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By PPCexpo Content Team
Data drives decisions. Bad data leads to bad choices. That’s where exploratory data analysis makes a difference. It finds patterns, spots mistakes, and confirms if data is worth trusting before making big moves. Without it, businesses risk acting on misleading trends.
Think of a company losing money on marketing. Sales are down, and they don’t know why. Is it pricing? Customer behavior? Poor targeting? Exploratory data analysis finds the answer. By examining the data before making assumptions, businesses make smarter choices and avoid costly mistakes.
Skipping exploratory data analysis is risky. It’s like trusting a broken compass. Errors hide in datasets. Trends appear where none exist. Acting on flawed data means wasted time and money. Whether refining strategies, detecting fraud, or predicting trends, exploratory data analysis ensures decisions rest on facts, not guesswork.
Exploratory Data Analysis (EDA) is not just about making sense of data, but about asking the right questions. It’s like being a detective at the scene of an investigation: every piece of data can provide a clue that solves a larger puzzle.
By using techniques such as statistical summaries and graphical representations, EDA helps to confirm or reject assumptions. For example, it can reveal if certain variables are correlated or if there are any outliers that could skew the analysis. This phase is crucial because it directly influences the accuracy and effectiveness of subsequent data modeling.
The true power of EDA lies in its ability to turn raw data into valuable insights. This process involves more than just observing; it’s about interpreting data in a way that leads to real understanding.
Through methods like clustering and dimensionality reduction, EDA helps identify which variables have the most impact on your analysis. This insight is vital in many fields, such as healthcare where EDA might reveal trends in patient outcomes, leading to better treatment strategies.
In the world of machine learning, EDA establishes the foundation for predictive modeling. Before you can train effective models, you need a deep understanding of the underlying data. Think of it as laying down a solid foundation before building a house.
EDA’s role in machine learning is to ensure that the data used for training models is well-understood and properly prepared. This includes handling missing values, encoding categorical variables, and normalizing data.
Only with a thorough EDA can you ensure that the machine learning algorithms will perform at their best, making reliable predictions based on the data patterns identified during the exploratory phase.
First, gather your data—think of it as assembling all the pieces of a puzzle. Next, tidy your dataset; this means removing duplicates, correcting errors, and dealing with missing values.
Now, visualize your data using graphs and plots to see patterns you might miss in raw tables. Analyze these patterns through statistical measures like mean, median, and mode. Finally, interpret your findings to make them actionable. This straightforward approach helps prevent common pitfalls in data analysis.
Data science EDA is not just a preliminary step; it’s a core part of the analytics cycle that influences everything from data collection strategies to final decision-making. It ensures that the models you build are based on correct and relevant data, improving their accuracy and reliability.
Moreover, EDA informs the choice of tools and techniques for advanced analytics, making it a foundation for all subsequent steps.
One major pitfall is rushing into data collection without a clear plan. This often leads to irrelevant or incomplete data, which skews analysis and results. Another error is neglecting to clean the data, which can introduce bias and errors into your findings.
Also, avoid relying solely on automated tools; they’re helpful, but they can’t replace human intuition and expertise. Lastly, don’t ignore the results of EDA when moving forward. They’re crucial for guiding your strategy and ensuring your analysis addresses the right questions.
EDA is not just about looking at data; it’s about preventing costly mistakes before they happen. Imagine you’re about to invest a hefty sum into a new business venture. Without EDA, you’re walking blind. EDA acts as your business’s financial shield, identifying potential pitfalls and financial drains that are not immediately apparent.
By understanding trends and patterns, companies can sidestep investments that might look promising but are statistical sinkholes.
In today’s market, information equals advantage. EDA transforms raw data into a gold mine of insights, giving businesses the upper hand.
For instance, by analyzing customer behavior trend and market conditions, companies can craft strategies that are not only reactive but also predictive. This proactive approach allows businesses to stay two steps ahead of the competition, seizing opportunities and mitigating risks swiftly.
The return on investment (ROI) from EDA can be staggering. By informing strategic decisions, EDA minimizes risk and amplifies profitability.
For example, a retail chain might use EDA to determine the most effective store layouts or to tailor product offerings to consumer preferences, leading to increased sales and customer satisfaction. This strategic use of data not only boosts immediate financial returns but also enhances long-term business sustainability.
