Data Analysis of Beginners: Understanding Your Data

When it comes to data analysis, understanding your data is a the most important step in extracting meaningful insights.

Whether you are a beginner or an experienced analyst, having a solid understanding of your data is essential for making informed decisions and drawing accurate conclusions.

Here are some things to consider that will help you better understand your data:

Data Source

The first question you should be thinking of when understanding data is “where is my data coming from and what does it represent?”.

Knowing where your data originates helps assess its reliability. Is it collected from a reputable source with established credibility, or is it user-generated and potentially biased? Different sources may have varying levels of data quality. Understanding the source allows you to gauge the accuracy, completeness, and consistency of the data.

Depending on what insights you wish to uncover, it’s also good practice to consider the scope of the data. Does it represent a specific time frame, geographic location, or demographic group? Understanding this helps in interpreting the data accurately.

By understanding the source of your data, you lay the foundation for meaningful analysis and interpretation.

Data Type

Understanding the types of data you’re dealing with is like knowing the different colors in your paint set before starting a painting. Data can be broadly categorized into two types: qualitative and quantitative.

Qualitative data refers to non-numerical information, such as text or categorical variables.

• Text Data: This includes written or spoken words, such as transcripts, social media posts, or customer reviews. For you analysis of this, you might want to know what people are talking about or how they feel from those words, or in other words, sentiment analysis.
• Categorical Variables: These are variables that represent categories or groups. For example, if you’re sorting people by their favorite colors or types of pets they have, for your analysis you might want to see how many people like blue or how many have dogs versus cats.

Quantitative data consists of numerical values that can be measured and analyzed.

• Continuous Data: Continuous data can take any value within a range and can be measured. This is things like like how tall someone is, how warm it is outside, or how long something takes. For you analysis you can then use this to find averages or trends, like the average height of people in a room.
• Discrete Data: Discrete data can only take specific values and often represent counts or whole numbers. For example, if you have the number of cars sold in a month, you can do analysis to look at the distribution of how many were sold on different days or the trend based on the time of day it was sold.

Understanding the types of data you’re working with helps in choosing appropriate visualisation techniques, statistical methods, and models for analysis. It also guides decisions on data preprocessing steps such as grouping categorical variables or handling missing values.

Data Cleanliness

Data cleanliness is like preparing your ingredients before cooking a meal. You wouldn’t want to use spoiled vegetables or moldy bread, right? Similarly, before starting your data analysis journey, you need to ensure your data is clean and ready to use.

Clean data leads to more accurate results. Just like using fresh ingredients ensures a tasty dish, clean data ensures reliable analysis. There are different basic ways to clean your data:

• Identify Errors: Look out for mistakes, like typos, incorrect entries, or inconsistencies in formatting. Just as you’d pick out bruised fruit, identify and correct errors in your data.
• Handle Missing Values: Missing data is like missing ingredients in a recipe — it can ruin the dish. Decide whether to fill in missing values, discard them, or find alternative solutions.
• Remove Duplicates: Duplicates clutter your data, just like having unnecessary duplicates of ingredients in your pantry. Remove them to streamline your analysis.
• Deal with Outliers: Outliers are like overly salty or spicy ingredients — they can throw off the flavor. Decide whether to keep, remove, or transform them based on your analysis goals.

Taking the time to clean and preprocess your data will helps you understand the state of your data a lot better which in turn improves the quality of your analysis.

Data Exploration

Exploratory Data Analysis (EDA) is like exploring a new city before settling down. It’s a crucial step in understanding your data and gaining insights before diving into more complex analyses.

EDA involves summarizing the main characteristics of the data through descriptive statistics, visualizations, and data exploration techniques.

By examining the distribution, patterns, and relationships within your data, you can uncover valuable insights and identify potential trends or anomalies. EDA helps you understand the underlying structure of your data, which guides subsequent analysis and decision-making processes.

I’ve outlined different methods of exploratory data analysis for beginners in this blog.

Knowing where your data comes from, its type, cleanliness, and context is key for good analysis. It’s like having a reliable map before exploring new terrain. By doing so, you can use the right tools, ensure accuracy, and uncover valuable insights to make informed decisions.

Remember that data analysis is an iterative process, and continuously revisiting and refining your understanding of the data will lead to more accurate and valuable results. So, take your time to understand your data, and let it guide you towards making informed decisions.