What are some data science projects that employers want to see on your resume?

Real world data is messy. I've seen data sources wherein multiple observations were included in the same column separated just by "||" characters. To use such a data for a time series analysis for detecting trend the data professional first has to clean the data and extract and store that data in a usable structured format. A better data scientist would create an automated pipeline to store this clean data in a warehouse to be further used by anyone down the line..!

This is one of the crucial ones, it helps recruiters understand your in-depth knowledge of the data sets and know how you can handle complex data. Thus, make sure you create a few projects around this and highlight the key skills you learnt.

What differentiates an average data scientist from a skilled data scientist are his/her observations about the data. Irrespective of the modality, identifying ways to impute data with domain considerations, flagging potential outliers to remove them from the analysis, thinking of ways to augment data in a realistic way such that it is similar to the underlying distribution are some key aspects which data scientists are required to know.

In my experience, employers highly appreciate candidates who can handle messy, real-world data effectively. Data cleaning projects that showcase your ability to handle missing values, outliers, duplicates, and inconsistencies demonstrate your attention to data quality. Coupled with exploratory data analysis (EDA), where you uncover insights, trends, and patterns within datasets, these projects exhibit your foundational data preparation skills. For example, you could showcase a project where you cleaned and analyzed customer data to identify hidden patterns, aiding in targeted marketing efforts.

Data cleaning and exploration is an art. 70% of the time a best data practitioner will spend on data cleaning and data exploration. Most of the data science projects get the outcome in this phase itself if done in the right way. Ability to handle different data types and different data sources comes with effective ETL processes.

Effective visualization and storytelling simplifies the complexity, making it easier for non-technical stakeholders to understand the key takeaways. It not only help you communicate your findings and insights but also make your work more accessible and actionable for a wider audience. It helps connect data-driven insights to real-world problems, making them more relatable and compelling.Data visualization can aid in hypothesis testing by allowing you to compare different scenarios or groups visually. By mastering data visualization and storytelling, data scientists can bridge the gap between raw data and meaningful insights, making their work more impactful and actionable.

Data visualization is a crucial skill in data science. Employers seek professionals who can transform complex data into easily understandable visuals. Create projects that showcase your proficiency in tools like Matplotlib, Seaborn, or Tableau to craft compelling visual narratives. Narrating your findings effectively is equally vital. Build projects that combine insightful visualizations with clear storytelling, making complex data accessible to non-technical stakeholders. A project illustrating trends in stock market data or social media sentiment analysis with interactive dashboards can be compelling.

Data-driven decisions are key for both technology and non-technology (i.e. chemical, manufacturing, construction, real estate, energy, consumer staples, utilities) companies. The leaders in non-technology companies have expertise and experience in respective fields and prefer to see data insights make decisions without getting too much into the data technologies. This requirement motivates employers to hire experts with data visualization and storytelling skills.

As a data scientist, data visualization and storytelling serve as a crucial bridge to engage both technical and non-technical audiences by presenting clear and easily understandable insights. When catering to your non technical audience, condensing a week of thorough analysis into just two sentences underscores the need to recognize that not all efforts in a project warrant commendation. Employers resonate with candidates that drive impact through clarity and understand the essence of conveying digestible storytelling.

You can clean your data, run your predictions and get some metrics but if you cannot communicate the insights then all that work will be useless. Visualizations needs to be tailored to the intended audience and you need to ensure the types of visualizations (e.g. bar, lines, etc) and even colors help to convey your data's story.

It's easy to gravitate toward the most complex models when it comes to building out your data science portfolio. But in my experience it has been my projects using regression and classification models that have impressed interviewers the most because they were more EXPLAINABLE projects. Being able to clearly speak to the business value of a project will go much further in your interviews than using super fancy models.

In my experience, most of the data scientists directly go for more complex machine learning models which can often lead to better performance on certain tasks. But the trade off is complex models typically require more computational resources, including memory and processing power, for both training and inference and are more prone to overfit. Complex models often have a large number of parameters, making it challenging to interpret and understand how they make predictions since they lack transparency. It’s always better to first try simple and less fancy models for your use case and measure whether those models solve your problem.

When it comes to ML projects in your portfolio, be creative! Avoid including projects that everyone else has completed, like the Titanic survival analysis on Kaggle. Instead, consider something that genuinely interests you, or explore freely available datasets on the web and come up with a feature you can attempt to predict. It doesn't always have to be extravagant; a well-executed simple project is often more impressive than an overly complicated one that is poorly executed.

Data science relies heavily on machine learning and modelling to solve diverse business problems, such as customer attrition and fraud detection. Consider including unsupervised projects, such as topic modelling or market segmentation, to demonstrate a broader skill set alongside supervised learning tasks. To distinguish yourself, you should pursue creative initiatives that address novel problems or utilize real-world data and constraints. The objective is to demonstrate not only your technical expertise, but also your adaptability and business acumen.

In building a data science portfolio, simplicity often trumps complexity. Projects using regression and classification models stand out in interviews due to their explainability. Clearly articulating the business value of a project is more impactful than employing intricate models. This approach demonstrates practical understanding and effective communication skills, key in data science roles.

As the VP of data science & AI I can emphasize the critical importance of deployment and production skills in a data scientist's resume. Recruiters highly value professionals who can not only build sophisticated models but also effectively deliver and maintain them. Projects demonstrating your deployment and production capabilities are like gold on your resume. They showcase your ability to take your models and applications and make them accessible to a broader audience. This involves creating web or mobile applications that incorporate your machine learning models or exposing these models through APIs. Such projects highlight your capacity to deliver and maintain solutions effectively, making you a valuable asset to any data science team.

