8 Data Analyst Skills Employers are Hiring for in 2024

Although you probably won't be that shocked by the statistic it uses, I'm going to ask you a loaded question. Did you know the demand for data analysts is projected to grow by 25% by 2030? And it's no wonder because companies all around the world are looking for people with data analyst skills who can transform raw data into actionable insights. But what exactly does it take to be one of those skilled people? Keep reading to find out!

In this comprehensive guide, I'll lay out the 8 essential data analyst skills you need to thrive in a data analyst role. From data cleaning and statistical analysis to data visualization and communication, I'll break down the key competencies that will set you apart in the job market. Plus, we'll take a look at the exciting world of generative AI and how it's revolutionizing the field of data analytics, creating new opportunities for those willing to adapt and learn.

Whether you're a recent graduate looking to launch your career or a professional seeking to upskill, this post will provide you with a roadmap to becoming a sought-after data analyst.

Data Analyst Skills or Data Analyst Tools?

When it comes to data analysis, the specific tools you use will vary depending on your role, company, and industry. That's why, rather than focusing on any one particular tool, we're going to zero in on the foundational skills you need to succeed.

Now, don't get me wrong - learning popular data analysis tools is still important. After all, you'll need to know how to put your skills into practice. But trying to decide between Python, R, Excel, Tableau, or Power BI can quickly become overwhelming, especially when you're just starting out. My advice? Focus on building a strong skill foundation first. Think of it like learning to cook - once you understand the basic techniques, you can start experimenting with different recipes and ingredients to create your perfect dish.

The Rule Breaker

The one exception to this rule is SQL. As the industry standard for querying databases, SQL is a must-have for any aspiring data analyst. Many agree that learning SQL has significantly boosted their career prospects.*

Throughout this post, you'll notice that the provided examples use a variety of tools. This is intentional - I want to show you that having the right skills is more important than mastering any one piece of software. Plus, by seeing how these skills can be applied across different platforms, I hope you'll be inspired to explore and (eventually) learn as many of them as you can.

So, what do you think are the most sought-after data analyst skills in 2024? Take a moment to think about that. In the next section, we'll reveal the top 8 that will help you thrive in your data analytics career. Let's get started!

The Top 8 Data Analyst Skills

So, what does it really take to excel as a data analyst in today's competitive market? While the specific tools you use may vary, these are 8 core skills that form the foundation of any successful data analyst's toolkit:

1. Clean and Prepare Data
2. Explore and Analyze Data
3. Apply Statistical Knowledge
4. Create Data Visualizations
5. Create Dashboards and Reports
6. Write and Communicate
7. Acquire Domain Knowledge
8. Solve Problems

Over the next several sections, we'll examine each of these critical competencies in detail. You'll learn why they matter, how they're applied on the job, and see real-world examples that demonstrate their importance. By the end, you'll have a clear roadmap for building the skill set you need to land your dream data analyst job and make a real impact in your role.

Ready to discover what goes into becoming a successful data analyst? Let's break it down, one skill at a time.

1. Clean and Prepare Data

If you open up your favorite search engine right now and search for "Time spent cleaning data," I'm almost positive you'll see this statistic all over the place: "Data professionals spend 80% of their time on data cleaning and preparation."* While the exact percentage is up for debate,* one thing is clear: this often overlooked skill is absolutely vital for any aspiring data analyst.

What's Involved in this Process?

In a nutshell, it involves transforming raw data into a usable format for analysis. This typically includes tasks like:

• Retrieving data from various sources (databases, spreadsheets, APIs, etc.)
• Handling missing, duplicated, or inconsistent values
• Reformatting fields and reshaping the data structure
• Merging datasets from multiple sources
• Validating and checking data quality

Without this crucial step, your analysis would be built on shaky foundations. Imagine trying to draw insights from a customer dataset full of invalid email addresses or inconsistent date formats. The results would be unreliable at best and downright misleading at worst.

Data Cleaning in Action

Let's look at a quick example using the Adult dataset from the UCI Machine Learning Repository. This dataset, despite its age, remains a popular choice for benchmarking data analysis skills. It consists of anonymized data from a 1994 census, featuring attributes like age, education, occupation, and income. This dataset provides a rich playground for applying and demonstrating the essential data analysis techniques that we'll be discussing. Here's how you might load the data, check for missing values, and then remove them using Python:

import pandas as pd

data = pd.read_csv('adult.csv')
missing_values = data.isna().sum()
missing_values = missing_values[missing_values > 0]
print(missing_values)

Output:

workclass	963
occupation	966
native-country	274

data.dropna(inplace=True)

In this snippet, we use the pandas library to load the dataset, then use isna().sum() to filter the data and display how many missing values there are in each column. Finally, we remove any rows containing missing data using the dropna() method.

