How can you improve your problem-solving skills with multiple data sources?

The problem that you're trying to solve is the foundation of the data you must use. We must think beyond the problem… think outside of the box as it relates to causation and correlation to the problem itself. One problem I solved involved Heat Pump Water Heater Technology. The company faced what is called Simpson’s Paradox and could not understand why all HPWH in a study appeared to perform the same based on an avg calculation. SP is a statistical phenomenon that occurs where you may have a trend appear within several groups of data that disappears or reverses when combined. I was able to uncover many different hidden variables and factors. Through deconstruction and adding relevant variables we solved the problem. #ai #data

The art of comprehending is the first step toward mastering problem-solving using numerous data sources. The secret is to approach the difficulty with a razor-sharp focus on understanding the complexities of the issue. 'Understanding the Problem' (step no. 1) is the first step that will open the door to improved problem-solving abilities, particularly when working with a variety of data sources. In data science particularly, after getting familiar with problems faced, the next stage is exploring the available data to derive insights on how to best to solve the problem. #DataScience #machinelearning

Some ways to get started: 1. Understand the business problem and the end goal that you are trying to achieve. 2. Break down the complex problems into smaller manageable chunks. 3. If data is coming from multiple sources, identify the relation among the data points. 4. Bring all the data points at a same granularity. 5. Perform Exploratory Data Analysis and find co-relation amongst the data points. 6. As our knowledge would be limited, we may not know all the possible ways to interpret the data hence, read good notebooks on websites like Kaggle. This would give you a new direction to tackle the problem, leading to continuous learning and growth. 7. Collaborate with your team and teach them a new concept that you have learnt.

One thing I've found helpful is to examine the metadata and granularity of the dataset. Sometimes, terminologies or definitions may vary from dataset to dataset. On the other hand, newer datasets may provide more granular data. Datasets are often prepared for a specific purpose under a budget, timeline, logistics, personnel. Quality of data may vary from dataset to dataset. Data collected for one purpose may not be readily suitable for another purpose. One point to ponder may be whether the data was measured or collected using the right set of parameters or questions in the appropriate sequence or conditions?

Improving problem-solving skills with multiple data sources involves developing a structured and analytical approach. Here are some strategies, 1. Define the Problem Clearly: - Start by clearly understanding and defining the problem. Identify the key issues and objectives you need to address. 2. Gather Relevant Data: - Collect data from various sources that are relevant to the problem at hand. This may include quantitative and qualitative data, reports, surveys, and expert opinions. 3. Verify Data Quality: - Ensure the accuracy and reliability of the data. Cross-verify information from different sources to minimize the risk of relying on inaccurate or biased data.

Combining creative and philosophical thinking has given me an edge when solving complex problems. Part of this process is exploring data using data science methodologies such as data mining and data integration; this is where we combine multiple data sources that have an association to the problem in order to discover unique patterns. Even prior to that, exploring data sources and brainstorming is essential when choosing data and which factors you want to include in your study. There are so many parts... When implementing EDA we can use statistical summaries, data visualization as well as feature engineering and selection. There are also many mathematical and statistical methods we can explore to uncover hidden gems. #ai #data

EDA is one of my favourite steps of the data science workflow. It’s a time to gain information on how you can best utilize your data. It’s also your first layer of insights that you can begin to build off of. This phase may create a lot of questions too, if you’re not the subject matter expert in the domain where the data sources are coming from make sure you ask!

To enhance problem-solving with multiple data sources, start by individually examining each source. Use statistical analyses and visual representations to understand trends, anomalies, and correlations. This helps identify the data's nature, including biases and limitations. Then, integrate these sources, looking for commonalities and differences. Techniques like data fusion and triangulation are vital here, as they merge findings and validate conclusions. This approach broadens perspectives and strengthens decision-making by combining diverse data insights effectively. The focus should be on insightful interpretation and integration of data, rather than mere collection.

EDA is one of my favourite steps of the data science workflow. It’s a time to gain information on how you can best utilize your data. It’s also your first layer of insights that you can begin to build off of. This phase may create a lot of questions too, if you’re not the subject matter expert in the domain where the data sources are coming from make sure you ask!

