Here's how you can apply problem solving techniques in data science.

My tech lead and mentor says.. Data science is all about experimenting 100 things and we just need to get lucky 1 or 2 times. Only thing that makes you a good data scientist is rationale behind those experiments, as it not should be random...

It's essential to clearly outline our ultimate objective as well as intermediate milestones to ensure our progress aligns with our objectives. One strategy involves dividing large problems into more manageable components. For instance, when creating a recommendation system for an online shopping platform, you can divide the task into stages such as gathering data, preprocessing, selecting models, and assessing performance.

Defining clear goals is essential in data science projects to ensure alignment with business objectives and to guide subsequent actions. Start by understanding the business context and identifying specific problems. set focused, measurable, and achievable objectives. Engage with stakeholders for insights and be ready to refine goals as new data emerges. Prioritize based on impact and resources, and ensure goals are well-documented and communicated across all teams.

Before you start to solve a problem, pause and get clarity on the "why". Why is the solution important and what will it unlock for the business? Why is this a priority? Why is a particular stakeholder invested in the outcome? Answering these and similar questions early will help you stay on the right track. Many times, problems are phrased ambiguously, or there is an unstated assumption about why it is important which can lead to an erroneous interpretation. For eg. If you knew the stakeholder wanted to use the Monthly Active Users (MAU) for a marketing campaign, vs a generic count of MAU, you might produce the results in different ways.

Defining clear goals is paramount in data science. Understand the business context and set specific, measurable objectives—such as reducing churn by a certain percentage to improve customer retention. Clear goals enable tailored data science techniques, ensuring efforts are focused and aligned with desired outcomes, maximizing impact and effectiveness in addressing business challenges.

After defining goals, gather relevant data meticulously. Identify needed datasets, ensuring quality and completeness. Data collection entails navigating sources, accessibility, and potential biases. Remember, data quality directly impacts insight reliability. Dedicate time to cleaning and preprocessing data for robust analysis, ensuring a solid foundation for informed decision-making.

Now that your data is ready, it's time to look at it closely. Try to find patterns, strange things, or connections that can help you solve problems. Use stats and tools to show the data in pictures, which can help you see important stuff that you might miss just by looking at numbers

With prepared data, delve into thorough analysis. Seek patterns, anomalies, and correlations vital for problem-solving. Employ statistical methods and visualization tools to explore data comprehensively. Visualization, especially, unveils insights beyond raw numbers. This meticulous analysis ensures robust understanding and informed decision-making.

In model development, translate insights into actionable solutions. Create statistical models, machine learning algorithms, or simulations to predict outcomes or automate decision-making. Select the most suitable model for the problem at hand. Iteratively refine models based on performance against validation datasets to enhance accuracy. Adaptation ensures optimal solutions aligned with evolving needs.

Testing solutions is vital for real-world efficacy. Employ a separate test dataset to assess model performance, understanding strengths, limitations, and generalizability. Unacceptable results may prompt revisiting earlier steps for refinement. Thorough testing ensures robust solutions ready for deployment, bolstering confidence in their effectiveness and reliability.

Basically the fail fast approach ! DS ML is clearly all about trying simple approaches and failing often. You start with a baseline and go incremental with multiple approaches

Create a small chunk of data and apply the simple solution first.Gradually increase the complexity according to the use cases. In Data science, you can’t reach the final optimal solution in one go.

You can apply problem-solving skills in data science by breaking down complex problems, identifying patterns, and implementing effective solutions. For example, when faced with a dataset containing missing values, you can employ various techniques such as imputation or deletion to handle the missing data and ensure accurate analysis.