Data analysis is one of those terms that “is what it sounds like,” although there’s more to it than may meet the eye. In any case, it’s a valuable skill for making better decisions—a skill that you can bring to bear on both your professional and personal life, from personal budgeting to analyzing customer experiences. Data analysis is more than a stepping stone to your career advancement, and no matter if you’re looking to upskill at work or kickstart a career in data analytics, the fascinating area of analyzing data is a superb springboard to help all across the board, too!
What is Data Analysis?
The history of data analysis dates back to the 1640s, when John Grant, a hatmaker, started collecting the number of deaths in London, and was the first person to use data analysis to solve a problem. Later on, Florence Nightingale—best known as a nurse from 1854—made significant contributions to medicine through data analysis, particularly in public health and sanitation and spurred on massive strides in life-saving tactics.
Then, in a similar vein—no pun intended—the team of J. F. Pantridge and J. S. Geddes revolutionized cardiac medicine in 1966, and what they did was to address the statistics of heart attack victims with the mobile defibrillator and a program to launch life-saving care to save those who experienced cardiac incidents at their place of attack, not waiting until they were brought to hospital.
An important thing to note is how the simple practice of data analysis has evolved and broadened over time, and what forms the bigger picture is “data analytics,” which employs data, tools, and techniques—and these topics are coming up in depth soon!—to discover new insights and make predictions.
Why is Data Analysis so Important Now?
Or, to put it in terms that show more about its value: how do businesses make better decisions, analyze trends, or invent better products and services? The simple answer is this; they leverage the distinct methods of data analysis to reveal insights that would otherwise get lost in the mass of information, and big data analytics is getting even more prominent owing to the below reasons.
1. Informed Decision-making
This one can pretty much seem to be the “leader,” not least since the modern business world relies on facts rather than it does on intuition—and it’s data analysis that serves as that all-important foundation of informed decision-making. It’s best here to show this in a UX (user experience) light, so consider what the role of data analysis in UX design is, specifically when it’s about dealing with non-numerical, subjective information.
Qualitative research gets right down into the “why” and “how” beneath—or behind—user behavior, and reveals nuanced insights. It provides a foundation for making well-informed decisions regarding color, layout, and typography, and if you apply these insights, it means you can create visuals that deeply resonate with your target audience.
2. Better Customer Targeting and Predictive Capabilities
It might sound close to a cliche—or even a truism—but data has become the lifeblood of successful marketing, and organizations do rely on data science techniques—and how they can create targeted strategies and marketing campaigns that work. Big data analytics helps uncover deep insights about consumer behavior—vital arteries of information to tap and channel to direct efforts later—for instance, Google collects and analyzes many different data types, and it examines search history, geography, and trending topics to deduce what consumers want.
3. Improved Operational Efficiencies and Reduced Costs
Another big benefit data analytics brings—or advantage of it—is streamlining operations and reducing organization costs, and it’s something that makes it easier for businesses to identify bottlenecks and opportunities for improvement, and so they’re more able to be in a better position to optimize resource allocation and—ultimately—bring costs down.
Procter & Gamble (P&G) is a leading company, and they use data analytics to optimize their supply chain and inventory management, and data analytics helps this industry leader reduce excess inventory and stockouts, achieving cost savings for them.
4. Better Customer Satisfaction and Retention
Customer behavior patterns enable you to understand how they feel about your products, services, and brand—vital sentiments to appreciate and comprehend at a deep level. But there’s more than that, in that different data analysis models help uncover future trends—and trends allow you to personalize the customer experience and improve satisfaction, so winning more “points”—of the kudos variety—and loyalty with your customers as time goes on and they stick with your brand.
The eCommerce giant Amazon learns from what each customer wants and likes, and what it then does is it recommends the same or similar products when they come back to the shopping app. Data analysis helps create personalized experiences for Amazon customers and improves user experience—and it’s part of what’s behind the “magic” customers feel that the brand knows them and the types of things they need, want, and enjoy.
Watch and enhance your knowledge by understanding “when” and “why” to use data analytics—UX Consultant, Expert, and Author William Hudson explains:
Types of Data Analysis Methods
“We are surrounded by data, but starved for insights.”
— Jay Baer, Customer Experience Expert & Speaker
The above quote is a neat capture of how strategic analysis must support data if it’s to produce meaningful insights, and there are ways to go about doing data analysis: to break them down, there are data analysis methods and there are data analysis techniques.