This is just the tip of the iceberg when it comes to data cleaning, but it illustrates how a few lines of code can make a big difference in the quality of your data.

While data cleaning may not be the most glamorous part of a data analyst's job, its advantages and benefits make it well worth your effort. By taking the time to understand your data and address any issues upfront, you'll be setting yourself up for analysis success down the line. If you want to practice data cleaning, this free guided project on Hacker News Posts is a great place to start.

2. Explore and Analyze Data

I know, it seems obvious to have "analyze data" in a list of skills for becoming a data analyst, but when it comes to essential skills, the ability to explore and analyze data is absolutely crucial. But what exactly does that mean?

At its core, data analysis is all about using data to answer specific questions. As a data analyst, you'll be tasked with taking a business question or need and digging into the relevant data to develop insights and solutions. This could involve anything from calculating key metrics to identifying trends and patterns.

Data exploration, on the other hand, is about going into the data to uncover interesting relationships or trends that could potentially bring value to the business. While exploration might be guided by a specific question, it can also be more open-ended - you might just be looking to see what the data can tell you.

Example Using SQL

Let's look at a quick example using SQL to explore the Adult dataset:

SELECT 
    education,
    AVG(hours_per_week) AS avg_hours,
    AVG(case when income = '>50K' then 1 else 0 end) AS pct_high_income  
FROM 
    adult
GROUP BY
    education;

This query groups the data by education level, and calculates the average hours worked per week (AVG(hours_per_week)). It also calculates the percentage of high income earners (AVG(case when income = '>50K' then 1 else 0 end)) for each group. The GROUP BY clause is used to aggregate the data by education level. By comparing these metrics across education levels, we can start to uncover potential relationships between education, work hours, and income.

As a data analyst, having strong skills in both structured analysis and open-ended exploration will be key to driving impact and uncovering valuable insights. And while AI tools are rapidly enhancing efficiency and analytical capabilities, these foundational skills will remain essential. If you're looking to thrive in the data-driven world, there's no better time than now to start building your skills in data analysis and exploration.

3. Apply Statistical Knowledge

Mastering statistical concepts is essential for any aspiring data analyst. A solid grasp of statistics will guide your data exploration, help you draw valid conclusions, and avoid common pitfalls in your analysis.

Key statistical concepts for data analysts include:

• Correlation: Measuring the relationship between two variables
• Regression: Modeling the relationship between a dependent variable and one or more independent variables
• Hypothesis testing: Assessing whether a hypothesis about a population is likely to be true based on a sample

The depth of statistical knowledge required may vary depending on your specific role and industry. For instance, if your company heavily relies on probabilistic analysis, you'll need a more rigorous understanding of probability theory.

Applying Statistics with Excel

Let's look at a practical example of using statistics with the Adult dataset in Excel. We can use Excel's built-in functions and Analysis ToolPak, an add-in that provides a suite of advanced statistical tools, to perform sophisticated statistical analysis without writing complex code.

For instance, to calculate the correlation between age and hours-per-week:

1. Install the Analysis ToolPak in Excel
2. Select the age and hours-per-week columns
3. Go to Data > Data Analysis > Correlation
4. Specify the input range and click OK

Excel will output the correlation matrix above showing that the strength of the relationship between these two variables is quite weak.

Similarly, you can use Excel to perform regression analysis and hypothesis tests. Its accessibility makes it a great starting point for learning statistical concepts.

Advanced Statistics with R

For more complex statistical computing, many data analysts turn to R. Its extensive library of statistical packages and powerful data manipulation capabilities make it a top choice for statisticians and data scientists alike.

Whether you're just starting out or looking to deepen your statistical knowledge, investing in these skills will pay dividends throughout your data analytics career. By understanding the why behind the numbers, you'll be equipped to uncover valuable insights, drive better business decisions, and set yourself apart in the job market. As you continue to build your data analytics skillset, don't underestimate the power of a strong statistical foundation.

