10 Data Cleaning Skills you need to know in 2024
Here's the bottom line: if you want a career in data, you need to know how to clean data. In fact, data scientists spend a whopping 80% of their time on data preparation and cleaning. That's right, 80%!
So what exactly is data cleaning and why is it so important? At its core, data cleaning is all about making sure your data is accurate and usable. After all, businesses are generating massive amounts of data these days. But that data is only useful if it's reliable.
Think of it this way: you wouldn't want to make a big business decision based on bad data, right? Of course not! That's where data cleaning comes in. By effectively cleaning and preparing data, you enable sound analysis and smart decision-making.
In this post, we're going to explore some of the most essential data cleaning skills you need to know, like:
- Handling anomalies
- Filtering data
- Merging datasets
We'll look at why each skill matters and how you can put them into practice. Plus, we'll talk about how mastering these skills can seriously boost your career prospects.
By the way, if you're looking for a guided way to build these skills, you might want to check out Dataquest's Data Analyst in Python path. It's a step-by-step approach that helps you learn by doing.
So let's get started! Let's do this!
Why you need to learn data cleaning in 2024
As the quantity of data continues to exponentially grow, being able to effectively clean and prepare that data is becoming a critical skill. Why? Because clean data is the foundation of accurate analysis and informed decision-making.
Data scientists spend a significant portion of their time on data preparation. In fact, a Forbes survey shows that data scientists spend around 80% of their time on preparing and managing data for analysis, with 60% of their time dedicated to cleaning and organizing data, and 19% of their time spent on collecting data sets.* This makes sense, as you can't uncover reliable insights from messy or inaccurate data. Additionally, identifying data issues early can prevent costly complications in the future.
Data cleaning skills aren't just useful for streamlining your work - they can also give your career a boost. For instance, HR teams rely heavily on clean employee data to make decisions that improve satisfaction and productivity,* so data cleaning abilities can be a major selling point.
Top 10 Data Cleaning Skills
Developing a strong foundation in data cleaning is a smart move if you're looking to boost your career prospects. These skills are increasingly vital for professionals in data-centric roles, as they directly impact the quality and efficiency of data analysis.
At its core, data cleaning is all about maintaining a pristine data environment. Here are the top 10 data cleaning skills to add to your toolkit:
- Data Exploration
- Handling Anomalies: Missing Values and Outliers
- Filtering Data
- RegEx and String Manipulation
- Date/Time Parsing
- Merging and Joining Data
- Data Transformation and Data Type Conversions
- Data Integrity: Deduplication and Validation
- Dealing with Sparse Data
- Normalization and Standardization
What does this look like in practice? When exploring a dataset, you might calculate average values and identify missing data points to assess its overall quality. You may also need to use regular expressions (RegEx) to extract specific text patterns and standardize inconsistent formatting.
By mastering these data cleaning techniques, you'll lay the groundwork for more advanced data manipulation skills. Not only will you improve the accuracy and reliability of your analyses, but you'll also be more efficient with your data lifecycle.
In the following sections, we'll take a closer look at each of these valuable data cleaning skills. Whether you're a student aiming to launch a data career or a professional looking to level up, building proficiency in these areas is a worthwhile investment in your future success.
1. Data Exploration
Want to clean your data effectively? Start by understanding what your data already includes.
There are many ways to explore data, but some great places to start include:
- Measures of central tendency (mean, median, and mode) and dispersion (standard deviation and range) to understand the distribution of your data.
- Check for missing values to identify gaps in your data that might require filling.
- Look at the structure of your data, including the number of columns and rows.
Real world example
Let's consider an example using a dataset named 'food_survey.csv', which includes details about a survey given to college students about their food choices.*
Look at the first five rows
With a programming tool like Python, we can start by looking at the first few rows and the general description of the data:
import pandas as pd
data = pd.read_csv('food_survey.csv')
data.head()
And here's the output (note that the output is truncated for brevity):
GPA Gender breakfast calories_chicken calories_day calories_scone
0 2.4 2 1 430 NaN 315.0
1 3.654 1 1 610 3.0 420.0
2 3.3 1 1 720 4.0 420.0
3 3.2 1 1 430 3.0 420.0
4 3.5 1 1 720 2.0 420.0
From the first few rows, we can understand the structure of our data. We can already see some interesting features such as multiple columns that are about "calories", and that the Breakfast column uses numeric data. This may prompt us to look at the data source to see what these values mean.