Toyota, a global auto manufacturer, uses EDA to hone its production processes. By analyzing assembly line data, Toyota identifies inefficiencies and areas of waste. This data-driven approach allows for precise adjustments to production practices, reducing costs and enhancing product quality.
The outcome is a more streamlined operation that not only saves money but also boosts output without compromising quality.
A Mekko chart, also known as a Marimekko chart, is an efficient tool for showcasing how costs distribute across different business segments during EDA. This visualization aids leaders in pinpointing where investments in data analysis yield the most significant financial impact.
By breaking down costs visually, decision-makers can better allocate resources to areas where EDA can drive substantial business improvements.
The following video will help you to do Exploratory Data Analysis in Microsoft Excel.
The following video will help you create a Sankey Diagram for Exploratory Data Analysis in Google Sheets.
The following video will help you to do Exploratory Data Analysis in Power BI.
Google Sheets is among the popular go-to data visualization tools for professionals, business owners, and those exploring business research methods.
However, it lacks ready-to-use charts for EDA methodology in its library. In other words, you have to invest extra time and energy to edit charts to align with your data story.
Yes, you read that right.
You don’t have to waste time editing charts.
You have the option to supercharge your Google Sheets with third-party add-ons to access ready-made and EDA-friendly charts.
We recommend you download and install an add-on called ChartExpo in your Google Sheets.
So, what is ChartExpo?
ChartExpo is a super user-friendly add-on you can install in your Google Sheets to access ready-to-use and visually appealing visualizations for your exploratory analysis and Business Analytics needs.
This exploratory analysis-recommended tool also offers over 50 other ready-made and advanced charts to help you succeed.
How to install ChartExpo in Google Sheets?
In this section, we’ll cover the two main types of exploratory analysis, namely: univariate and multivariate analyses. You’ll also learn how to leverage ChartExpo to generate the best-suited charts associated with the main types of EDA.
Radar Chart
In this example, we’ll use the Radar Chart to visualize the tabular data below:
| Products | Months | Number of Orders |
| Face Cream | Jan | 80 |
| Face Cream | Feb | 99 |
| Face Cream | Mar | 93 |
| Face Cream | April | 80 |
| Face Cream | May | 70 |
| Face Cream | June | 65 |
| Face Cream | July | 85 |
| Face Cream | Aug | 90 |
| Face Cream | Sep | 80 |
| Face Cream | Oct | 75 |
| Face Cream | Nov | 65 |
| Face Cream | Dec | 80 |
| Skin Lightening Cream | Jan | 100 |
| Skin Lightening Cream | Feb | 60 |
| Skin Lightening Cream | Mar | 95 |
| Skin Lightening Cream | April | 75 |
| Skin Lightening Cream | May | 100 |
| Skin Lightening Cream | June | 60 |
| Skin Lightening Cream | July | 95 |
| Skin Lightening Cream | Aug | 75 |
| Skin Lightening Cream | Sep | 109 |
| Skin Lightening Cream | Oct | 80 |
| Skin Lightening Cream | Nov | 109 |
| Skin Lightening Cream | Dec | 75 |
| Beauty Cream | Jan | 50 |
| Beauty Cream | Feb | 55 |
| Beauty Cream | Mar | 51 |
| Beauty Cream | April | 40 |
| Beauty Cream | May | 45 |
| Beauty Cream | June | 30 |
| Beauty Cream | July | 39 |
| Beauty Cream | Aug | 45 |
| Beauty Cream | Sep | 56 |
| Beauty Cream | Oct | 39 |
| Beauty Cream | Nov | 48 |
| Beauty Cream | Dec | 44 |
Pareto Chart
In this example, we’ll use the Pareto Chart to visualize the table below.
| Products | Sales |
| Rouge | 1579 |
| Mascara | 1962 |
| Lipstick | 3654 |
| Foundation | 2578 |
| Powder | 4942 |
| Eyebrow pencil | 5561 |
| Eye shadows | 2961 |
| Nail polish | 4831 |
| Lip gloss | 8961 |
In this section, we’ll use the Grouped Column Chart to analyze the data set below.