4. Create Data Visualizations

Data visualizations are a powerful tool for uncovering trends and patterns in your data. By presenting information visually, you can help others quickly grasp key insights that might be hidden in a spreadsheet of numbers. In fact, research shows that the human brain processes visual information 60,000 times faster than text―that's up to three times faster than blinking!*

As a data analyst, creating clean, compelling visualizations will be a core part of your job. This means choosing the right type of chart for your data, designing visuals that are easy to interpret, and avoiding common pitfalls like overcrowding your charts or distorting the data with misleading axis values.

But data visualization isn't just about presenting your findings to others. It's also a key part of the data exploration process. Sometimes patterns or outliers will jump out at you in a visual format that you might have missed by looking only at the raw numbers.

Creating Interactive Visualizations with Tableau

Let's look at an example of creating visualizations using Tableau and the Adult dataset. Tableau's intuitive drag-and-drop interface makes it a great choice for quickly exploring your data visually.

Suppose we want to visualize the distribution of age in our dataset and see if there's a relationship between age and income. We could create a histogram of the age variable and color the bars by income category:

1. Connect to the Adult dataset
2. Right-click on the Age measure and select Create > Bins... then click OK
3. Place this new measure on the Columns shelf
4. Drag the generated field that counts the number of records to the Rows shelf
5. Place the Income dimension on the Color card

Tableau will create an interactive histogram showing the count of records in each age group, color-coded by income level. We can quickly see that the distribution is roughly normal, with a peak around the mid-thirties, and that higher incomes are more common in the late-thirties to early-forties age ranges.

With just a few clicks, we've gained some valuable insights into our data. And by publishing our visualization to Tableau Server or Tableau Public, we can easily share it with stakeholders across the organization.

Whether you're using Tableau, Excel, Python libraries like Matplotlib and Seaborn, or any other data visualization tool, the ability to create informative, visually appealing charts and graphs is a skill that will serve you well throughout your data analytics career. By making data visualization a key part of your workflow, you'll be able to explore your data more effectively, share your insights more clearly, and drive better business decisions.

5. Create Dashboards and Reports

As a data analyst, one of your key responsibilities will be to empower others across your organization to leverage data for decision-making. By building intuitive dashboards and insightful reports, you can democratize data access and remove technical barriers for your non-technical colleagues.

Dashboards and reports can take many forms, from a simple combination of charts and tables with date filters, to sophisticated, interactive displays with hundreds of data points. Regardless of the scale, the goal is to provide an at-a-glance view of key metrics and enable users to explore the data to answer their own questions.

While specific job requirements may vary, nearly every data analyst role will involve producing reports on your findings and building dashboards to showcase key insights. Let's take a look at how you can use Microsoft Power BI, a powerful business intelligence tool, to create an impactful dashboard.

Building a Dashboard in Power BI

For this example, we'll use the Adult dataset to design a comprehensive dashboard in Power BI. Here's how you can get started:

1. Import the Adult dataset into Power BI Desktop
2. Use Power Query Editor to clean and transform the data as needed
3. Create a new report and add visualizations like:
• A donut chart showing the distribution of education levels
• A clustered column chart comparing average hours worked per week by occupation
• A scatter plot examining the relationship between age and income
4. Add interactive filters for age, education, and sex
5. Organize your visualizations and apply formatting to create an intuitive layout
6. Publish your report to the Power BI service and share it with your colleagues

By creating a single-page dashboard with multiple, interconnected visualizations and filters, you enable users to explore the data and uncover insights relevant to their specific needs. For instance, HR could use the occupation filter to compare hours worked across different roles, while management could use the age and income scatter plot to inform compensation decisions.

Remember, effective dashboards strike a balance between providing comprehensive information and maintaining simplicity. By carefully selecting your visualizations, creating clear labels and annotations, and leveraging interactive features, you can design dashboards that drive data-informed decision making across your organization.

Building powerful dashboards and reports is a critical skill for any data analyst. By leveraging tools like Power BI, you can bridge the gap between complex data and actionable insights.

6. Write and Communicate

Strong communication skills are essential for any data analyst to effectively share insights and drive action within their organization. From writing clear reports to presenting findings to key stakeholders, the ability to communicate in multiple formats is critical for success in this role.

Verbal Communication Skills

As a data analyst, you'll frequently collaborate with colleagues across various departments. Effective verbal communication is key in these interactions. For example, in a kickoff meeting with business stakeholders, active listening skills will help you fully understand the analysis requirements. Similarly, throughout the project, you may need to explain complex topics to non-technical teammates in a clear, accessible manner.