1. Quantitative Data Analysis
The name pretty much gives away what quant analysis is about—quantitative analysis means to look at the “what” that you’ve got: as in, the complex data, the actual numbers, or the rows and columns, and it’s something perhaps best illustrated through a scenario:
Your e-commerce company wants to assess the sales team’s performance and see how everyone’s doing on it, so you gather quantitative data on various key performance indicators (KPIs), and they’re things that include the number of units sold, the sales revenue, the conversion rates, and—last, but not least—the customer acquisition costs. So, from doing an analysis of these numeric data points, the company is then able to calculate its monthly sales growth, the average order value, and the return on investment (ROI) for each sales representative: more than a little important to have.
How does it help?
The quantitative analysis is a pretty handy thing that can help you identify the top-performing sales reps, the best-selling products, and the most cost-effective customer acquisition channels—and these are metrics that help the company make data-driven decisions and improve its sales strategy—ultra-important insights to be able to wield in a competitive marketplace.
2. Qualitative Data Analysis
Quant data can help with bucket-loads of insights, but—for all its value—there are situations where numbers in rows and columns are just impossible to fit, and that’s where qualitative research can help you understand the data’s underlying factors, patterns, and meanings via non-numerical means and get behind what’s going on.
To see “qual data” in action, imagine you’re a product manager for an online shopping app and what you want to do is to improve the app’s user experience and boost user engagement. You’ve got quantitative data that tells you what’s going on but not why—which is why you’ve got to collect customer feedback through interviews, open-ended questions, and online reviews, as well as conduct in-depth interviews to explore their experiences.
Watch as Professor of Human-Computer Interaction at University College London, Ann Blandford explains how you can elevate your interview preparation to a more professional level:
How does it help?
From reading and summarizing the comments, you can identify issues, sentiments, and areas that need improvement, and that qualitative insight is what can guide you to identify and work on areas of frustration or confusion—and alleviate them or remove them altogether.
Learn more about quantitative and qualitative user research as Author and Human-Computer Interaction Expert, Professor Alan Dix explains important points:
10 Best Data Analysis and Modeling Techniques
We—as in, the combined human efforts on Earth—generate over 120 zettabytes daily, which works out to be about 120 billion copies of the entire Internet in 2020, daily. To call that an ocean of info would perhaps be an understatement—and then some!—but it’s a humongous volume of data to trawl, and, without the best data analysis techniques, businesses—however big they are—will never be able to collect, analyze, and interpret data into real, actionable insights. And that’s why they—and you—need to sharpen their picture of data analysis into something that’s workable, and so apply the top methods of data analysis or top techniques of data analysis; there are “five-a-side" for quantitative and qualitative, to put it one way.
© Interaction Design Foundation, CC BY-SA 4.0
Quantitative Methods
1. Cluster Analysis
This one may sound familiar—and it goes by the names of segmentation analysis and taxonomy analysis, too—and it’s a method to identify structures within a dataset, a bit like sorting objects into different boxes (clusters) based on their similarities. That’s a massive help when it comes to working out what’s what, because data points within a similar group are similar to each other (homogeneous), and—likewise—they’re dissimilar to data points in another cluster (heterogeneous).
The point with cluster analysis is that you’re wanting to find hidden patterns in the data, and it can be your go-to approach if you require additional context to a trend or dataset.
Let’s say you own a retail store and you want to understand your customers better to tailor your marketing strategies to be spot-on for them; so, you collect customer data—including their shopping behavior and preferences. You’re after information that can help you target your marketing efforts so they’re more efficient—and effective. So, to cluster analyze things, you can help yourself group customers with similar behaviors and preferences. Customers who visit your store often and shop a great deal may form one cluster, while those who don’t shop so often and spend less could make up another cluster.
2. Regression Analysis
Regression analysis is a powerful data analysis technique—and it too may sound familiar, not least since it’s quite popular in economics, biology, and—sure—psychology. This one’s a technique that helps you understand how one thing (or more) influences another—a handy thing to look at for knock-on effects. For this one, suppose you’re a manager and you’re trying to predict next month’s sales—and you’ve got many factors, like the weather, promotions, or the buzz about a better product, as things that can affect these figures.