Let’s dive in.
| Year | Internet Sales | Sales in Person | Sales via Phone |
| January | 1036 | 345 | 691 |
| February | 456 | 263 | 526 |
| March | 741 | 400 | 666 |
| April | 561 | 913 | 211 |
| May | 361 | 864 | 464 |
| June | 801 | 210 | 425 |
| July | 342 | 278 | 786 |
| August | 456 | 1357 | 304 |
| September | 1674 | 581 | 550 |
| October | 647 | 245 | 144 |
| November | 298 | 567 | 201 |
| December | 457 | 421 | 222 |
Double Axis Line and Bar Chart
We’ll visualize the data set below using the Double Axis Line and Bar Chart.
| Quartiles | Sales | Growth |
| Q1-19 | 7000 | 4.2 |
| Q2-19 | 7606 | 7.6 |
| Q3-19 | 7895 | 3.8 |
| Q4-19 | 8242 | 4.4 |
| Q1-20 | 8327 | 0.7 |
| Q2-20 | 8768 | 5.3 |
| Q3-20 | 9337 | 6.5 |
| Q4-20 | 9589 | 2.7 |
Messy data is like a hidden gremlin in EDA, wreaking havoc silently. It includes inconsistencies, duplicates, and errors that can skew analysis, leading to faulty conclusions.
For instance, if customer feedback data has duplicate entries, it might seem like more customers favor a product than they actually do. Recognizing and rectifying these issues early in the analysis ensures the integrity and usefulness of the data.
Transformation techniques in EDA refine raw data into a more suitable format for analysis. Techniques like normalization and standardization adjust data scales, while feature encoding transforms categorical variables into numerical formats.
These steps are vital because they bring uniformity and comparability to the dataset, enhancing the accuracy of the analysis tools applied later.
The phrase “Garbage in, Garbage out” is crucial in data science. If your input data is poor, your output will also be poor. EDA relies heavily on the quality of data.
Poor quality data can lead to misleading patterns and insights which, in turn, can lead to erroneous business decisions. Ensuring data quality from the start saves a lot of trouble and rework down the line.
In exploratory data analysis, univariate analysis acts like the magnifying glass of a detective. It scrutinizes one variable at a time. This focus can highlight usual and unusual patterns. Are most values clustered around a particular point? Are there extreme values skewing the data?
By answering these questions, analysts can prepare the data for deeper investigation.
Distribution patterns are the bread and butter of data analysis. They tell us where most data points lie and how spread out they are. For instance, if the data forms a bell-shaped curve, it follows a normal distribution. This shape is vital as it underpins many statistical tests and methods.
Choosing the right tool often makes or breaks the analytical process. Histograms are great when you want to see the shape of data distribution clearly. Box plots shine when you need to pinpoint outliers and understand the range of data values.
Meanwhile, density plots are perfect for observing the smoothness of data distribution, offering a clearer picture of where values are concentrated.
Tesla, a leader in electric vehicles, uses univariate analysis to ensure their batteries meet high standards. By examining individual battery cell performances through univariate methods, Tesla can quickly spot inefficiencies. If a particular cell frequently falls outside the norm, it might indicate a manufacturing flaw or material inconsistency.
The box and whisker plot is a hero when it comes to spotting outliers. This chart displays the median, quartiles, and extremes of data at a glance. In product testing, such as checking battery cells at Tesla, these plots can immediately highlight units that don’t perform as expected. This quick detection is critical in maintaining high-quality production standards.
By analyzing how variables correlate, machine learning algorithms can fine-tune their predictions, improving over time. This is crucial in fields like finance where predicting market trends can mean the difference between profit and loss.
The algorithms adjust based on the correlations they detect, making each prediction more accurate than the last.
The magic happens when these correlations reveal trends hidden in the raw data. For example, in retail, a strong correlation between weather and purchasing patterns might help predict spikes in demand for certain products.
By training on these insights, machine learning models become more adept at predicting future trends, making them invaluable tools for data-driven decision-making.
Distinguishing between cause and coincidence is vital. Just because two variables show a correlation does not imply that one causes the other. This is where statistical tests step in, helping to determine whether a relationship is likely due to chance or a reliable, causal connection.
