Top 5 data science projects to get hired in 2022
Data science is shaping our future – playing a core role in both business and technological innovation. As we continue churning out mass volumes of digital information, the field now faces rapid evolution and high demand, unsurprisingly drawing in those with the technical, mathematical, and analytical talents to succeed.
Below, we break down the top five data science projects to pursue for those aspiring to the industry, and how they can boost your chances of landing a career in the field.
How to plan and organise data science projects
As a highly complex, technical skills area, it pays to be well-organised with your data science projects. You’ll be dealing with large sets of raw, unstructured information – and without a proper plan or system, you’ll risk losing focus, critical datasets, and overall productivity.
Individuals are recommended to create a checklist segmented into three parts:
- the problem you intend to tackle (or the core objective),
- your solution (the set of deliverables your market or stakeholders need), and
- how you aim to approach it (your implementation plan).
Starting off with this template can help you set the required direction of your project. From here, you can customise the checklist depending on your specific project needs, and flexibly tweak certain elements as you implement it.
Top 5 data science projects to get you hired in 2022
Data cleaning
A common project type to flex your data science skills is one that involves data cleaning. With digital users generating unfathomable amounts of data per day (estimated to reach 97 zettabytes daily by the end of 2022), much of your time spent as a data scientist involves mining, managing, and cleaning these large volumes of raw information.
A data cleaning project as a good way to practice (and showcase) your basic skills in this field. When opting for this project type, be sure to select a data set that effectively challenges your skills – particularly one that involves substantial research, has a lot of nuances, is spread across multiple files, and represents a real-world application.
Exploratory data analysis
Once you’ve cleaned the data, your next step is to analyse it. This is also known as “exploratory data analysis”, or EDA. Through this type of project, you’ll be challenged with spotting any patterns, trends, errors, or insights within your newly-cleaned data – which will then be presented through statistics or visual graphics.
Creating graphical insights through EDA projects could include plotting raw data to present your findings and drawing simple, statistical information from this plotted data.
The final step is to then ask questions on the various correlations you’ve found through the EDA process. This helps practice and prove your skills as a data analyst, and further assesses the relationships and patterns found within your dataset.
Data visualisation
As mentioned, data visualisation forms a core part of EDA – though it can also form its own separate project. Data visualisation projects can demonstrate your ability to present data findings through engaging, interactive graphical elements, such as charts, maps, scatterplots, and dashboards. Visual images are often more effective in expressing complex data information, particularly for those with a non-technical background. As such, data scientists are recommended to visualise and tell a story when presenting their findings, especially in an educational or business setting.
Those opting for this type of project can benefit from related apps such as Dash by Plotly (for Python users) and RStudio’s Shiny (for R users).
Data clustering
Data clustering projects allow you to further organise your data into classified sets, categorising them by similar features and characteristics. As with other data science projects, you can also use algorithms (such as KNN or DBSCAN) for a more efficient clustering process.
There are plenty of datasets you can currently use for clustering projects, making this one of the more accessible project types. Professionals are recommended to pick a few to implement their clustering approach.
Machine learning
Once you’ve covered the fundamentals of data science, you can bring your projects to the next level by engaging in machine learning concepts. Machine learning and AI technologies are currently paving our digital future, so this allows you to further stand out from the crowd as someone with an advanced, up-to-date knowledge of industry demands. Example project topics could include fake news detection, review sentiment analysis, sales prediction, and digital recommendation systems.
When tackling data science projects in machine learning, be sure to cover all the fundamentals of the area – including clustering, regression, and classification algorithms. This can help demonstrate your comprehensive knowledge of the field, as well as carve out a potential pathway towards data engineering.
What method can be used to measure the success of a data science project?
While there are varying methods one can use to measure the success of their data science project (with some data scientists implementing model performing metrics like Precision, RMSE, and AUC), some also recommend the FISO (Financial, Innovation, Stakeholder, and Organisation) framework as a general approach.
Financial metrics are simple – they measure the value of your data science project in terms of financial returns. Innovation measures the growth of one’s skills and the impact of their contributions, such as the competencies they’ve gained throughout the project, the number of papers published, the re-usable elements created, and any contributions to the open-source community. Stakeholder metrics measure the resulting engagement and satisfaction of your target market or stakeholders. This could include the use of your product and the number of times it directly impacted the consumer experience. Finally, organisation metrics focus on the value your project has brought to your overall organisation, and how it’s positively impacted or upheld company values.
What are some best practices for managing data science projects?
So, ready to start your first data science project? Below are additional tips on how to make the project run as smoothly as possible.
- Proper naming is everything. With the large volumes of data you’ll be handling, be sure to properly (and specifically!) label your files, folders, and directories as necessary. Keep them descriptive, and avoid using characters or spaces to make them both human and machine-readable.
- Iteration is key. To keep up to speed with a rapidly changing industry landscape, data scientists must constantly review and rework their machine learning algorithms, ensuring they stay effective and are bearing optimum results.
- Select the appropriate tools and KPIs for your project. Every project requires its own unique set of business intelligence and data science tools. Be sure to choose the right coding languages, datasets, and systems to carry out your project – along with appropriate KPIs to measure your project goals.
- Be adaptive. Projects will oftentimes evolve during the implementation process, so it helps to stay open-minded and flexible. Don’t stray away from adapting your approach when necessary, as doing so can often lead to greater results than expected.
Explore data science projects and get started today
Pursuing your own data science projects are a recommended path to career success – but so is having a certified skillset. Upskilled currently offers a wide range of courses in the data science field, including programs on Python, artificial intelligence, and big data engineering. With online delivery, you’ll also have the freedom of flexibly learning at a time, place, and pace that suits you best. Build the skills to become a data scientist or data analyst today, and enquire with us on a course.
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