Data Analysis Techniques for Beginners
Data analysis is a broad field but, when broken down into manageable steps, the most foundational of data analysis techniques for beginners are actually pretty straightforward. Data analysis, at its core, is a process for understanding information to answer a particular question.
No matter what beginner level data analysis project you’re working on; be it a sales data spreadsheet or a data survey Responses, the same/similar structures and principles of data analysis apply.
Table of Contents
- Cleaning and Organizing Data First
- Descriptive Methods for Understanding Data
- Spotting Patterns and Relationships
- Common Beginner Mistakes to Avoid
- Cleaning and Organizing Data First
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Cleaning and Organizing Data First
Prior to any analysis, data needs to be in an appropriate, consistent format. Many beginners aim to arrive at answers and analysis, therefore bypass these important steps. However, this is potentially the most critical step in the entire process:
- Removal of duplicate records
- Format normalization (e.g. standardization of currency or date formats)
- Dealing with missing data (incomplete records or removal of row, or filling gaps with some reasonable value)
- Review of data to check for record of negative age, impossible dates, etc.
Skipping this step means that analysis will be misleading, no matter how sophisticated your analysis methodology.
Descriptive Methods for Understanding Data
After cleaning data, these techniques assist a beginner to understand the structure of the data in preparation for more extensive analysis.
Common beginner friendly descriptive statistics are:
- Mean: calculating the average value to have a general idea of the typical value in your dataset
- Median: utilized more often than average when you have extreme outlier values in your dataset
- Range: the gap between your dataset’s smallest and largest values
- Frequency: how many times a value or a category occurs
Being familiar with these beginner level statistics will give you insight into any dataset even prior to more advanced statistics.
Spotting Patterns and Relationships in Data
Beyond basic descriptions, beginners can start looking for patterns and relationships that reveal more insights. Some beginner accessible ways to start looking for patterns and relationships:
- Sorting and filtering data to identify time periods or target categories
- Grouping data and comparing averages or totals within different groups
- Basic graph creation, including bar and line graphs
- Identify correlations
The Statistics in Schools program developed by the Census Bureau may be instructed towards schools, but the practice of developing data literacy, the ability to understand, interpret, and evaluate data, is a useful skill for all beginners, not just students.
Common Beginner Mistakes in Data Analysis
Some bad habits show up early for beginning data analysts, even after they learn a few techniques. Confusing correlation and causation comes from novices viewing patterns and seeing movement in the same direction and assuming that one caused the other, when in actuality, each movement is independent and could have happened because of different factors.
Drawing unfounded conclusions from small data sets is asking for trouble, and can lead to misleading conclusions when more data is added. Outliers should not be ignored either, data entry mistakes can result in outliers that mean nothing, but there can be outliers that uncover anomalies and elicit interesting results.
Lastly, it is important to provide context and meaning to the numbers, given in the computation, because, without justifying the numbers, a computation is useless.
For more intermediate beginners who want to apply their techniques within some code, our beginner’s catalogue of Python projects with source code provides numerous beginner level data handling tasks.
Frequently Asked Questions
Do you need to know statistics to begin learning data analysis?
In short, no. Knowing about averages and some simple patterns should be enough to learn data analysis, and as your projects become more complex, you can learn to incorporate more advanced statistical concepts.
What is the beginner data analyst’s tool of choice?
A spreadsheet program is hands down the easiest way to get started, as it requires little to no coding, and sorting and filtering are available with a few clicks. A lot of beginners take up Python to expand to more advanced data analysis tasks.
How big should my data sample be to make conclusions?
There are no hard and fast rules, but I would say under 50 data points merit extra precaution.
What is the difference between correlation and causation?
Correlation implies a relationship, while causation means one imposes a change on the other. Correlation does not imply causation.
Is data cleaning relevant for small, simple datasets?
Yes, all datasets, large or small, may contain multiple instances of the same data entry, formatting inconsistencies, and errors.
Conclusion
The techniques in this chapter have given beginners the tools to tackle data analysis for small datasets. The beginner should first get comfortable using clean formatted datasets to spot simple patterns. After continual use, these techniques should become intergrated and then learning additional complex analysis should be straightforward.
