What are the most important machine learning projects for a data scientist's resume?

Too many people go beyond the basics and overthink to create a problem that wasn't a problem to begin with for their projects. The project should showcase your ability to identify and solve a real-world problem. Identifying the correct and most pressing problem that we have in the real world is half the problem solved. The remaining half is to develop ML models and relevant technologies showcased to solve that particular problem. Focus on the basics and you shall create something truly wonderful!

Instead of selecting the right project, I'd suggest to select the right problem to solve. Too many aspiring data scientists select projects based on what ML models they can use. In reality, not all projects are suitable to use ML models. Identifying the right problem is often the most difficult step, but it's this skill that separates the great data scientist from the good ones. Problem first, solution later. Find a problem to solve, your project will come after.

Strengthen your data scientist resume by showcasing key machine learning projects. Prioritize real-world problem-solving and Kaggle competition achievements. Highlight predictive modeling, NLP, computer vision, recommendation systems, or anomaly detection projects. Time series analysis, A/B testing, and deep learning experience are also valuable. Emphasize collaboration and relevant contributions to open-source projects. Tailor project selection to align with job descriptions, provide concise descriptions, and consider sharing work on platforms like GitHub for a more detailed view by potential employers.

First, think about the industry you are/want to work in. What are your interests and strengths? Second, tailor the project to the position you are applying for and select a dataset. For example, if you want to get a job in a biotech company it makes sense to work on protein dataset related to the companies drug portfolio. Third, think about question you want to answer about your data and choose the right techniques. Fourth, consider your compute resources. Modern ML algorithms need big GPUs and might not run on your laptop. As an alternative you can use a cloud account. Lastly, make plots to show your results so it is easy to follow the different steps of the project. You can use Jupyter notebooks to showcase the project on GitHub then.

There would be no specific or one size fit for all, it is important to attach key projects in your resume to your domain knowledge. Re-look into your immediate and long term career strategy and attach specific ML projects that speak to your career needs.Say if your key industry is finance, incline towards credit scoring, if it is FMCGs you may incline towards customer segmentation and profiling, or market basket analysis e.t.c

In machine learning project design, the CRISP-DM framework, with its six phases—business understanding, data understanding, data preparation, modelling, evaluation, and deployment—provides a systematic approach. Define the problem and stakeholders, explore data sources, and prepare data. Choose and train ML algorithms in the modelling phase. Evaluate performance with metrics like accuracy, and deploy models into production using tools like cloud platforms or APIs. This structured process ensures a robust foundation for ML projects.

Imagine Sarah, a budding data scientist, wanting to stand out. She chose a project predicting energy consumption. Rather than just building a model, she started by understanding the business implications, like cost-saving. She then meticulously documented her journey from gathering data, cleaning it, and engineering features. She tried multiple models, fine-tuned them, and shared her evaluation metrics. Finally, she deployed it as a web app. This holistic approach showcased not just her technical skills but also her ability to think from a business perspective, making her resume more appealing to potential employers.

The first phase in the DL life cycle is identifying a business goal. Every machine learning project starts with a business problem, scope, and risks. A key watch out at this step is choosing a business problem to solve where there is a solution in place. The next phase is to convert the business goal into a machine learning problem. Post-ML problem framing, the next phase is the data processing phase. Model development includes a training phase, a tuning phase, and evaluating models. Model interpretation is important for understanding how and why a particular model works. Model deployment is when you put the evaluated model in production. Finally, model monitoring is useful for identifying indicators of data and concept drift.

An effective machine learning project on a resume reflects a structured design process, ideally following the CRISP-DM framework. This involves clearly defining the business context, thoroughly understanding the data, and meticulous data preparation. Projects should illustrate skill in modeling, using techniques like cross-validation, and be evaluated using relevant metrics. Deployment, a crucial phase, showcases the practical applicability of the model. Highlighting projects that encompass these phases demonstrates not only technical acumen but also a comprehensive understanding of the project lifecycle, from conception to deployment.

When choosing topics for machine learning projects, it's important to follow a structured process. First, you define the problem, goals, and scope of the project, along with expectations from stakeholders. Next, you collect and explore data sources, considering their quality and potential variables. Then, you prepare the data by cleaning, transforming, and selecting the necessary features. Afterward, you choose and train machine learning algorithms and fine-tune them. You evaluate the models' performance using metrics like accuracy. Finally, you deploy the models for practical use, similar to serving a dish after cooking it. This structured approach ensures effective and well-planned machine learning projects.

Open Source Contributions, publications and repos carry a lot of weight compared to individual project reports or notebooks, that being said, as long as your implementation has impactful uses and showcases your understanding it will work well.

Open source projects are always more valuable than powerpoint only ones. Moreover, it is useful to show what worked and what didn't work. It makes the other understand your thought process and why you end up with the specific solution you're telling for the project.

Elena, a recent graduate, wanted to stand out in the job market. Instead of just listing her projects, she built an interactive portfolio. For her weather prediction project, she integrated a dynamic graph, allowing viewers to adjust parameters and see real-time predictions. She also included a concise report detailing her process. When recruiters explored her projects, they not only saw her technical prowess but also her ability to make complex topics accessible and engaging. This combination set Elena apart, demonstrating both her depth of understanding and her ability to communicate effectively.

