How can data scientists increase their problem-solving confidence?

- Don't just accept the initial problem statement at face value. Dig deeper to understand the business goals, stakeholders involved, and any existing data or analyses. - Engage with colleagues from different backgrounds to gather diverse perspectives on the problem. This can spark new ideas and challenge existing assumptions. - Don't aim for a perfect solution right away. Build a simple, initial model or analysis to test your understanding of the problem and gather feedback.

Defining and understanding the problem correctly constitute a significant part of any task. This applies not only to the field of data science but also to other problems we encounter in life. Understanding the need accurately can reduce the time spent on unnecessary outputs. Therefore, examining the dataset and contemplating what is exactly required before coding, considering how much of it we can solve with the resources at hand, is highly beneficial. We should manage the process in a planned and organized manner to avoid drowning in code, complex datasets, and outputs in the future.

The main goal of data science is to derive insights from data, and a data scientist should be adept at efficiently utilizing and extracting the full value from the available data. Therefore, understanding the problem and the data is the initial step in data science. This understanding is crucial for establishing Key Performance Indicators (KPIs) and developing effective solutions.

Solving a problem with scientific approach is our main goal and to do it we need a problem. Identifying the problem is crucial to defining the scope and the goals of the study. Client and the stakeholders are need to be communicated clearly on both of these. When we define our problem clearly we can make our research on the literature, databases and business. We need to know what we are looking for to build our hypothesis.

It's very true and I would like to mention one small point also that when working with data science problems the first step should be to verify that "This problem is a data science problem" and we actually need an intelligent machine to solve this problem rather than a hard coded logic.

Familiarizing with available data is a pivotal step in problem-solving, involving understanding data characteristics and quality, and assessing how it relates to the problem at hand. Through exploratory data analysis, using techniques like summary statistics and visualizations, valuable insights and patterns are uncovered. Data cleaning, preprocessing, and feature engineering are essential to prepare data for analysis and modeling. Additionally, integrating external data sources and iterative exploration can enrich the analysis. This thorough understanding of the data ensures that subsequent modeling and analysis are grounded in a solid, relevant foundation, enhancing the potential for meaningful solutions.

I will always plot my data in several different ways to see what I am working with. Those of us lucky enough to have our sight are visually driven. You can quickly spot patterns and correlations a lot of the time by simply plotting what you have. It also can tell you very quickly a lot about what the quality and quantity of data is like.

By failing… a lot. This sounds cynical but trial and error is the best way, I’ve found, to know you can solve problems. When I was in class I would try to apply new tech or lessons to something I wanted to build so I would come across real world problems. I have continued to build “for me” ever since and find new problems that never came up in my lectures or in my day to day work and had to figure out how to solve it. Then when you come across something similar in the real world you’ll know you’ve solved something similar before

Make sure that data engineers are involved in this step to point out all of the gaps in Meta data descriptions. If the data contains real quality issues it can effect your problem solving later on drastically.

What I personally do is to explore the space of solution by choosing a metric of comparison and then compare a number of models. So for example, imagine I need to solve a Classification problem for an imbalanced data: What I do is that after splitting data I choose for about 10 completely different models like XGBoost, Random Forest, CNN, Logistic regression, KNN etc. and monitor how my model react to them and try to understand the behavior of data. Also how numerical columns and categorical columns effect the model by just use one or another. How better you understand the results of the model, how better you choose the most stable one to the future.

Knowing what methods are suitable for your problem is important for a number of reasons; memory management, application, accuracy, repeatability, etc. Occasionally you have to become inventive and develop your own methods based on how you are able to manipulate your data. This will require a deep understanding of the data itself from a Subject Matter Expert (SME) in the area from which the data has been obtained. Don't fall into the trap of running everything through a Neural Network and being unable to explain what logic is being used.

As data scientist, I used K-Fold Cross Validation method to estimate the skill of Machine Learning model when using new data or limited data. Whether It is M/L classification or regression module, The result is less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split. This Cross-validation method is also primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model. Interoperability plays in key role in delivering the best trained M/L model results.