For instance, ice cream sales and shark attacks are correlated because both happen more often in summer—not because one causes the other. Recognizing these nuances prevents incorrect conclusions and guides more accurate, reliable data interpretation.
Netflix uses EDA to transform viewer data into better recommendations. By analyzing viewing patterns and comparing them across millions of users, Netflix can suggest shows you might like with surprising accuracy. This isn’t just about watching trends; it’s about understanding what keeps viewers hooked episode after episode.
The data might reveal that fans of a sci-fi show tend to binge-watch late at night or that comedies are popular in certain regions. Netflix uses this info to tailor its content and recommendations, keeping viewers engaged and subscribed.
Imagine a scatter plot charting customer satisfaction against the number of service tickets. Each point represents data from one month. If higher satisfaction correlates with fewer tickets, you’d see points clustered in a downward trend from left to right.
This simple visualization helps businesses quickly grasp the effectiveness of their customer service at a glance, guiding decisions on where to allocate resources for improvement.
Overlooking outliers in data can skew your analysis, leading to wrong conclusions. Imagine you’re calculating an average salary in a group. If most earn around $50,000, but one earns $1,000,000, the average isn’t very helpful, right?
Outliers can hide true trends and patterns, making your data analysis less accurate.
When searching for anomalies, several techniques shine. Box plots show data distribution, highlighting outliers visually. The Z-score method identifies data points that are too far from the mean.
The IQR (Interquartile Range) technique focuses on data dispersion, flagging numbers outside the 1.5 IQR range as outliers. Using these methods ensures anomalies don’t go unnoticed.
Deciding how to handle outliers depends on their impact and origin. If they result from errors, removal might be best. However, if they’re true values, consider transforming them using scaling or log functions. Sometimes, keeping them offers valuable insights into data variability and real-world complexities.
Financial institutions rely heavily on EDA to spot fraudulent activities. They analyze patterns and trends in transaction data to identify outliers. These outliers often indicate fraud. By continuously monitoring and analyzing transactions, banks can quickly spot and address these issues, protecting both their interests and their customers’.
Dot plots provide a clear view of transaction frequencies over time. They make it easy to spot spikes in data, which could indicate fraud. For example, if a customer typically makes small purchases, a sudden large transaction might be a red flag. Dot plots help in visualizing such anomalies clearly and effectively.
It’s easy to fall into the trap of seeing two things move together and assuming one causes the other. Remember, just because ice cream sales and shark attacks both increase in the summer doesn’t mean one causes the other!
Always question your findings and look for hidden variables that could be influencing the results. This keeps your analysis sharp and reliable.
Overfitting happens when your model is too complex, capturing noise instead of the signal. This can lead to conclusions that don’t pan out in real-world scenarios. To avoid this, simplify your model and use techniques like cross-validation.
This helps ensure your conclusions are robust and not just a fluke of your specific dataset.
Bias can sneak into data analysis from many sources, from the way data is collected to the way it’s analyzed.
Confirmation bias, for instance, is a common pitfall where analysts subconsciously favor data that supports their existing beliefs. Combat this by seeking out disconfirming evidence and getting input from others who might see things differently.
A slope chart is a fantastic tool for comparing changes in correlation strength across different datasets or time points. By plotting the strength of relationships at two points, you can quickly see how relationships have changed.
This visualization makes it straightforward to spot increases or decreases in correlation, providing clear, actionable insights into your data.
In EDA, selecting the right features is key. Machine learning models thrive on data that’s relevant. By identifying and using the most impactful features, models become more accurate without the noise of unnecessary data.
This process not only simplifies the models but also speeds up the training phase. How do we decide which features to keep? By using techniques like feature importance scores which highlight the most influential variables based on their effect on the model’s predictions.
Principal Component Analysis, or PCA, is a technique used to emphasize variation and bring out strong patterns in a dataset. It’s often used when you have many correlated dimensions.
PCA helps by transforming these dimensions into a set of linearly uncorrelated variables known as principal components. This method is most effective when you want to reduce the number of variables but retain the essential information.
The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces. To avoid this, dimensionality reduction techniques are crucial. They help in reducing the number of random variables under consideration, by obtaining a set of principal variables.