But there’s more, because some people in your organization might have their own theory as to what might impact sales the most—with remarks like, “When winter starts, our sales go up.”
All the above factors are “variables,” and now, the “dependent variable” is always going to be the factor being measured (in our example—the monthly sales), and then, what’s next is you’ve got your independent variables—and they’re the factors that might impact your dependent variable.
Regression analysis can mathematically sort out which variables have an impact, and this statistical analysis identifies trends and patterns and so is a vital way to make predictions and forecast possible future directions. You’ve got many types of regression analysis to pick from—and these include linear regression, non-linear regression, binary logistic regression (and there are more)—and the model you choose will to a very great degree depend upon the type of data you’ve got.
3. Monte Carlo Simulation
This one—also!—may look familiar, and it’s a mathematical technique that is an excellent way to estimate the possible outcomes of an uncertain event. And what more uncertain an event could one “hope” to find themselves in than being in a gambling house (and, to be sure, the method derives its name from the Monte Carlo Casino in Monaco).
All right, don’t worry about the gambling element—because business is serious business!—but let’s say you want to know how much money you might make from your investments in the stock market. So, you make thousands of guesses instead of just one guess—and then, you consider several scenarios, which could be a growing economy or an unprecedented catastrophe like Covid-19. In any case, what you want to do with this is to test many random situations to estimate the potential outcomes.
4. Time Series Analysis
This one is pretty much what it says “on the label”—it’s to analyze data collected over time, and it’s a good way to spot trends and cycles over time periods, where one data set recorded at different intervals helps you understand patterns and so make forecasts. The sorts of industries that leverage time-series analysis to predict trends include finance, retail, and economics—which is more than a little understandable when one considers that they deal with ever-changing currency exchange rates and sales data.
To be sure, time series analysis at work in the stock market shows up as a prime example of this technique in action, not least since many stocks exhibit recurring patterns in their underlying businesses (due to seasonality or cyclicality)—and time-series data analysis can uncover these patterns in a neat way so investors can take advantage of seasonal trading opportunities—or adjust their portfolios as they need to.
Time series analysis is part of predictive analytics. It can show likely changes in the data to give up a better understanding of data variables—and make for better forecasting.
5. Cohort Analysis
Cohort analysis involves breaking down datasets into relative groups (or cohorts) too—like cluster analysis—but, in this method, you focus on studying the behavior of specific groups over time, so you can understand different groups’ performance within a larger population. Not for nothing, then, is this technique popular amongst marketing, product development, and user experience research teams.
To see why, let’s say you’re an app developer and want to understand user engagement over time; so, with this method, you define cohorts based on a familiar identifier (which could be the demographics, app download date, or users making an in-app purchase). In this way, your cohort represents a group of users who had a similar starting point. Then, with the data in hand, you analyze how each cohort behaves over time and look at some things in particular—such as, do users from the US use your app more frequently than people in the UK?
Qualitative Methods
6. Content Analysis
Now to get into the “qual” side of things; when you think of “data” or “analysis,” do you think of text, audio, video, or images? Probably not—you might be thinking of the “dry” stuff, like number crunching—but, to be sure, these forms of communication are an excellent way to uncover patterns, themes, and insights.
In fact, this one sees wide use in marketing, and content analysis can reveal public sentiment about a product or brand—which makes it more than a little valuable in such a “vocal” digital age. For instance, analyzing customer reviews and social media mentions can help brands discover hidden insights.
What’s more, this technique splits into two further categories: conceptual analysis (which focuses on explicit data, such as the number of times a word repeats in a piece of content) and relational analysis (which examines the relationship between different concepts or words and how they connect). It’s not about counting but about understanding how things fit together, and a user experience technique—or a UX technique—called card sorting can help with this. This technique involves counting and measuring the frequency of categorical data, and it also studies the meaning and context of the content, and that’s why content analysis can be both quantitative and qualitative.
7. Sentiment Analysis
This one also goes by the name of opinion mining, and it’s a technique that is a valuable business intelligence tool—and it can help you to enhance both your products and your services. In the modern business landscape, there are substantial amounts of textual data, including emails, social media comments, website chats, and reviews. You often need to know whether this text data conveys a positive, negative, or neutral sentiment, so you can act according to it to optimize things.
