic web-based analytics app, while R users can leverage RStudio’s Shiny.
Closing Thoughts
In today’s competitive job market, companies have become significantly more selective in their hiring process, actively seeking out candidates with precise skills and extensive experience.
Which is why, obtaining a position in the field of Data Science can be quite challenging. However, engaging in projects like the ones mentioned above will unquestionably help you showcase your abilities and proficiency in this domain.