Explore null values and data types
data.info()
class 'pandas.core.frame.DataFrame' RangeIndex: 125 entries, 0 to 124 Data columns (total 61 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 GPA 123 non-null object 1 Gender 125 non-null int64 2 breakfast 125 non-null int64 3 calories_chicken 125 non-null int64 4 calories_day 106 non-null float64 5 calories_scone 124 non-null float64
This summary information tells us about the columns, non-null counts, and data types, which is crucial for identifying fields that need attention for missing values or type conversion. Our example output shows that the "GPA" column is currently stored as an object, which will cause issues if we want to perform calculations with it later on.
Descriptive statistics
data.describe()
Gender breakfast calories_chicken calories_day calories_scone
count 125.000000 125.000000 125.000000 106.000000 124.000000
mean 1.392000 1.112000 577.320000 3.028302 505.241935
std 0.490161 0.316636 131.214156 0.639308 230.840506
min 1.000000 1.000000 265.000000 2.000000 315.000000
25% 1.000000 1.000000 430.000000 3.000000 420.000000
50% 1.000000 1.000000 610.000000 3.000000 420.000000
75% 2.000000 1.000000 720.000000 3.000000 420.000000
max 2.000000 2.000000 720.000000 4.000000 980.000000
This descriptive statistics breakdown offers insights into the data's distribution and can highlight potential anomalies. The sample output shows a "min" value of 1 and a "max" value of 2 for both Gender and Breakfast, which warrants further investigation to ensure we understand what these values represent.
Equipped with this understanding of your data's basic attributes, you'll be better prepared to move forward with targeted cleaning processes, ensuring your data is accurate and useful for analysis.
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2. Handling anomalies: missing values and outliers
Handling data anomalies like missing values and outliers is a critical skill for ensuring your data is reliable. If left unchecked, anomalies often skew datasets and lead to faulty conclusions.
Looking at the "food_survey.csv" dataset again, we can see that the calories_day column has several missing values. We'll have to ensure we handle this field carefully so our interpretations don't introduce unintended biases.
RangeIndex: 125 entries, 0 to 124 # Column Non-Null Count Dtype --- ------ -------------- ----- 0 GPA 123 non-null object 1 Gender 125 non-null int64 2 breakfast 125 non-null int64 3 calories_chicken 125 non-null int64 4 calories_day 106 non-null float64 5 calories_scone 124 non-null float64
While anomalies pose challenges, learning to spot and correct them will take your data skills to the next level. Some key techniques include:
- Using anomaly detection methods, which are automated tests that flag data points falling outside an expected range, to quickly spot potential issues
- Handling missing data through deletion or educated guesses based on other available information
Learning these approaches won't just improve the quality of your data - it will make you a better problem-solver and decision-maker. Tools are important, but sound judgment is irreplaceable.*
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3. Filtering data
Filtering data is a key part of data cleaning. It improves the quality of your dataset so you can perform targeted and accurate analysis.
But what does filtering actually involve? At its core, filtering is about removing or changing data in your dataset that is irrelevant, inaccurate, or not formatted correctly. This process helps ensure the integrity and usefulness of your data.
Real world example
Let's look at a practical example. Looking again at the "food_survey.csv" file, if we only want to look at data for males with a GPA under 3.0, we'll need to filter out any rows that are for females and have a GPA over 3.0. In order to do this, we first convert GPA into a numeric field. Then, we'll ensure we understand the numeric values for males (2) versus females (1) (this information can be found in the dataset's documetation). We can create a refined dataset that only includes the relevant, accurate data for our analysis:
import pandas as pd
data = pd.read_csv('food_survey.csv')
data['GPA'] = pd.to_numeric(data['GPA'], errors='coerce')
male_low_gpa = data[(data['GPA'] < 3.0) & (data['Gender'] == 2)]
male_low_gpa.head()
GPA Gender breakfast calories_chicken calories_day ... 2.40 2 1 430 NaN ... 2.20 2 1 430 2.0 ... 2.80 2 1 430 NaN ... 2.71 2 2 265 2.0 ... 2.90 2 1 265 2.0 ...
Filtering the data this way makes it easier to identify meaningful trends and draw reliable conclusions.