There is no shortcut to exploring a data source. A human needs to understand the data in a deep way in order to draw real conclusions from it. This means examining as much of the metadata about a dataset as possible while gathering context & subject matter expertise. We've sourced over 22,000 data providers over the years and curate over 200 distinct fields to properly profile a dataset. Profiling is just the first step. You need to make sure the data makes sense and that typically means digging deep into the documentation, the data dictionary and, at the very least, a snapshot of what the data product looks like.

Cleaning data is an often underplayed aspect of data processing and analysis from those not in the field. Clients will often describe their data as "clean" or "well-organized". However, anyone who has working on real-world projects knows that this almost never true. Ensuring that your data pipeline as measures in place to clean and transform incoming data as well as detect unaccounted for issues is critical to any analysis. The saying "garbage in, garbage out" is common for a reason. Not properly preprocessing your data can lead to unintended consequences and incorrect conclusions.

A technology like databricks or snowflake can be key to solve this challenge. To often companies have to many different systems which are not connected to each other so its very hard to get real insights on data-science and ai projects while only seeing a small portion of the truth. Get your data-engineering right and you can do the shiny object things.

This a very crucial stage in leveraging data from across multiple sources. You must consolidate the data and make them consistent across the sources before you can leverage them to make them analytical worthy.

Before analyzing data, cleaning, and integration are vital. Cleaning tackles missing values, errors, format standardization, and outlier/duplicate handling. Integration means merging diverse sources, resolving conflicts, and ensuring data harmony. Selecting integration tools that offer pre-built connectors, real-time data syncing, and an easy-to-use interface is helpful. This step significantly boosts data accuracy, reliability, and usability, setting a solid base for further analysis.

Too easy! After figuring out linkages and the why to the datasets and their relationships, it’s time to join them as many ways as possible. Your problem solving will level up as you determine which join is most appropriate and, downstream, how best to present this ecosystem to leadership and stakeholders. This is the time to flex your engineering, vis, and stewarding skills Do the Hokey Pokey of joins.

Analyzing the data is where the puzzle pieces come together. It's not just about applying statistical methods or machine learning algorithms but understanding the story the data is telling. In my experience, this involves a dynamic process of iteration—testing hypotheses, refining approaches, and adapting to unexpected insights. It's crucial to choose analysis techniques that align with the problem's nature, ensuring that the results are not only accurate but also actionable. Balancing the technical aspects with a keen interpretative eye transforms raw data into actionable intelligence.

Data analysis, for me, is a constant dialogue with the unknown. Each problem whispers clues, and it's our job to listen carefully. Statistical models serve as translators, deciphering hidden patterns. Algorithms, like nimble brushes, paint a clearer picture. But rigor is the guiding light, ensuring each step is grounded in solid methodology. Every solved puzzle, every unearthed truth, is not a personal victory, but a shared revelation, a step closer to understanding the world around us. In this ongoing conversation with data, elegant equations are not just tools, but bridges - connecting the known to the unknown, the seen to the unseen.

Com uma mentalidade de aprendizado contínuo e determina??o para enfrentar desafios complexos, acredito que posso melhorar significativamente minhas habilidades de resolver problemas com várias fontes de dados. Na minha opini?o, devo praticar análise regularmente, aprender técnicas e ferramentas relevantes, comunicar insights de forma clara, colaborar em projetos reais, manter-me atualizado e cultivar uma mentalidade de aprendizado contínuo e determina??o para enfrentar desafios. Eu também devo dominar ferramentas de análise de dados, como Python, R e Excel, para manipular e analisar dados de forma eficaz.

Reading data without using non bias analysis can steer you in the wrong direction and cause you to see trends that are not there. During the analysis phase it is important to use the best approach to meet your organizations needs. Don't be afraid to ask questions and make informed predictions to be more proactive in possible setbacks.

Analyzing data from multiple sources is a bit like being a detective. You’re sifting through clues, looking for patterns and connections that others might miss. Dive into those spreadsheets, databases, or whatever form your data takes. It’s about transforming raw numbers and figures into a form that’s ready for deeper analysis. Next, consider the different angles. Each data source might tell a different part of the story, and it’s your job to piece them together. It’s like listening to different witnesses in a case – everyone has a unique perspective. This is where your ability to think critically is crucial. Good data visualization can turn complex datasets into clear, understandable insights. This ensures data to be understood by all.