Sentiment analysis tools help scan this text to determine the emotional tone of the message automatically, and the insights from sentiment analysis are far more than a little helpful in terms of how they can improve customer service and elevate brand reputation to levels that are healthy—and prosperous.
8. Thematic Analysis
No matter whether you’re an entrepreneur, a UX researcher, or a customer relationship manager—thematic analysis is something that can help you better understand user behaviors and needs.
The thematic technique analyzes large chunks of text data—or textual data—such as transcripts or interviews, and it then groups them into themes or categories that come up often within the text. The thematic method uses just qualitative data, and—what’s more—it’s a very subjective technique since it depends on the researcher’s experience to derive insights.
9. Grounded Theory Analysis
You can think of grounded theory as being something that you—as a researcher—might do, and instead of starting with a hypothesis and trying to prove or disprove it, you get ahold of and collect information and construct a theory as you go along. It’s like a continuous loop—a loop where you collect and examine data and then create a theory based on what you discover, and you keep on repeating this process until you’ve squeezed out all the insights from the data that you can.
This method allows theories to emerge in a natural way from the information, which makes it a flexible and open way to explore new ideas; and it might be right up your street for what you need to do. Another thing to consider is that grounded theory is the basis of a popular user-experience research technique—contextual enquiry.
10. Discourse Analysis
Discourse analysis is a popular analytics approach in linguistics, sociology, and communication studies—and the aim of it is to understand the meaning behind written texts, spoken conversations, or visual and multimedia communication. The things it seeks to uncover are how individuals structure a specific language, what lies behind it, and how social and cultural practices influence it.
For instance, if you’re working as a social media manager and if you analyze social media posts, you go beyond the text itself—and you would consider the emojis, hashtags, and even the timing of the posts. For example, you might find that a particular hashtag is used to mobilize a social movement.
The Data Analysis Process: Step-by-Step Guide
© Interaction Design Foundation, CC BY-SA 4.0
1. Problem Identification
The first step in the data analysis process is to find out what the problem you’re trying to solve is—so, what research question do you want to address with your data analysis? For instance, if you’re an analyst who’s working for an e-commerce company and there’s been a recent fall in sales and the company needs to know why this is happening, then the problem statement is to find the reason for the decline in sales.
2. Data Collection
The next step is to collect data and get in the information you need—and you can do this through various internal and external sources; for example, surveys, questionnaires, focus groups, interviews, and the like.
Delve deeper into the intricacies of data collection with Ann Blandford in this video:
What the key here is is to collect and aggregate the appropriate statistical data—and, by “appropriate,” we mean the data that could help you understand the problem and build a forecasting model, and this data can be quantitative (sales figures) or qualitative (customer reviews).
All types of data can fit into one of three categories: first-party data is data that you, or your company, can collect directly from customers; second-party data is the first-party data of other organizations (for instance, sales figures from one of your competitor companies); and then third-party data, which is data that a third-party organization can collect and aggregate from numerous sources (things like government portals or open data repositories).
3. Data Cleaning
Now that you’ve got the necessary data in, the next step is to prepare it for analysis—which means you’ve got to clean or scrub it, which is essential since acquired data can be in different formats. Cleaning makes sure you’re not dealing with bad data and your results are dependable. In any case, some critical data-cleaning steps are to remove white spaces, duplicates, and formatting errors; to delete unwanted data points; and to bring structure to your data.
For survey data, you’re going to need to do consistency analysis, too, and some of this relies on good questionnaire design, but you also need to ensure that respondents are not “straight-lining” (all answers in a single column); that similar questions get answered consistently; and that open-ended questions contain plausible responses.
Watch as William Hudson explains important points about ensuring data quality:
4. Data Analysis
Now’s where the actual analytics begin in earnest, and this is the stage where you’d be ready to leverage any one or more of the data analysis and research techniques mentioned above. The choice of technique depends on the data you’re dealing with and the results you desire to get, and all types of data analysis fit into the following four categories.
© Interaction Design Foundation, CC BY-SA 4.0
A. Descriptive Analysis
Descriptive analysis focuses on what happened—and it’s the starting point for any research before you’d proceed with deeper explorations, and, as the first step, it involves breaking down data and summarizing its key characteristics.
B. Diagnostic Analysis
This analysis focuses on why something has happened—and pretty much just as a doctor uses a patient’s diagnosis to uncover a disease, you can use diagnostic analysis to understand the underlying cause of the problem.
