How can you identify patterns and trends in data?

A goal in data analysis acts as a compass, guiding your exploratory journey. In my experience, well-defined goals often align with business objectives or scientific questions and should be revisited frequently during the analysis. Sometimes, insights uncovered during the analysis lead to refinement of the initial goals, allowing for a more targeted approach.

Each data point is a piece of a story or a bigger picture you're trying to form. Begin by segmenting your data into relevant categories. Are there specific demographics, time periods, or variables that stand out? Organize your data in ways that mirror your objectives. Visual tools, such as heat maps, scatter plots, or histograms, can be invaluable in revealing hidden patterns. As you delve deeper, maintain a critical mindset. Question anomalies and don’t assume correlations imply causation. Most importantly, revisit and refine your approach periodically. As you get familiar with your data, fresh insights may emerge, guiding you to a clearer understanding of the narrative it tells. Data analysis is as much an art as it is a science.

To find patterns and trends in data, you should first know what you're looking for. Start by deciding what you want to learn or understand. This is your goal. Once you have a clear goal, it becomes easier to spot relevant patterns that will help you achieve it. Think of it like going on a treasure hunt: if you know what treasure you're searching for, you'll recognize it when you see it.

The main goal is to implement everything we study each day in the real world, finding patterns in everything we observe in our surroundings. For instance, during exploratory data analysis (EDA), sometimes one method or visualization isn't sufficient to provide insights compared to another method. Thus, we must learn multiple techniques to uncover the best patterns. We break down this journey into various checkpoints to ensure we reach our goal effectively.

Being flexible with potential outcomes makes for better, less rigid interpretations of gathered data. Being too committed to the expected outcomes can influence how you respond to the data you collect. Hypotheses shouldn’t be set in stone, but loose-ish guides to what you can discover.

A great way to get started analyzing your data (once you've defined your goal for your analysis) is to summarize what you have. I look to answer some of the following questions: how many variables am I working with, how large is the dataset, are there any extremes, are the mean/median values of my numeric data fields similar or wildly different, what do the quartiles tell me, what about my min. and max. values? Once I have reviewed the summary results, I can dive deeper to glean more insights. I can choose to look into surprising or unusual results, or I can validate expected results. Then, I can start building more complex analytical models to learn more about my dataset.

Once the goal is firmly established, the next step is to address two main questions: 1) What do you currently possess? 2) What do you require? Upon obtaining the answers, it's crucial to assess whether you can achieve your goal with the available data sources. This evaluation guides you in selecting the most suitable and minimal data sources necessary to achieve your objective.

Think of every new column added to your dataset as an unique way to see the question you want to solve, that's why they're often called "features": It presents an opportunity to add valuable insight to your analysis. Do you want to explain a trend over time? A date is a must! And do you need to classify patterns along your clients' historical purchases? A date column may be included in your original source but for the initial clustering it could be optional to include. Remember: More data is good, but right amount of data is better.

To process data from different sources, we’ve to normalise our data format and making it uniform to process on our platform.

In the realms of genomics, cell biology, and ecology, the choice of data type and source is pivotal based on the specific objective at hand. In genomics, sequencing data from technologies like Next-Generation Sequencing can be sought to decode genetic information, while cell biology might call upon microscopic imaging data to study cellular structures. In ecology, data from satellite imagery or field surveys provides insights into species distribution and habitat changes. Data might be structured, like organized genomic databases, or unstructured, such as raw ecological field notes. Prior to deep analysis, one might often find the need to refine, alter, or amalgamate different datasets to yield meaningful conclusions.

Best approach to build usable data visualization is to reverse engineer the task. Start by jotting the business queries in focus. Next group the queries: time axis, business vertical axis, user persona axis, etc. Once the business requirements are identified, start with data partitioning using logical set of filters to create mutually exclusive sets. Operate on numerical and categorical values, treat the null values and perform transformation to bridge gaps between min to max of respective data values. Secondary Axis and data superimposition are clever techniques to make visualization simple.

The best way to visualize your data arguably depends on the nature of your data and the insights you're looking to gain. Common visualization methods include histograms, scatter plots, box plots, line graphs, and heatmaps. Choose the visualization that effectively communicates the patterns, trends, and relationships within your data. Make sure you articulate your results in a straight forward digestible manner. Always know thy user!

It is usually helpful to split the exploration and visualization of numerical and categorical variables. Numerical features lend themselves to be analyzed using histograms, box plots, bivariate scatter plots. On the other hand, bar charts, area charts and pie charts can capture insights from categorical attributes. If the problem is one of classification, you can facilitate feature selection and engineering by juxtaposing visualizations for each value of the target variable across all numerical and categorical features.

Time is a valuable and scarce resource, and computer resources are meant to be used as optimal as possible, but the fun part of modern analysis tools is the possibility of visualize and explore data in a matter of seconds. Reaching your goal also includes understanding the most of your dataset, and the only way to achieve it is by try every possible relation and insight hidden inside your data. Hands-on Learning also applies when working serioursly.

