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  • What is Exploratory Data Analysis in Data Science


    What is Exploratory Data Analysis in Data Science

    Exploratory data analysis (EDA) is a data science technique that helps researchers identify potential insights and patterns in their data. This Exploratory data analysis can be used for a variety of purposes, such as exploring relationships between variables or investigating the effects of a change on a specific set of data. 

    The key to successful EDA is using a systematic approach and tracking all the steps in your analysis.

    The term “exploratory analysis” refers to methods used to analyze data without having a specific hypothesis in mind. Often this type of analysis is referred to as exploratory because it gives you the flexibility to look at data without knowing exactly how it is going to be analyzed. For example, if you want to find out whether there are differences between men and women, you could compare the ages of people in each gender group. If you don’t know about any potential differences, you’ll have no way of knowing if you’re seeing something significant or just random noise.

    In contrast, a hypothesis-driven approach involves identifying a particular question you’d like to answer and gathering evidence to support or refute that idea. Once you’ve identified a problem, you can use statistical tests to see if there are statistically significant differences between groups — meaning that the difference is likely due to chance rather than being caused by some factor related to the variable under study.

    If you’re interested in learning more about Data Analysis, check out our Data Science Course in Chennai for an overview of the topic. FITA Academy helps you to build your skills in data science and analytics.

    Exactly what is exploratory data analysis?

    The idea of exploratory data analysis, or EDA as it is commonly referred to, is not new. In fact, it was first proposed by James Wilder Tuley in 1977. But, despite being around for over 40 years, EDA hasn’t really taken off. This is mainly because of how different it is from traditional data analysis.

    EDA primarily starts with an objective or a particular business goal. Analysts then use the collected data to reach conclusions that support the business goals. For example, if you’re trying to determine whether or not a certain product is selling well, you’ll look at sales numbers to see what products are doing better than others.

    In contrast to traditional data analysis, where the analyst tries to answer questions like “what does this mean?” or “why did this happen?”, EDA focuses on answering questions like “how do we interpret this data?” or “what conclusion can we draw from this?”.

    When you gain knowledge through expert guidance with our Data science Tutorial, you can obtain confidence in tackling complex situations by applying data science skills.

    Data Analysis Types

    Studies require data analysis as one of their most important components. You want to make sure that you do it correctly. If you don’t analyze your data properly, you could end up getting incorrect conclusions. This is why there are different kinds of data analysis. These include univariate, bivariate and multivariate analysis. Taking a look at each type of data analysis will help us better understand it.

    1) Univariate- Univariate analysis is the simplest type of data analysis. It involves analyzing data that has only a single independent variable. For example, you might want to know how many people visited your site during each month of the year. Or, perhaps you’re interested in finding out what percentage of visitors come from mobile devices versus desktop computers. In both cases, you’d use univariate analysis.

    A histogram is the most common way to visualize data collected via univariate analysis. A histogram displays the frequency distribution of values associated with your data set. You’ll see examples of histograms throughout this course.

    2) Bivariate – A scatter plot is a visual representation of data points where each data point represents a single observation. Bivariate analysis is performed by plotting the values of two variables against each other. In the case of a scatter plot, the value of one variable is plotted along the horizontal axis while the value of the second variable is plotted along the vertical axis. This allows you to see how the two variables correlate with each other.

    The most common type of bivariate analysis is the Pearson correlation coefficient. However, there are many others, such as Spearman rank correlation or Kendall tau correlation.

    3) Multivariate Analysis – Multivariate analysis involves analyzing the relationships among several variables simultaneously. When you want to know how different factors affect one another, it makes sense to look at the entire picture at once. For example, imagine a person with a high IQ score, low body weight, and high blood pressure. You might wonder whether his high IQ score causes his low body weight or vice versa. A multivariate analysis helps answer such questions.

    The multivariate analysis includes looking at three variables at once. Suppose you wanted to see how well students in a class performed on exams compared to their grade point average (GPA). In this case, a Multivariate analysis would include both grades and exam scores.