Developing strong filtering skills can really boost your productivity and effectiveness in data-focused roles. A data analyst who can filter datasets efficiently will save time and reduce errors in their analysis and insights. This ability to work with clean, high-quality data is invaluable for informing smart business decisions.
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4. RegEx and String Manipulation
Have you ever worked with a dataset full of inconsistent text? Maybe it contained phone numbers in different formats or email addresses with typos. Cleaning that kind of messy, unstructured data can be a huge headache. That's where Regular Expressions (RegEx) and string manipulation come in.
In simple terms, regular expressions allow you to search for specific patterns in text. For example, you could write a RegEx pattern to find all phone numbers that follow the format (123) 456-7890. String manipulation refers to tasks like removing extra white space, changing the case of letters, or replacing certain characters.
Why are these valuable skills?
Let's say you're analyzing customer reviews for an e-commerce company. The reviews are full of abbreviations, misspellings, and inconsistent punctuation. Before you can get any meaningful insights, you need to clean and standardize all that text data. With RegEx and string manipulation, you could remove special characters, convert everything to lowercase, and replace common abbreviations with full words. What might take hours by hand can be done in seconds with a well-crafted RegEx.
These techniques become even more crucial when working with large datasets. The ability to efficiently clean and preprocess text can make the difference between spending days manually cleaning data and moving swiftly on to analysis.
Learning RegEx
If you're new to regular expressions, the syntax can seem intimidating at first. When I first saw a RegEx pattern, it looked like a jumble of slashes and brackets that I really didn't want to learn. But with practice, it started to make sense. It was when I encountered my first dirty dataset with over 10,000 rows that I truly appreciated the value of RegEx — if I had to handle each inconsistent data value by-hand it would have taken me weeks!
Interactive tutorials like Regexr.com are a great place to start. You can test out patterns and see what they match in real-time. For extra practice that incorporates some fun, try your hand at Regex Golf, a game where you compete to write the most concise RegEx for a given problem.
With practice, you'll be able to clean and wrangle text data with confidence. As you advance in your data career, these skills will help you work more efficiently and tackle a wider range of data challenges.
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5. Date/time parsing
Want to be a great data analyst? Master date/time parsing. Put simply, this skill is all about converting date and time data from various formats into one standard format.
Why is this so important? When you're analyzing data, you need dates and times to be right. So much of what we analyze ties back to time - trends, predictions, you name it. If your dates are in different formats, it can really throw things off. You might get the order of events wrong or make false connections.
As one data scientist put it, "When I first learned to code, handling date/time parsing was challenging. Every solution I found online used a different technique or Python library and everything seemed incompatible with my dataset." The trick is to practice with lots of different datasets, like weather or traffic data full of timestamps.
Imagine you're looking at sales data to see how weather impacts buying. If your dates are a mess, you could easily mistake a July heatwave for a January blizzard! And that would totally change your analysis.
In short, nailing date/time parsing will make you an invaluable data analyst. Your insights will be spot-on and any data-savvy company will be lucky to have you.
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6. Merging and Joining Data
Merging and joining data is a must-know technique that can help you create more comprehensive, reliable datasets for making smarter decisions. In my first-ever data-related job interview the very first technical question they asked me was about the different types of joins in SQL. Knowing the difference between a left join and an inner join helped me land the job, and the fact that it was the first question they asked is good antecdotal evidence that shows how important merges and joins are to the data lifecycle.
Understanding merging and joining can bring some big benefits to your data work. For one, it helps keep your datasets consistent and accurate. It also allows you to work with more complex data structures, which is a valuable skill for tackling analytical challenges.
That said, merging big datasets or dealing with mismatched formats can sometimes get complicated. The key is to choose the right keys for joining your tables and frequently spot-check values to ensure the logic behind the merge aligns with what you're seeing in the data table.
So if you're ready to up your data game, start tackling merges and joins. With a little practice, you'll be combining datasets like a pro and uncovering insights you never knew were there!
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7. Data Transformation and Data Type Conversions
Have you ever worked with a dataset that wasn't quite in the right format? Maybe the dates were inconsistent or the numbers were stored as text. This is where data transformation and data type conversion come in.
In simple terms, data transformation means changing the format or structure of your data. This could involve tasks like standardizing date formats, splitting columns, or aggregating data to summarize details. Data type conversion, on the other hand, is about making sure each piece of data is categorized correctly, such as numbers vs. text.