In details, organizations most commonly use below methods to identify patterns: Clustering: Grouping similar data points together to discover natural patterns in unlabeled data. Natural Language Processing (NLP): For text data, NLP techniques can be used to extract patterns and sentiments from text documents. Pattern Mining: Discovering frequent patterns, associations, or sequences in large datasets. Data Mining: Utilizing data mining techniques to uncover hidden patterns and trends. Time Series Forecasting: Using historical data to make predictions about future trends.

One important aspect in pattern mining is comparison of trends with peers. Say, there are 5 companies in an industry that can be considered benchmark for a company (company X). Due to some external market conditions (macro-economic or otherwise), growth/ revenue/ net income of all these companies declines in a quarter. But, company X's parameters are stable (zero growth). Without taking into consideration the overall competitive market within which company X is operating, it may appear it has registered 0 growth, but within its environment, when every other company's performance declined, company X has actually grown. Same is true for the opposite conditions. Considering this aspect of historical pattern is important for future projections.

Even when statistical programs and frameworks have built functions and libraries with all the "dirty job" (mathematical principles) done, it is important for you to understand what does every method do, what are they pros and cons, and more important, when you can use them or not. Trial and Error may be good if you're getting things done for the first time, over time you'll be able to think of more complex methods that fit your goal.

There are few additional Important things we need to keep in our mind - Data Quality: Ensure that the data you're analyzing is clean and free from errors. Missing, duplicate, or incorrect data can distort the patterns you might identify. - Use Graphs for the visualization and exploration: Charts and graphs can help see patterns. -Statistical Analysis: Use statistical methods to confirm the trends and patterns you observe. - Look at Different Granularity Levels: Sometimes you need to zoom in or out to see patterns. - External Factors: Things like holidays can change your data. - Talk to Others: Get other people's opinions on what you find. - Look Over Time: Make sure the trend happens for a while, not just once.

I begin by clearly articulating the business questions that I want the data to answer; this focuses the analysis. I then Understand the context which the data was acquired. To examine datasets and detect trends, I rely on visualizations such as histograms, scatter plots, time series charts, and so on. I can drill down on topics of interest using interactive visualizations. I depend on statistical testing assists in quantifying the dependability of apparent trends and avoiding misleading conclusions. At the end, results must be communicated effectively through data stories/images, examples, and clear explanations to give stakeholders with practical, business-driving insights. The process of analysis is both art and science!

The ability to data story tell will be critical. It must sequence the complete life cycle through its final state of consumption. It will start from 1. Problem statement 2. Data sources / EDA 3. Approach to the Modelling 4. Results/ validations/ consumption/ metrics 5. Monitoring / transparency / explainability XAI #LCA #DataScience

Storytelling i.e., weaving your findings from a data exploration and analysis project into a relatable story built around your meaningful and stragegically placed visualizations can enhance the impact and engagement of your results many fold.

Cleaning your dataset could take 50% of the project's total time, but it is inferred into your pre-processing tasks, so instead of a step-by-step explanation of what you did, consider taking advantage of your knowledge about the data and the results and go straighforward to what your audience want to know. It is great to save some context when asked for it and highlighting issues regarding future data process and tasks.

Technology and methodologies evolve, but the essence of data science remains understanding and extracting meaningful patterns. Balancing complexity with interpretability, considering ethical implications, and fostering a culture of curiosity and continuous learning have been central themes in both my industry and academic experiences.

As a Healthcare professional looking after patients seems more about risk aversion that better health outcomes.We make instant decisions on a human level without reference to all data possibilities as revealed in this article.Sometimes its just not the culture to explore and demonstrate the power and vision of of real time data analysis. But it has a point that if AI eases the burden let's use it ,Co operate! Definitely will get better patient outcomes and reduced cost. Most Healthcare systems are really 'paying agent's' include them as stakeholders. Reduce costs and get better outcomes at the same time.Shift the goalposts.

Once you have your data(definitely from right source and method), you already have an idea or goal, why this data? Now its time to figure out what could be most relevant features that you might need for that goal. Do data cleaning and start working on those features. Once you have gained an insight what your data is trying to tell use specific graphs and plots to confirm your hypothesis. Lastly share your results in some BI tools.

For effective data science endeavors, a tripartite approach is essential: prioritization, implementation, and measurement. These three pillars are paramount to remember when working with data and collaborating with stakeholders. Our role extends beyond mere management; it's about providing data-driven insights that resonate with the key players. Our value lies in offering profound perspectives on the future.

Align the patterns and trends in the data with domain knowledge (from yourself or a domain expert). Do the patterns make sense given the domain? If yes, great. If not, you might need to revisit the data / techniques. When communicating the results, try to link the patterns back to known phenomena / or numbers in the domain, to assist with getting buy-in.