C. Predictive Analysis
This third type of analysis allows you to identify future trends based on historical data, and it generally uses the results from the above analysis, machine learning (ML), and artificial intelligence (AI) to forecast future growth.
D. Prescriptive Analysis
Now you know what to do, you’re also going to need to understand how you’ll do it—enter prescriptive analysis, which aims to determine your research’s best course of action.
5. Data Interpretation
This step you can think of being like connecting the dots in a puzzle, and this one is where you start making sense of all the data and analysis done in the previous steps. It’s where you dig deeper into your data analysis findings and visualize the data to present insights in meaningful and understandable ways, and so get it worked out well to know what’s going on.
Explore this comprehensive video resource to understand the complete user research data analysis process:
The Best Tools and Resources to Use for Data Analysis in 2023
Data Analysis Tool | Type | Ideal For | Best For | Pricing |
Microsoft Excel | Spreadsheet | Business Analysts, Managers | Basic data manipulation | Paid (Microsoft 365) |
Google Sheets | Spreadsheet | Individuals, Small-Medium Businesses | Basic data analysis and collaboration | Free with Paid upgrades |
Google Analytics | Web Analytics | Digital Marketers, Web Analysts | Digital marketing analysis | Free and Paid (Google Analytics 360) |
RapidMiner | Data Science | Data Scientists, Analysts | Predictive analytics | Free and Paid (various licensing options) |
Tableau | Business Analysts, Data Teams | Interactive dashboards | Paid (various plans) | |
Power BI | Business Intelligence | Business Analysts, Enterprises | Business reporting | Paid (various plans) |
KNIME | Visual Workflow | Data Scientists, Analysts | Data science workflows | Free and Open-source |
Zoho Analytics | Business Intelligence | Small-Medium Businesses | Collaboration and reporting | Paid (various plans) |
Qlik Sense | Business Intelligence | Business Analysts, Data Teams | Interactive analysis | Paid (various plans) |
1. Microsoft Excel
The world’s best and most user-friendly spreadsheet software features calculations and graphing functions—and it’s ideal for non-techies to perform basic data analysis and create charts and reports.
Pros
No coding is required.
User-friendly interface.
Cons
Runs slow with complex data analysis.
Less automation compared to specialized tools.
2. Google Sheets
Similar to Microsoft Excel, Google Sheets stands out as a remarkable and cost-effective tool for fundamental data analysis. Not only does it handle everyday data analysis tasks, including sorting, filtering, and simple calculations, but it is known for its seamless collaboration capabilities as well.
Pros
Easily accessible.
Compatible with Microsoft Excel.
Seamless integration with other Google Workspace tools.
Cons
Lacks advanced features such as in Microsoft Excel.
May not be able to handle large datasets.
3. Google Analytics
Digital marketers and web analysts use this one widely, and this tool helps businesses understand how people interact with their websites and apps, and it provides insights into website traffic, user behavior, and performance to make data-driven business decisions.
Pros
Free version available.
Integrates with Google services.
Cons
Limited customization for specific business needs.
May not support non-web data sources.
4. RapidMiner
RapidMiner is ideal for data mining and model development, and it’s a platform that offers remarkable machine learning and predictive analytics capabilities. It allows professionals to work with data at many stages, including preparation, information visualization, and analysis.
Pros
User-friendly interface.
Excellent support for machine learning.
Large library of pre-built models.
Cons
Can be expensive for advanced features.
Limited data integration capabilities.
5. Tableau
Tableau is one of the best commercial data analysis tools you can get, and it’s famous for its interactive dashboards and data exploration capabilities. Data teams can create visually appealing and interactive data representations through its easy-to-use interface and powerful capabilities.
Pros
Intuitive drag-and-drop interface.
Interactive and dynamic data visualization.
Backed by Salesforce.
Cons
Steeper learning curve for advanced features.
6. Power BI
This one is an excellent choice for creating insightful business dashboards, and among its nifty features it boasts incredible data integration features and interactive reporting, so making it ideal for enterprises.
Pros
Intuitive drag-and-drop interface.
Interactive and dynamic data visualization.
Backed by Salesforce.
Cons
Steeper learning curve for advanced features.