When presenting your findings to the stakeholder, 1. Keep things crisp and clear 2. Do not dive into technical details 3. Highlight important things and give reason only when asked 4. Never give action points, give options. Let the domain expert make choice

When communicating the results, make sure you take care about: 1. Not being over confident about your findings. Be sure that you present it as findings rather than verdicts. Also, look critically at the results you have, and try to find and share any conditions or weaknesses it may have. 2. Your communication should be as simple as possible. For example, Make use of rich, yet simple visualizations. Using a flowchart to explain a data preprocessing pipeline will help you far more than explaining the procedure verbally.

This is very important. Usually your audience will be a mix of tech and non tech people. You have to communicate in a way that it is digestible to the people of both sides. For that, 1. You need to include rich and appropriate visualizations. 2. Provide statistical/logical justifications to your assumptions and conclusions. 3. Do an RnD of similar problems. It will help you polish your results and save you from a few blunders. 4. Do not communicate every bit of detail. How you cleansed those strings might be important to you, but might not be very important to management people. Your communication should be brief, and answer to the main problem. For other details, just document those.

Arguable the most important part of Data Science in communication of results. Having the best performing model in the world means nothing if the output is unintelligible to stakeholders who need to use it. This is also another reason to avoid the pitfalls of running everything through a Neural Network until you achieve acceptable scores. It is impossible to communicate what the data is telling you to someone else if you can't know what the data is doing.

Learning from feedback is fundamental for data scientists to boost their problem-solving confidence. Feedback, whether from peers, mentors, or data outcomes, is a rich source of learning and improvement. By actively seeking and constructively processing feedback, data scientists can refine their approaches, learn from mistakes, and understand diverse perspectives. This process not only sharpens analytical skills but also builds resilience and adaptability. Embracing feedback fosters a growth mindset, crucial for tackling complex data challenges with confidence. It turns every project into an opportunity for development, gradually bolstering the confidence needed to excel in problem-solving.

Incorporating a feedback loop in data science teams is crucial for continuous improvement and increased problem-solving confidence. Here's a practical guide to implementing an effective feedback loop: - Collect Diverse Feedback Sources: Gather feedback from various sources, including end-users, domain experts, and business leaders. Diverse perspectives provide a comprehensive view of the project's impact. - Utilize Code Reviews and Pair Programming: This ensures that code quality meets standards and allows team members to share knowledge and identify potential improvements. - Document and Learn: Document the feedback received and the actions taken in response. Use this documentation as a learning resource for the team.

In my experience, the best feedback is from stakeholders or end users: does your data science solution really help them, solve their problem, e.g. increase the CTR (click through rate), increase sales? And also important: Was the result achieved worth the effort? Or would it have been better to invest the effort in solving other data science problems? As a data scientist, you should always critically question in which area you can achieve the best results and always take the company's roadmap into account, i.e. regularly coordinate your planning with the business roadmap

Learning from feedback is a powerful way to boost problem-solving confidence. Whether it's positive or negative, feedback provides valuable insights into our strengths and areas for improvement. By actively seeking opinions from peers, mentors, clients, or users, and through self-assessment and reflection, we can sharpen our skills and expand our understanding. Feedback also serves as a guide for setting realistic goals, monitoring our development, and recognizing our successes. Embracing feedback, in all its forms, opens the door to continuous learning and personal growth.

In order to advance in job, one should never back down from a challenge. Obstacles do arise; don't be afraid to seek assistance and learn. Challenges will help people develop, push boundaries, and increase chances of being recognized by team

Life isn’t a spectator sport. Get your hands dirty, risk your ego & embrace challenges to increase your problem-solving confidence. Even people with loads of natural talent aren’t superstars out of the gate - it takes learning, practice, discipline & commitment to be successful. As you learn, identify a few real problems that matter to you or your employer. Start out small then scale it up. To this day I build toy models before delivering a full scale solution. Practice - nothing builds confidence like a few wins.

I strongly believe that the best way to learn is to do. And the only way to learn something new is to do things that you have never done before. Look into areas of Data Science that you have not touched upon, find projects that will push you out of your comfort zone. Attend conferences and project meetings of other people's work in areas adjacent to your own. I have learned a lot within my academic journey, but I have learned the most from completing projects in areas where I previously had little to no experience.