So why do these matter for data cleaning? Let's say you have a dataset with dates in different formats - some are MM/DD/YYYY while others are DD/MM/YYYY. To analyze this data accurately, you'd need to transform all the dates into a consistent format. Or if you had numbers stored as text, you couldn't perform mathematical operations until you converted them to a numeric data type.
As more businesses rely on data to make strategic decisions, having strong skills in data transformation and type conversion is becoming increasingly valuable. It allows you to work with complex datasets more efficiently and ensures your analysis is built on a solid, accurate foundation.
Real world example
If we look at the "food_survey.csv" dataset again, we can see that several fields are coded as the incorrect data type. In order to validate our data, we can go column by column to create consistency with formatting:
import pandas as pd
data = pd.read_csv('food_survey.csv')
data['GPA'] = pd.to_numeric(data['GPA'], errors='coerce')
To build your skills in this area, I recommend:
- Practicing with real-world datasets that need cleaning
- Exploring tools like Python's pandas library to experiment with different data transformation techniques (especially the
to_numericandto_stringmethods) - Familiarizing yourself with common data type issues and learning troubleshooting strategies
With practice, you'll be able to handle data in all sorts of formats and set yourself up for more effective analysis.
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8. Data integrity: deduplication and validation
There is a common saying in the data world: "Garbage in, garbage out." This means that if your data isn't reliable or if "bad data" leads your team to misguided outcomes, it's worse than not having any data to begin with. Ensuring your data is trustworthy and accurate will lead to data-driven decisions you can rely on.
But how do you make sure your data has integrity? We'll focus on two key aspects here: deduplication and validation. Deduplication is all about removing duplicate entries from your datasets. Not only does this clean up your data, but it also makes processing more efficient. If your data file has multiple entries for some individuals (maybe the data was collected more than once), you can use deduplication techniques to eliminate those redundancies. One way to do this is by identifying unique identifiers like email addresses, phone numbers, or user IDs.
Validation, on the other hand, is about making sure your data follows specific rules or norms. This helps boost the reliability of your data for decision-making. For example, you might check if numerical values are within expected ranges or if date entries use a consistent format.
The bottom line? Addressing these challenges effectively is a must-have skill. It will level up your ability to handle real-world data scenarios. And that's key for advancing your career in data.
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9. Dealing with sparse data
When you're cleaning data, it's crucial to know how to handle sparse data effectively. What exactly is sparse data? It's data that has a lot of missing or zero values.
Sparse data can really throw off your analysis, cause problems like inaccurate models, and even introduce bias into your work. That's because it increases the chances of issues like overfitting, where a model performs well on the data it was trained on but poorly on new data.
So what can you do about it? Some common techniques include filling in missing values, creating new data features, or removing sparse features entirely. Knowing which techniques to use and when is more of a art than a science because it requires you to be intimately aware of how a certain technique will positively and negatively impact the data.
Using the right methods to clean up sparse data doesn't just lead to better performing models - it also helps you make smarter decisions by ensuring your insights are based on high-quality data.
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10. Normalization and Standardization
Normalization and standardization are the final data preprocessing techniques we'll talk about today. Let’s explore what they involve, how they differ, and examples of their application.
Normalization is a technique that adjusts values measured on different scales to a common scale, usually between 0 and 1. It is especially useful when the parameters have varying ranges. Standardization, on the other hand, transforms data to have a mean of zero and a standard deviation of one, making it essential for algorithms that assume data is normally distributed.
The differences between these two techniques can determine when each should be used:
- Normalization is generally used when the dataset does not follow a Gaussian distribution. It can be impacted by outliers but is less sensitive to them than standardization.
- Standardization is typically used in scenarios that require the dataset to have a Gaussian distribution, forming the basis for techniques such as principal component analysis and linear regression.
Practical examples
Here are some practical examples of each:
Normalization
- Adjusting salaries ranging from $30,000 to $90,000 within a dataset to fall between 0 and 1 to help compare employees' relative earnings regardless of the wide range difference.
- Rescaling ages in a dataset where users range from 10 to 60 years old for a marketing analysis study.
- Normalizing distances traveled in kilometers by delivery trucks in a logistic company to standardize route evaluations.
Standardization
- Standardizing test scores from students to evaluate performance statistically across various schools or universities.
- Transforming features like height and weight in healthcare data to predict health risks using machine learning algorithms.