Techniques like PCA are vital as they trim down the excess dimensions without losing critical information, thus simplifying the model and ensuring better performance.
Pharmaceutical companies leverage EDA to speed up drug discovery. By analyzing biological data, they identify key features that contribute to drug efficacy. This data-driven insight allows them to focus their experiments on promising compounds, reducing time and cost.
EDA tools enable them to visualize complex interactions between biological entities, which simplifies decision-making in the early stages of drug design.
A Radar Chart is a useful tool for visualizing the importance of different features in predictive models. It plots one ‘spoke’ for each variable, and the length of each spoke represents the importance of that feature.
This chart is particularly helpful when you need to display multivariate data in a way that is easy to understand, allowing quick comparisons and better visual interpretation. This visualization aids in highlighting which features are playing significant roles in the model, guiding further refinement and analysis.
Automated tools streamline data analysis but come with drawbacks. On the plus side, they process data at a speed no human can match, allowing for rapid insights.
However, they might miss subtle patterns or make errors if not properly supervised. Users must balance speed with accuracy, ensuring automated tools are correctly tuned to the data they analyze.
AI and machine learning significantly boost EDA capabilities. These technologies can predict trends from data, learning continuously from new information. This means they get better over time at identifying data patterns, which can lead to more accurate and insightful analysis outcomes.
However, they require large amounts of data to learn effectively and can be complex to set up.
While automated tools offer speed, human intuition adds a layer of depth to data analysis. Humans can perceive context and subtleties that machines might overlook. The best results often come from a hybrid approach where analysts use automated tools to handle large data sets but step in to guide the analysis and interpretation where needed.
Google relies heavily on EDA to refine its search algorithms. By analyzing vast amounts of data, Google identifies which website features correlate with user engagement. This ongoing analysis allows continuous improvements to the search algorithms, ensuring they evolve with user preferences and behaviors.
A Sankey diagram shows the transformation of data through an automated EDA pipeline. It begins with raw data input, followed by pre-processing where data is cleaned and normalized. Next, the data undergoes various analyses—statistical analysis, pattern recognition, and anomaly detection.
Outputs then inform business decisions or feed into further learning cycles. This visualization helps in understanding how data moves and transforms, providing clarity on where bottlenecks or data loss might occur.
Imagine you’re looking at a spreadsheet filled with numbers. Without context, it’s just chaos, right? This is where EDA shines. EDA uses visual tools to turn these numbers into a story. You’ll see patterns, trends, and outliers. It’s like turning a confusing book into an exciting movie plot.
Think of a scatter plot. It shows you how two variables relate in a glance. Or a histogram that groups data to show distributions. Without these visuals, the raw data can seem meaningless. But with them, you suddenly “see” the story behind the numbers.
Choosing the right visual can make or break your data story. For quantitative data, box plots show you distribution and outliers. They are great for spotting which parts of your data are off the typical path.
For categorical data, Clustered Stacked Bar Charts are your best bet. They compare different categories at a quick glance. Need to show changes over time? Multi-axis line charts are perfect. They connect data points in a way that clearly shows trends up or down.
Remember, the goal is clarity, not just beauty. Each chart type serves a purpose. Match them wisely to your data story.
Visuals are powerful. But with great power comes great responsibility. Misleading visuals can distort the truth. They can be due to scaling issues, or by showing correlations that aren’t there.
Say you have a bar chart, but the y-axis starts at 50 instead of zero. This can exaggerate differences. Always check that your visuals are fair and represent the true story of the data.
Airbnb uses data visuals to tweak pricing strategies. One effective visual is the sunburst chart. It segments customer data into a colorful, layered ring. Each layer represents a category, like booking lead time or season.
This chart shows Airbnb how different factors play a role in pricing decisions. It helps them spot which features lead to a booking spike. Maybe last-minute bookings in July cause a surge? With this insight, Airbnb can adjust prices dynamically.
Using visuals like the sunburst chart, Airbnb turns complex data into actionable strategies. This not only boosts profits but also enhances customer satisfaction. They ensure prices are fair and competitive, all thanks to smart data visualization.
In the thrilling world of data, EDA acts as your flashlight in a dark cave. It helps reveal patterns, anomalies, and insights by sifting through mountains of data. But once these gems are uncovered, what’s next? The real challenge lies in transforming these insights into decisions that drive your business forward.