Presenting machine learning projects effectively is crucial. A portfolio, ideally hosted on platforms like GitHub or Kaggle, should succinctly summarize each project, including links to code, results, and key learnings. Reports, following the CRISP-DM framework, offer a comprehensive view, detailing each project phase with supporting charts and diagrams. For a more interactive showcase, demos using tools like Jupyter Notebook or Streamlit demonstrate the practical functionality of projects. These formats not only highlight technical skills but also the ability to communicate complex ideas, a key attribute for data scientists.

During an interview process, your project is likely going to be evaluated from two different angles: A business and a technical one. For the business one, prepare a presentation, e.g. via PowerPoint, in which you explain to a (fictional) business stakeholder what your project means to them. It is important that you show that you can explain your complex data science work in easy but actionable language to a business stakeholder. For the technical angle, publish your code in a code repository, e.g. on GitHub, and make sure to have proper code documentation in place. This means at least a well-written readme file (including a link to the business presentation) and ideally also explanations for your code, e.g. in Jupyter Notebooks.

Starting machine learning projects can be streamlined with focused examples and datasets. For sentiment analysis, employing NLP techniques like word embeddings on datasets like IMDb reviews enhances understanding of text processing. In image classification, leveraging CNNs or transfer learning on datasets like CIFAR-10 or MNIST showcases skills in computer vision. Recommender systems, using techniques like collaborative filtering, can be exemplified with datasets like MovieLens or Netflix Prize. These examples not only demonstrate proficiency in various ML techniques but also adaptability in handling diverse data types.

For a strong data scientist's resume: - Showcase projects addressing real-world business problems. Start with a problem is key! - Demonstrate diverse machine learning skills (EX: supervised and unsupervised learning). - Highlight projects with high-quality results (95% accurate customer churn prediction). Example projects: 1. Predicting customer churn. 2. Fraud detection. 3. Product recommendation. 4. Marketing campaign optimization. 5. Future prediction for better decision-making. 6. Anomaly detection in data and metrics + - Align projects with job relevance (e.g., healthcare for healthcare roles). - Choose projects you're passionate about to demonstrate motivation. - Select challenging yet achievable projects to showcase skills.

In my experience, if you are new start with doing as many projects as you can , don't worry about project selection , once you get the hang of it use the learnings from these projects to apply them to a real world use case like an open source contribution, portfolio project etc.

I think the above mentioned projects are great to start your ML journey especially for gaining first hands on experience. This is not enough! Many people are mentioning these projects in their resume which is common nowadays. You should - Pick a problem statement(Domain related or type of data) - Find related data from Kaggle, APIs or Web Scraping. - Get your hands dirty in the Data Cleaning process. - Create different pipelines such as transformation, feature store, training, tuning and inference. The more unique and clean your approaches will make your profile outstanding.

Starting machine learning projects can be easier if you begin with some cool ideas. For instance, when you want to figure out if people like or dislike something, like a movie or a restaurant, you can use machines to help you. Imagine you have a bunch of movie reviews from websites. You can teach the computer to read those reviews and decide if they are positive or negative. Now, if you want to teach a computer to look at pictures and tell you what's in them, like recognizing cats or dogs, that's another exciting project. You could use lots of pictures of cats and dogs to help the computer learn how to tell them apart. These ideas are like starting points for fun machine learning projects, great ways to learn and have fun with technology.

In my opinion, it is very important to include appropriate data pre-processing and feature engineering steps in your project. After choosing an important problem to solve with machine learning and collecting relevant data, do include all the required data preprocessing and feature engineering steps based on the type of the problem and the data. This not only helps show case your data skills but also positively impacts the performance of your model.

Based on my experience, the most important machine learning projects for a data scientist need to demonstrate the ability to solve real-world problems, for example, + Predictive Modeling Projects: Show your ability to build and deploy predictive models, such as sales forecasting, customer churn prediction, or credit scoring. + Natural Language Processing (NLP) Projects: Highlight skills in processing and analyzing text data. + Computer Vision Projects: Demonstrate proficiency with image or video data. + Time Series Analysis Projects: Show your capability to handle sequential data and time-based predictions, such as stock price prediction, or demand forecasting.

Beyond specific projects, consider showcasing soft skills and continuous learning in your portfolio. Engaging in community-driven projects or competitions like Kaggle can demonstrate teamwork and problem-solving abilities. Documenting your learning journey, through blogs or tutorials, highlights your ability to evolve and adapt in the ever-changing ML landscape. Also, emphasizing ethical considerations and data privacy in your projects can set you apart, reflecting a well-rounded understanding of the broader implications of ML applications.

I think that the premise of this article is a bit off. Defining “best” or “most important “ will vary from industry-to-industry & can/may have many sub-specializations. For example, for all the hype about Data Science, the community seems to only focus on things like OLS, Neural Networks, etc. However, there are numerical optimization methods as well. If I am planning to be a Decision or Data Scientist , knowing numerical optimization techniques like Mixed-Integer Programming maybe more important or suitable than a Neural Network depending on the business need, existing infrastructure, and requirements.

Lets imagine you're a detective, and data is your crime scene. Machine learning is like your superpower that helps you solve complex mysteries in that data. Try to think of your machine learning projects as the thrilling stories of how you've used your detective skills in (machine learning) to crack data mysteries . Put that in your resume ...