- Equating volatility measures from different stocks in financial models to assess relative risk accurately.
Developing a skill set in both normalization and standardization not only improves the performance and accuracy of your data models but also contributes to your versatility as a data professional ready to manage a diversity of datasets and challenges.
Additionally, it's important to visualize your data before and after applying these transformations to understand their effects fully and make an informed decision on which method best fits your specific needs. Remember that while normalization is more susceptible to being skewed by outliers, standardization requires data to ideally follow a normal distribution for optimal results.
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Common misconceptions and challenges
Data cleaning is critical for accurate analysis, but it's not a one-and-done task. As new information enters your system, you'll need to regularly clean and update your data to maintain quality.
What are some common challenges you might face? Let's take a look:
- Inconsistent data formats across datasets that require careful alignment
- Duplicate records that need to be identified and merged or removed
- Ensuring data privacy and meeting regulations without compromising data integrity
These issues can complicate the cleaning process and affect the reliability of your data. For instance, if customer names are entered in different formats across sales databases, it will take time to standardize them before you can properly analyze customer trends.
Fortunately, there are strategies you can use to streamline data cleaning. Consider using automation tools to handle repetitive formatting tasks. Setting up standardized coding systems can also help keep data consistent. By understanding the realities of data cleaning, you'll be well-prepared to ensure accurate, reliable datasets in the real world.
How to get started with data cleaning skills
Ready to get cleaning? Here's how to get started on the right foot:
Build a strong foundation
First, make sure you understand the basics. Get to know your data by looking at things like averages, medians, and missing values. This helps ensure your data is reliable. Practice filtering, manipulating strings, and converting data types to gain hands-on experience.
Focus on skills that match your goals
What kind of data cleaning tools will you need in your dream job? Excel? Python? SQL? Master the skills that will make you efficient at preparing data and advance your data science career. Also, don't be afraid to mix and match! Data analysts commonly use a combination of tools to get the job done.
Practice, practice, practice
The best way to cement your new knowledge? Apply it right away with hands-on projects. Try practicing with real-world datasets from sources like Kaggle or the UCI Machine Learning Repository. The more hands-on experience you get, the more intuitive these techniques will become!
Keep learning and stay current
Data cleaning is always evolving, so keep up with the latest techniques and tools. Join online communities, go to workshops, or take courses on emerging methods*. Staying on top of trends will expand your skills and make you a stronger job candidate.
Ready to become a data cleaning pro? Follow these steps and you'll be well on your way to this in-demand data skill.
Why choose Dataquest for data cleaning?
If you want to launch a career in data, data cleaning proficiency is a must. Luckily, Dataquest offers an excellent way to gain these critical skills, even if you're a total beginner.
Through hands-on projects, you'll practice key data cleaning techniques used by data professionals every day, such as:
- Handling missing values and outliers
- Deduplicating and normalizing data
- Parsing dates and working with sparse data
By applying these skills in realistic scenarios, you'll develop a strong practical understanding that will impress employers.
Dataquest's interactive platform guides you step-by-step, so you learn by doing. You'll write real code in Python, an essential language for data cleaning and analysis. This means you'll gain valuable programming experience as you learn.
Have a question as you work through a project? No problem! Dataquest's welcoming community of data professionals and students is ready to help.
Learning alongside others keeps you motivated and provides fresh perspectives.
The well-rounded curriculum covers all the bases, from basic filtering to complex operations. So you can feel confident you're building all the skills you need for a data career.
Want to show off your new abilities? The projects you complete become an impressive portfolio that demonstrates your data cleaning prowess to potential employers. Imagine how great it will feel to share your work with pride!
So if you're excited to get started with data, Dataquest provides an engaging, effective path to gaining must-have data cleaning skills. You'll be surprised how quickly you can learn!
Conclusion
Want to take your data science career to the next level? Master the art of data cleaning. From handling missing values to merging datasets, the techniques covered in this post will set you up for success.
So, what's the best way to start building these essential skills? We recommend a step-by-step approach:
- First, get comfortable with the basics, like identifying anomalies and understanding your data's structure.
- Next, move on to more advanced tasks, such as data type conversions and normalization.
- Finally, consider using a structured learning resource, like Dataquest's comprehensive data science courses, to guide your progress.
By putting these data cleaning practices into action, you'll keep your skills relevant and in-demand, making you an invaluable asset in any data-driven role.