Written Communication Skills

Equally important are strong writing skills. You'll often need to summarize your analysis or share a key finding from your data exploration in written form. Whether crafting an email update or authoring a comprehensive report, communicating your message clearly and concisely is crucial.

Let's think about how effective written communication can be implemented using Jupyter Notebook and the Adult dataset. Suppose you've analyzed the data and identified a significant wage gap between men and women across all occupation categories. You could use a Jupyter Notebook to walk through your analysis, including code snippets and data visualizations, while weaving a compelling narrative around your findings. By combining technical details with clear explanations and data storytelling, you can create a powerful written report that inspires action on this critical issue.

Ultimately, the best analytical skills in the world are only valuable if you can effectively communicate your insights and convince others to act on them. While often considered a "soft skill," communication is essential for advancing your career and driving impact as a data analyst. If you've never used Jupyter Notebook before, I highly recommend checking out this free project that'll teach you the basics and walk you through installing it on your local computer.

7. Acquire Domain Knowledge

Domain knowledge refers to the understanding of topics and concepts specific to the industry and company you work for. It's the contextual information that enables you to make sense of the data you're analyzing and draw meaningful insights.

For instance, if you're working as a data analyst for an e-commerce company, a solid grasp of online retail metrics, customer behavior, and digital marketing principles will be crucial. On the other hand, if you're analyzing data for a manufacturing firm, understanding the intricacies of the production process, supply chain management, and quality control will be essential.

Regardless of your industry, a lack of domain knowledge can significantly hinder your ability to effectively perform your job as a data analyst. Without a clear understanding of the business context, you may struggle to identify the right questions to ask, select appropriate data sources, or interpret the results of your analysis in a way that drives actionable recommendations.

Applying Domain Knowledge

Let's consider a practical example using our trusty Adult dataset. Suppose you're working as a data analyst for a government agency focused on labor and employment. Your task is to identify factors that influence an individual's income level, with the goal of informing policies aimed at reducing income inequality.

With domain knowledge about socioeconomic factors and labor market dynamics, you might hypothesize that education level and occupation are key determinants of income. To test this, you could use Python libraries like pandas for data manipulation and sklearn for machine learning to run a logistic regression model predicting the probability of an individual earning over $50,000 per year based on their education and occupation:

import pandas as pd
from sklearn.linear_model import LogisticRegression

data = pd.read_csv('adult.csv')
X = pd.get_dummies(data[['education', 'occupation']])  
y = (data['income'] == '>50K').astype(int)

model = LogisticRegression()
model.fit(X, y)

print(model.coef_)

The model coefficients that are produced from the code above provide insights into the impact of each education level and occupation category on the likelihood of earning a high income. It reveals that the variable with the strongest positive correlation (2.47) is the one associated with an executive managerial position. Combining these quantitative results with your domain knowledge, you could develop targeted policy recommendations. This could lead to investing in education programs for underserved communities or promoting wage transparency across industries.

Bottom Line

While domain expertise can be developed on the job, aspiring data analysts can gain a significant advantage by proactively building knowledge in their target industry. This can be achieved through various methods, such as attending industry conferences, reading trade publications, or completing relevant online courses. By demonstrating a solid understanding of industry-specific concepts and trends, you'll position yourself as a strong job candidate and be better equipped to drive impact in your role from day one.

8. Solve Problems

In the fast-paced world of data analytics, you'll inevitably encounter challenges and roadblocks on a daily basis. Whether it's a quirk in your code, limited company resources, incomplete datasets, or looming deadlines, the ability to think critically and find creative solutions is a must-have skill for any data analyst.

Let's consider a real-world scenario using the Adult dataset and Python. Suppose you're tasked with building a model to predict whether an individual earns over $50,000 per year. However, upon exploring the data, you realize that the dataset is imbalanced - there are significantly fewer high earners compared to those earning less than $50,000. This can lead to biased models that struggle to accurately identify the minority class (high earners).

Tackling Imbalanced Datasets with SMOTE

To address this problem, you could leverage Python's machine learning libraries and apply techniques like SMOTE (Synthetic Minority Over-sampling Technique). SMOTE works by creating synthetic examples of the minority class, effectively balancing the dataset. It does this by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line. Here's how you might implement SMOTE in Python using the imbalanced-learn library:

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split

X = data[['age', 'education', 'hours-per-week']]  
X = pd.get_dummies(X, columns=['education'])
y = (data['income'] == '>50K').astype(int)

smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X, y)

X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2)

By creatively applying SMOTE, you've transformed an imbalanced dataset into one that's more suitable for training a fair and accurate model. This example demonstrates how strong problem-solving skills, coupled with technical expertise in Python and machine learning, can help you overcome common challenges in data analysis.