7. KNIME
The name is short for Konstanz Information Miner, and KNIME is an outstanding tool for data mining, with its user-friendly graphical interface that makes it accessible even to non-technical users, enabling them to create data workflows easily. What’s more, KNIME is a cost-effective choice, and hence, it is ideal for small businesses operating on a limited budget.
Pros
Visual workflow for data blending and automation.
Active community and user support.
Cons
Complex for beginners.
Limited real-time data processing.
8. Zoho Analytics
Artificial intelligence and machine learning fuel this one, and Zoho Analytics is a robust data analysis platform. A neat point is how its data integration capabilities empower you to seamlessly connect and import data from diverse sources while offering an extensive array of analytical functions.
Pros
Affordable pricing options.
User-friendly interface.
Cons
Limited scalability for very large datasets.
Not as widely adopted as some other tools.
9. Qlik Sense
Qlik Sense offers you a wide range of augmented capabilities, and it’s got everything from AI-generated analysis and insights to automated creation and data prep, machine learning, and predictive analytics.
Pros
Impressive data exploration and visualization features.
Can handle large datasets.
Cons
Steep learning curve for new users.
How to Pick the Right Tool?
Consider the below factors to find the perfect data analysis tool for your organization:
Your organization’s business needs.
Who needs to use the data analysis tools?
The tool’s data modeling capabilities.
The tool’s pricing.
How to Become a Data Analyst?
Data analysts are in high demand—and that’s owing to the soaring data boom across various sectors—and so it’s possible to enjoy quite some success in this rewarding role. As per the US Bureau of Labor Statistics, the demand for data analytics jobs will grow by 23% between 2021 and 2031. What’s more, roles offer excellent salaries and career progression, and as you gain experience and climb the ranks, your pay scales upwards, which makes it one of the most competitive fields in the job market.
Learn data analytics methodology and it can help you give an all-new boost to your career, and here are some tips to become a data analyst:
1. Take an Online Course
You don’t necessarily need a degree to become a data analyst—to be sure, a degree can give you solid foundational knowledge in relevant quantitative skills, but so can certificate programs or university courses.
2. Gain the Necessary Technical Skills
Getting hold of a set of specific technical skills will help you deepen your analytical capabilities—and you’ll need to explore and understand the data analysis tools to deal with large datasets and comprehend the analysis.
3. Gain Practical Knowledge
You can work on data analysis projects to showcase your skills, and then, create a portfolio highlighting your ability to handle real-world data and provide insights. If internship’s something you’re up for, you’ll find you can also seek internship opportunities that provide valuable exposure and networking opportunities.
4. Keep Up to Date with the Trends
Since data analysis is rapidly evolving, it’s pretty much a foregone conclusion that you’ll need to keep pace with cutting-edge analytics tools, methods, and trends—something you can do through exploration, networking, and continuous learning.
5. Search for the Ideal Job
The job titles and responsibilities are going to keep on changing—and expanding—in data analytics. Beyond “Data Analyst,” you can explore titles like Business Analyst, Data Scientist, Data Engineer, Data Architect, and Marketing Analyst, but—in any event—your knowledge, education, and experience can guide your path to the right data job.
The Take Away
Data analysis encompasses a wealth of factors and concepts like the four main types of data analysis (descriptive, diagnostic, predictive, and prescriptive) and the types of data analysis techniques for both quantitative (the “what,” such as cluster analysis and regression anaylsis) and qualitative analysis (the “why,” such as content analysis and sentiment analysis).
We have designed The IxDF courses and certifications to align with your intellectual and professional objectives. If you haven’t already, take the initial step toward enriching your data analytics skills by signing up today. Your journey to expertise in data analysis awaits.
References and Where to Learn More
1. Learn the most sought-after tool, Microsoft Excel, from basic to advanced in this LinkedIn Microsoft Excel Online Training Course.
2. Ensure all the touchpoints of your service are perfect through this certification in Service Design.
3. Learn more about the analytics data types we encounter daily in this video.
Video copyright info
Author: Stewart Cheifet. Appearance time: 0:22 - 0:24. Copyright license and terms: CC / Fair Use. Modified: Yes. Link: https://archive.org/details/CC1218greatestgames
4. Read this free eBook, The Elements of Statistical Learning, to boost your statistical analysis skills.
5. Check out Python for Data Analysis to learn how to solve statistical problems with Python.
6. Join this beginner-level course and launch your career in data analytics. Data-Driven Design: Quantitative UX Research Course