    From the above Data Analysis Types, you can see that there are multiple combinations. From our Data Science Online Course, you can get a better understanding of each of these types and their importance.

    Key Components in Exploratory Data Analysis

    Exploratory data analysis (EDA) is a method used to analyze large amounts of data. In this process, you’ll use statistical methods to discover patterns within the data. This helps you understand what information is most important to your audience. You might want to find out how many people like each type of product, where customers live, whether certain products sell better during different seasons, etc. There are several key components involved:

    An understanding of variables

    Almost all data sets contain variables. A variable is anything that affects something else. Think about variables like the color of a shirt, the size of a room, or the speed of a train. Each of those things affects another thing. In this case, the “thing affected” could be the price of a house, the number of people living there, or the amount of money spent on groceries. These are examples of variables.

    The importance of variables varies depending on what type of analysis you want to do. If you want to find out whether certain types of houses sell faster than others, then you’ll likely use variables such as square footage, age, and neighborhood. But if you want to know why some companies earn more profit than others, you’ll probably look at variables such as sales volume, revenue per employee, and customer satisfaction scores.

    Dataset cleanup 

    • Cleaning up the data begins once you have a clear understanding of what you want.
    • Once you’ve identified the variables, you’ll want to go ahead and remove those that don’t seem relevant. In our used car example, we know that the model number isn’t really useful because it doesn’t change the price. You’d probably rather look at the horsepower and transmission type.
    • In general, you’ll want to keep the most important variables around. But sometimes, you might find yourself having too many variables or ones that aren’t even related. If that happens, you’ll want to eliminate them.
    • You’ll want to make sure that you’re keeping the variables that actually matter. Otherwise, you won’t be able to draw any conclusions about the data.

    Identify variables that are related

    After you’ve cleaned the dataset, it’s time to evaluate the relationship between the variables.

    If you were trying to determine which cars sold the fastest, you wouldn’t just take the top 10% of cars and compare them to the bottom 10%. That would give you an inaccurate result. Instead, you’d need to look at every single car and figure out which ones sold the fastest.

    Similarly, when analyzing the data from your used car dealership, you’ll want to look at every single transaction and see which ones resulted in higher profits.

    Choosing the Right Statistical Method

    Now that you’ve got the data ready, it’s time to choose the right statistical method.

    For exploratory data analysis, you’ll typically use descriptive statistics. Descriptive statistics help you describe the data set by giving you the average, median, standard deviation, minimum, maximum, range, and other measures.

    Descriptive statistics can also tell you how much variation exists in the data. For example, if you had 100 transactions, you’d expect to see a wide range of values. Some might be very high, while others might be very low.

    Visualizing Your Results

    Finally, it’s time to visualize your results.

    Visualization is one of the best ways to understand data. It helps you see patterns and trends.

    When visualizing your data, you’ll often use charts. Charts are great for showing relationships between variables. They can show you how different groups of people behave differently.

    Charts can also be helpful for comparing two or more groups. For example, you could create a chart with all the cars sold by the dealership and another chart with only the top-selling cars.

     This way, you can easily see which cars did better.

    Analyzing the Data

    The final step is to analyze the data.

    Analyzing the data involves interpreting the information you found during the previous steps.

    This means figuring out exactly what each variable represents and how they relate to each other.

    Data analysis involves the identification, interpretation and presentation of data in a way that is meaningful to the user. Expert Data Science Course in Bangalore will give you the Conceptual knowledge and skills in order to analyze data effectively. Key components in exploratory data analysis include: data collection, data organization, data analysis methods, reporting and presentation. These key areas help users understand the data and make informed decisions. 

    What is the importance of exploratory data analysis in data science?

    Exploratory data analysis (EDA), sometimes called descriptive statistics, is one of the most fundamental skills you must master to become a data scientist. This skill set allows you to see patterns and make sense of what you observe. It can help you determine whether you should keep collecting data, or it might indicate that you already have enough information to draw meaningful conclusions. You can use EDA to check how much variance there is within a dataset and whether the values of different variables are correlated or independent.