In addition to tackling technical issues, problem-solving in data analytics often involves thinking strategically about how to approach a project given constraints. For instance, if you're working with a tight deadline, you might need to make trade-offs in your analysis, prioritizing speed over exhaustiveness. In such cases, strong problem-solving skills will help you find innovative ways to streamline your workflow and deliver valuable insights under pressure.

Ultimately, as a data analyst, your problem-solving abilities will be tested daily. By cultivating a creative, solution-oriented mindset and continually expanding your technical toolkit, you'll be well-equipped to navigate the complex challenges of this dynamic field.

Will AI Replace Data Analysts?

I've done a lot of research in order to find an answer to this question―for both personal and professional reasons―and the one that makes the most sense to me is: "AI will not replace you, but people who use it may."* While this does make me smile, it also tells us the reality is that data analysts who embrace AI as a tool to automate mundane tasks and enhance their capabilities will be sought after, not replaced.

The rise of AI has sparked debates about its impact on various professions, including data analysis.* While the role will undoubtedly evolve in the coming years, those who possess foundational skills in problem-solving, analytical judgment, and insight generation will remain in high demand.

The Transforming Role of Data Analysts

As AI advances, it will increasingly take on routine tasks such as data cleaning, preprocessing, and basic analysis.* This shift will allow data analysts to focus more on interpreting results, providing strategic recommendations, and driving business decisions. The most valuable skills in this new era will be:

• Critical thinking and problem-solving abilities
• Creativity in approaching data challenges
• Effective communication and storytelling with data
• Strategic decision-making based on insights

Embracing the AI Opportunity

Ready for some exciting news? The rise of AI is opening up a world of opportunity for data analysts looking to take their skills to the next level. By proactively learning about AI and weaving it into your workflow, you can position yourself as an invaluable asset in our data-driven world.

Now, this doesn't mean you should abandon those tried-and-true data analysis skills you've worked so hard to master. Rather, it's about getting the most out of them with powerful new tools and techniques. The data analysts who will really shine in this transforming field are the ones who can seamlessly bridge the gap between classic analysis and cutting-edge AI.

Why Data Analysts Should Embrace Generative AI

As a data analyst, you're always looking for ways to work smarter, not harder. That's where generative AI comes in. By learning to leverage AI tools, you can open up a world of new opportunities. Here's why embracing generative AI is a no-brainer for any ambitious analyst:

1. Time: AI can take a load of routine tasks off our plate. Think of all the time-consuming but necessary work that eats up valuable hours. By automating these processes with AI, we can focus our energy on high-impact work like interpreting results and crafting strategic recommendations. As I mentioned earlier, data analysts spend a lot of time cleaning and preparing data. Even if we can scrape back half of it by using AI tools, imagine what we could achieve with all that time back in our day!
2. Efficiency: Generative AI can crunch through massive datasets at lightning speed, uncovering hidden patterns and insights that might slip past the human eye. By enhancing our analytical capabilities with AI, we can deliver deeper, more nuanced findings that drive real business impact.
3. Opportunity: Lastly―and perhaps most exciting of all―are the doors that AI skills can open. As organizations race to adopt AI, new roles are emerging at the intersection of analysis and AI. I doubt anyone who is an "AI Ethics Officer"* or "AI Security Specialist"* can say they've been in the field more than a couple of years.

Launching Your Data Analyst Career

Becoming a successful data analyst takes more than just technical prowess. It requires a diverse skill set, from data preparation and analysis to strategic problem-solving and effective communication. And in today's rapidly evolving field, embracing the power of AI is no longer optional - it's a must.

By mastering tools like generative AI, you'll increase your efficiency and analytical capabilities. This puts you in a great position for cutting-edge roles at the intersection of data and AI. The good news? Learning data analysis and AI doesn't have to be a daunting journey.

Investing in comprehensive data analytics projects will give you the skills and hands-on experience to stand out in the job market. You'll finish with a bolstered resume, and a compelling portfolio that showcases your ability to drive real business impact.

So if you're ready to launch your career as a data analyst, there's no better time to start than now.