Understanding Your Data: The Essentials of Exploratory Data Analysis.

INTRODUCTION

As data scientists and data analyst, This is a very very important and crucial initial step that must be performed. After data collection, data is in raw form and unprocessed facts a data scientist, analyst, or any other person is unable to understand the structure and contents of that data, That's where EDA comes in; analyzing and visualizing data to understand its key characteristics, uncover patterns, and identify relationships between variables.

Understanding data requires understanding the expected qualities and characteristics of data. The knowledge you have about data, the needs the data will satisfy, it's content and creation. Let's now dive deeper into EDA to understand how we should transform data into information. Information is data that has been processed, organized, interpreted, and structured.

EXPLORATORY DATA ANALYSIS

As defined above, EDA refers to analyzing and visualizing data to understand it's key characteristics, uncover patterns, and identify relationships between variables. It helps determine how best to manipulate data sources to get answers you need, making it easier for data scientists to discover patterns, spot anomalies, test hypothesis or assumptions. It is an important first step in data analysis, it is the foundation for understanding and interpreting complex datasets.

TYPES OF EDA
These are different methods and approaches used within exploratory data analysis process. Here are three main types of EDA:

Univariate Analysis: This is the simplest form you can use to analyze data, It explores each variable in a dataset. Involves looking at the range of values, as well as the central tendency of the values. It describes the pattern of response, each variable on its own For example, examining the age of employees in a company.

Bivariate Analysis: This analysis, two variables are observed. It aims to determine if there is a statistical link between the two variables and if yes how strong are they. Bivariate lets researchers look at the relationship between two variables. Before using this analysis, you have to understand why it is important;

 Bivariate analysis helps identify trends and patterns
 Helps identify cause and effect relationships.
 Helps researchers to make predictions.
 It also inform decision-making.

Techniques used in bivariate analysis include scatterplots, correlation, regression, chi-square tests, t-tests, and analysis of variance which can be used to determine how two variables are related.

Multivariate Analysis: This involves the statistical study of experiments in which multiple measurements are made on each experimental unit and for which the relationships among multivariate measurements and their structure are important to the experiment's understanding. For example, How many hours a day a person spends on Instagram.

Techniques include dependence techniques and interdependence techniques.

ESSENTIALS OF EDA

a. Data collection: The first step when dealing with data is first having the data you want. Data is gathered from various sources according to the topic you're working on, using methods like web scraping or downloading datasets from platforms such as Kaggle.

b. Understanding your data: Before proceeding to cleaning, you first have to understand the data you collected. Try to understand the number of rows and columns you'll be working with, the information for each column, the characteristics of your data, data types and much more.

c. Data cleaning: This step involves identifying and addressing errors, inconsistencies, duplicates, or incomplete entries within the data. The main objective of this step is to enhance the qualities and usefulness of data hence leading to more dependable and precise findings. Data cleaning involves several steps;
How to clean data;

      i)Handling missing values: by imputing them using mean, mode, median of the column, fill with a constant, forward-fill, backward-fill, interpolation or dropping them using the dropna() function.

      ii)Detecting outliers: you can detect outliers using the interquartile range, visualizing, using Z-Score or using One-Class SVM.

      iii)Handle duplicates: Drop duplicate records

      iv)Fix structural errors: Address issues with the layout and format of your data such as date formats or misaligned fields.

      v)Remove unnecessary values: Your dataset might contain irrelevant or redundant information that is unnecessary for your analysis. You can identify and remove any records or fields that won't contribute to the insights you are trying to derive. 

d. Summary statistics. This step provides a quick overview of the dataset's central tendencies and spread, including mean, median, mode, standard deviation, minimum, maximum using the describe method in pandas or numpy for numeric features. For categorical features we can use graphs and actual summary statistics.

e. Data visualization: This is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data. Try to identify trends and patterns in the dataset, using lines, bars, scatter and box plot with tools like matplotlib, seaborn or tableau.

f. Data relationship. Identify the relationship between your data by performing correlation analysis to examine correlations between variables.

• Analyze relationships between categorical variables. Use techniques like correlation matrices, heatmaps to visualize.

g. Test Hypothesis: Conduct tests like t-tests, chi-square tests, and ANOVA to determine statistical significance.

h. Communicate Your findings and Insights: This is the final step in carrying out EDA. This includes summarizing your evaluation, highlighting fundamental discoveries, and imparting your outcomes cleanly.

• Clearly state the targets and scope of your analysis.
• Use visualizations to display your findings.
• Highlight critical insights, patterns, or anomalies you discovered in your EDA.
• Discuss any barriers or caveats related to your analysis.

The next step after conducting Exploratory Data Analysis (EDA) in a data science project is feature engineering. This process involves transforming your features into a format that can be effectively understood and utilized by your model. Feature engineering builds on the insights gained from EDA to enhance the data, ensuring that it is in the best possible form for model training and performance. Let’s explore feature engineering in simple terms.

Feature Engineering.

This is the process of selecting, manipulating and transforming raw data into features that can be used in model creation. This process involves 4 main steps;

1. Feature Creation:- Create new features from the existing features, using your domain knowledge or observing patterns in the data. This step helps to improve the model performance.

2. Feature Transformation: This involves the transformation of your features into more suitable representation for your model. This is done to ensure that the model can effectively learn from the data. Transforming data involves 4 types;

    i) Normalization: Changing the shape of your distribution data. Map data to a bounded range using methods like Min-Max Normalization or Z-score Normalization.
   
    ii) Scaling. Rescale your features to have a similar scale  to make sure the model considers all features equally using methods like Min-Max Scaling, Standardization and  MaxAbs Scaling.
   
    iii) Encoding. Apply encoding to your categorical features to transform them to numerical features using methods like label encoding, One-hot encoding, Ordinal encoding or any other encoding according to the structure of your categorical columns.
   
    iv) Transformation. Transform the features using mathematical operations to change the distribution of features for example logarithmic, square root.
   

3. Feature Extraction: Extract new features from the existing attributes. It is concerned with reducing the number of features in the model, such as using Principal Component Analysis(PCA).

4. Feature Selection: Identify and select the most relevant features for further analysis. Use filter method( Evaluate features based on statistical metrics and select the most relevant ones), wrapper method(Use machine learning models to evaluate feature subsets and select the best combination based on model performance) or embedded method(Perform feature selection as part of model training e.g regularization techniques)

Tools Used for Performing EDA

-Let's look at the tools we can use to perform our analysis efficiently.

Python libraries

         i)   Pandas: Provides extensive functions for data manipulation and analysis.

         ii)  Matplotlib: Used for creating static, interactive, and animated visualizations.

         iii) Seaborn: Built on top of Matplotlib, providing a high-level interface for drawing attractive and informative capabilities.

         iv)  Plotly: Used for making interactive plots and offers more sophisticated visualization capabilities.

R Packages

     i)  ggplot2: This is used for making complex plots from data 
      in a dataframe.

    ii)  dplyr: It helps in solving the most common data manipulation challenges.

   iii)  tidyr: This tool is used to tidy your dataset; Storing it in a consistent form that matches the semantics of the dataset with the way it is stored.

Conclusion
Exploratory Data Analysis (EDA) forms the foundation of data science, offering insights and guiding informed decision-making. EDA empowers data scientists to uncover hidden truths and steer projects toward success. Always ensure to perform thorough EDA for effective model performance.

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