One thing I found helpful is to seek specific feedback from stakeholders. You can do better than asking generically, "Do you have any feedback?". Ask them more specific targeted questions such as, "What items were essential for them?" or "What one item would they choose to omit if they must choose?" Seek this feedback and you will better understand the needs of each stakeholder in your audience, build relationships, and create more effective messages while improving communication skills.

To enhance problem-solving skills with multiple data sources and effective communication: Clarity in Objectives: Clearly define the problem and set specific objectives to guide your analysis. Data Exploration: Thoroughly explore and understand the data sources, identifying key variables and patterns. Structured Approach: Organize your problem-solving process with a structured methodology, such as defining hypotheses and testing them against the data. Visualization Techniques: Utilize visualizations to communicate complex findings. Charts and graphs can make data more accessible to diverse audiences.

When communicating results from a multi-source data analysis, clarity and simplicity are key. Use clear and concise language, avoiding jargon to ensure your audience, regardless of their technical background, can follow along. Visual aids like charts and graphs can be powerful tools here, turning abstract numbers into tangible visual stories. It's also important to tailor your communication to your audience. For a technical team, dive into methodologies and data nuances. For business stakeholders, focus more on actionable insights and how the data impacts business decisions. Remember, the goal is effective communication – it’s not just about what you say, but also about how it’s understood.

Communicating the results is both art and science. Visual communication may be very effective. I am a fan of the books written by Nathan Yau of Flowing Data, namely, Visualize This: The FlowingData Guide to Design, Visualization, and Statistics and the other one is Data Points: Visualization That Means Something. Communicating Data and Complex Ideas may take some time to develop, but is very rewarding. It is definitely worthy of the investment.

embracing a growth mindset is crucial in problem-solving. It's not just about mastering existing skills but actively seeking new challenges and learning from them. Whether it's staying updated on emerging technologies or diving into different domains, continuous improvement is key. Feedback loops, both internal and external, have been invaluable in refining my problem-solving approach. The evolving nature of data science demands a commitment to perpetual learning, ensuring adaptability and resilience in tackling diverse and complex problems.

making the data "consistent" across the sources - this one thing was missing from the above list - say for example Unit of Measure could be different for different datasets from different sources - so to be able to consume them you should bring in the data consistency

Let's not view learning and professional development as taking time away from real work. Instead, that thinking must change and ensure everyone on our team sees learning as part of the job. The time spent learning and developing skills will enable the organizations to compete successfully in today's global marketplace, and to thrive, they need to be innovative, adaptive, and ever-changing.

Playing with different types of data, and specially multi-modal data, may enhance learning significantly. The exposure to a wide variety of data and problems contributes to developing a strong problem-solving skills and critical thinking.

One thing I've found helpful when deciding the best way to illustrate the data is to know your audience. Some may prefer PowerPoint, while others excel or Tableau. Some audience will appreciate the ability to get a copy of the presentation for further review in house.

Exploring and Analysing goes hand in hand. Sometimes while analysing, e.g. it feels like what could happen if these features are clubbed together in this fashion (using domain knowledge). And voila! we got a very strong relationship to our initial hypothesis. That's why I think that these processes are iterative cyclic processes and things only tend to improve with time

This may go under understanding, but what if your problem isn't a data problem? We often jump to how data can solve a problem, but a real-world understanding is needed first. As it goes in the classic joke: A QA tester walks into a bar. Orders a beer. Orders 2 beers. Orders 0 beers. Orders 1000 beers. Orders -1 beers. Testing complete. Then a real customer walks in and asks where the bathroom is. The bar bursts into flames.

It might be helpful to keep monitoring the utilized datasets to see if a revised or updated version of the dataset was made available, perhaps after corrections or as a more enriched version which may affect the analysis. On that note, reproducibility is an important concern. Utilzing scripts at all stages and sharing that script may be important. In general, following FAIR and CARE principles is recommended.