In product companies, data scientists are not just building ML models but are equally responsible to put the code into production. Recruiters highly value this skill!

One data science project deployed to production in a cloud environment is more impressive than 10 Jupyter Notebooks on your portfolio.

Core skills for Data Scientists including data analysis, modeling, and interpretation are essential, they are not sufficient on their own. Data science models and insights when deployed into production systems can influence decision-making and improving processes for an organisation. It helps Data Scientists understand how to build systems that can handle large amounts of data and traffic and tools and platforms, such as Docker, Kubernetes, and cloud service.Its deployment in production which ridge the gap between analysis and real-world implementation making your skills a valuable asset.

Data science isn't just about building models; it's about deploying them in real-world scenarios. Employers value candidates who can bridge the gap between development and production. Highlight projects where you've taken a machine learning model from development to deployment, whether through APIs, web applications, or integration with existing systems. This shows that you understand the practical aspects of putting data science into action.

This is the most critical aspect for data science. Some key major challenges are the following. 1. Domain functional and granular data understanding 2. End to end domain/ process life cycle 3. Comprehensive data depth through hands on data understanding 4. EDAs need to be comprehensive/ many times very basic (simplistic notebooks copied with statistical distributions and correlations) 5. Data analysis approach and explaining 6. Many a time either a tool or simple excel is used; explainable data storytelling is missing 7. Discussions and whiteboards technical are missing/ catches many aspects 8. Lack of working documentation/analysis is missing 9. Queries / question on data to be asked from experts 10. Preparation / collaboration is key

Effective data scientists understand the business context and can apply their skills to solve specific industry-related problems. Projects that reflect your domain expertise and problem-solving abilities are highly regarded. For instance, if you're interested in healthcare, a project involving predictive analytics for disease diagnosis or resource allocation in hospitals can demonstrate your domain knowledge and problem-solving skills.

For a fresher, it is beneficial to work on diverse projects such as classification, regression, time series analysis, deep learning, and NLP. However, when mentioning projects on your resume, it is important to tailor them to the specific job you are applying for. If you are applying for a Deep Learning Engineer role, focus on highlighting strong DL or CV projects like Image Classification or Object Detection, rather than basic ML projects, to showcase your expertise in the desired field.

A project that applies data science techniques to a specific industry issue, such as fraud detection in finance or demand forecasting in retail, can showcase your ability to leverage data in practical, real-world situations. Highlight how you've identified key problems, formulated hypotheses, and applied relevant data science methods to solve them. Projects where you've worked with domain experts or utilized specialized datasets to drive meaningful outcomes can particularly stand out, as they show your capability to bridge the gap between data science and domain-specific challenges, providing solutions that can significantly impact business strategies or operations.

Na carreira de ciências de dados, o profissional tem que ser capaz de ver de forma macro como um problema de negócio pode ser resolvido com técnicas estatísticas, matemáticas, ou de aprendizado de máquina. Mais importante que a técnica é saber como ela resolve um problema. Saber técnicas avan?adas mas n?o conseguir gerar valor n?o vai dar resultado.

I hired for major technology companies, and here's one thing I like to see but almost never do, and another that I don't care about seeing but I almost always do. First, what I would like to see: projects that go to production. A lot of data science projects are experiments, many become a slide on a PowerPoint or an email. The real big projects become part of production systems. That requires different skills, things need to provably work at scale. It's rare to find these people. If you done something like this, make sure it's in your resume. What should not be there? Stuff that everyone else did, like Titanic, Iris, MNIST. If we are interviewing you, we think you know the very basics.

It will largely depend on the role I am hiring for and the level of responsibility it demands. If it is for early to mid career role, I would expect the resume to have projects solving a part of problem with a right approach well explained. Potential red flag - e.g. someone is claiming to be responsible for creating a credit risk model from developement to monitoring. Surely he/she doesn’t have right idea how it works in the enterprise setup. For mid to senior level role, the focus would be more on the steps taken to put the solution in production- from ROI consideration to service outage fallback. Potential red flag - e.g. someone not really mentioning the potential loss saved/revenue generated through the model recommendations.

One of the most important aspects I want to see from a candidate is a deep understanding of the problem they are trying to solve. Many times I heard candidates explaining the data science aspects but unable to explain what is the real problem that they were trying to solve and why what they did was the right solution. In my opinion, a good data scientist must be responsible for verifying that they are solving the right problem

Companies want to know if you have the relevant skills or experiences and that's where portfolio projects play a key role. Being in the industry for 9 years and so on, most of the companies solve 2 problems. 1) Churn and 2) Revenue. Companies would want to know why customers are leaving i.e. churn as well as how much money they are bringing in. So, focus on building projects that answer these questions and you will have the eye of the hiring managers.

Along with basic data cleaning, EDA, Data visualizations, like Others have commented, deployment is definitely an important aspect of data science. In my career, although I have deployed a few models, a lot of the other models have been experiments too. But, what I have done differently is that, to use the experimental models too, to mine valuable information from the data, tell compelling stories to the leadership to make impactful business decisions. Data Science, like my first Company's CEO used to say is a "symphony of - Analysis, Insights, Predictions and Prescriptions". So, I would say, although deployment is important, impacting the business decisions is also a valuable contribution.