Imagine you’re a detective with all the clues laid out. Your next step isn’t just to acknowledge these clues but to piece them together into a strategy that solves the case. Similarly, after performing EDA, your role shifts from data explorer to strategic decision-maker. You start by identifying which insights have the potential to impact your business significantly and then prioritize actions based on this potential.
Deciding what to act on first can feel like standing at a crossroads. The key is to categorize your insights based on urgency and impact. Start with changes that can significantly boost efficiency or profits with minimal disruption.
For example, if EDA shows that a minor tweak in your production line could reduce costs by 20%, it’s a no-brainer to prioritize this insight.
Consider this scenario: if your analysis uncovers that 90% of customer complaints stem from just 10% of your services, focusing on improving these problematic services could dramatically enhance customer satisfaction and retention. It’s all about smart choices that pack a punch!
Now, let’s kick things up a notch by marrying EDA with predictive modeling and machine learning. This integration is like adding a supercharger to your car—it boosts your capabilities to predict and prepare rather than just react.
For instance, if your EDA reveals seasonal trends in sales, predictive models can forecast future demand more accurately, allowing better stock management and marketing strategies. Machine learning can take this further by continuously learning and improving these predictions as more data becomes available. It’s a dynamic duo that keeps you steps ahead of the competition!
Waterfall charts are fantastic tools for visual storytellers. They transform abstract numbers into a clear narrative about your business’s financial journey. Let’s say you implement a series of changes based on your EDA findings. A waterfall chart can visually break down how each change contributed to an overall increase or decrease in revenue.
Picture a chart where each bar represents a change—like reducing downtime or improving marketing ROI—and shows whether it pushed your revenue up or down. This visual approach not only makes the impacts clear and digestible but also highlights the cumulative effect of all the changes. It’s like watching your business climb a staircase of growth, step by step, with each bar a solid footing that boosts or reduces your ascent.
Think of EDA as your business’s routine health check. Just as regular doctor visits keep a person healthy, continuous EDA keeps your business strategies sharp and effective. Why settle for a snapshot when you can have the entire album?
Making EDA a habit ensures you’re always aware of underlying trends and subtle shifts in your data landscape. This practice not only helps in identifying immediate opportunities but also flags potential risks before they evolve into real problems.
It’s like having a financial advisor who constantly updates your investment strategy based on market conditions.
In a world where market dynamics shift rapidly, having a future-proof strategy isn’t just nice—it’s necessary. EDA acts as your crystal ball, helping predict and prepare for future trends. By routinely analyzing data, you can fine-tune your business strategies to be more resilient to market changes.
Imagine being able to anticipate a major trend before it hits the mainstream. That’s the power of ongoing EDA—it turns data into a strategic foresight tool, giving your business a competitive edge that’s hard to match.
Machine learning and EDA are becoming an inseparable duo in advanced analytics. With machine learning, EDA is no longer just about understanding what has happened; it’s about predicting what will happen next. This combination allows businesses to move from reactive to proactive analytics.
Think of it as upgrading from a regular camera to a high-definition video camera that captures every detail in vivid clarity. Machine learning models thrive on data, and EDA feeds these models the right data in the right form. This synergy not only accelerates the analytical process but also increases the accuracy of insights derived, making every strategic decision backed by solid data evidence.
Each of these aspects shows why EDA is not just a tool but a vital part of the decision-making process. By embedding EDA into your regular business practice, you can turn data into one of your strongest strategic assets.
It’s not just about having data but about continuously interacting with it to extract value that not only supports but also enhances your business decisions.
EDA is not optional. It is the first and most important step in working with data. It uncovers patterns, flags errors, and confirms assumptions before decisions are made. Skipping it leads to flawed models, wasted resources, and costly mistakes.
Raw data is unreliable. Without cleaning, visualizing, and summarizing it, businesses risk basing strategies on noise instead of facts. EDA provides the foundation for accurate forecasting, AI models, and data-driven decisions.
Visualization matters. Charts and statistical summaries turn numbers into clear insights. Stakeholders grasp findings faster, teams align better, and mistakes get caught early.
Data tells a story, but only if you ask the right questions.
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