    You can use EDA to confirm whether you are asking the right question, and to ensure that your findings are relevant to your intended business outcome. For example, you could use EDA to test whether the number of customers who buy X product every month correlates with the number of salespeople working in the department that sells X products. If you learn that the numbers are related, you can conclude that adding more salespeople will increase overall sales.

    Once you’ve completed EDA, you can start using more complex tools like regression models, clustering algorithms, and decision trees to build predictive models. These techniques allow you to explore correlations across multiple variables, predict future trends, and even automate some processes.

    Tools for exploratory data analysis

    There are many different types of exploratory data analytics tools out there to help you analyze your data. Here are some popular ones and discuss what makes each one unique.

    Python

    Object-oriented and imperative features are both available in Python, which is a multiparadigm programming language. This allows Python to be used for a variety of tasks ranging from web development to scientific computation. Python supports multiple input/output formats, including text files, network sockets, databases, XML documents, and binary file formats such as.gz,.bz2, and.xz. Python is often used in combination with libraries like NumPy, SciPy, Matplotlib, Pandas, and others. Understanding through our python training in Chennai can start you on the road to learning and understanding this programming language.

    R

    Statistical computations and graphics can be performed using R, a free software environment. It provides a wide range of statistical functions, linear algebra routines, visualization capabilities, and extensive support for database connectivity. R includes powerful graphical interfaces to allow quick and easy exploration of large datasets. R is especially useful for statistics and data mining applications where speed matters.

    Matlab

    Matlab is a product of MathWorks. Matlab is a high-performance matrix math and numerical computing environment designed specifically for technical computing. With Matlab, researchers can perform complex mathematical operations quickly and easily. Matlab is used extensively in engineering, science, financial modeling and mathematics.

    The tools available for exploratory data analysis can be helpful in gaining a better understanding of the data with R and Python through our Data Science Course in Coimbatore by being able to identify and analyze the various patterns and trends present in the data, analysts can gain valuable insights that can help them make better decisions.

    Advantages of Using Exploratory Data Analysis

    The primary advantage of EDA is that it helps you understand your data better than any other method. When you first collect data, you may not know exactly what questions you want to be answered. However, once you’ve collected all of your data, you can apply EDA to find answers to these questions.

    In addition, EDA can also help you identify potential problems before they occur. For example, if you notice an anomaly in your data, you can use EDA to investigate why it occurred. By identifying the cause, you can fix the problem or prevent it from happening again.

    Finally, EDA can save time by helping you avoid unnecessary analysis. By exploring your data, you can determine which variables are most important to your business and which methods should be applied to answer those questions. You can then focus on analyzing only the variables that matter most.

    Data science is a rapidly growing field, with lucrative salaries and opportunities available. However, before you can start earning a hefty salary as a data scientist, you’ll need some foundational skills. If you are a fresher or a new graduate, getting a data science job will be easier than ever. Check out this blog on how to get a Data Scientist Salary for Freshers to get you started.

    Conclusion

    EDA is a great way to get started with data analytics. If you’re just getting into data science, you might consider starting with EDA. Once you’re comfortable with EDA, you can move onto more advanced topics like machine learning and artificial intelligence.

    Exploratory data analysis (EDA) is a crucial skill for data scientists. It allows them to explore data and find insights that may not be apparent when looking at the raw data. By using our Data Science Interview Questions and Answers to help you prepare a data science CV, you’re more likely to feel comfortable during your interview. Getting the right skills for data analysis is essential if you want to be successful as a data scientist.

    Exploratory data analysis is a way to gain a deeper understanding of your data by using different techniques and methods. By doing this, you can find patterns or insights that you may have missed if you had only used analytic techniques. This type of data analysis is important because it can help you better understand your data and identify issues that need to be addressed. Therefore, it is important to include exploratory data analysis in your data science workflow.






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