Data analysts help leaders make informed decisions, optimize operations, understand their customers, and enable companies to understand where they might have an advantage. Companies expect analysts to be great problem solvers with a strong technical skillset.

Vladislav Bilay, DevOps engineer at Aquiva Labs, tells Dice: “Data analysis relies on tools like Python, R, SQL, and Excel to extract insights from data. Python and R are powerful for data manipulation and statistical analysis, while SQL is key for querying databases and joining tables. Excel, though basic, remains widely used for quick analysis and visualization.”

Strong and insightful data analysis, knowledge of SQL, and programming languages like Python and R are critical competencies that every data analyst must have. But how does that impact the ebb and flow of a data analyst job interview?

During a job interview, data analysts can expect to field a variety of questions. Hiring managers and recruiters are interested in whether a candidate can use common (as well as esoteric) data-analysis tools, and how their analysis has positively impacted their previous employers’ strategies and bottom lines.

As a data analyst, you’ll certainly field questions about past work experience, including the size and complexity of datasets you’ve analyzed. Depending on the prospective employers’ needs and tech stack, you might face questions about your mastery of flat-file databases, Hadoop, Apache Spark, SQL or NoSQL, and other tools and platforms.

Josh Drew, regional director at Robert Half Technology, said data analyst candidates can also expect questions regarding their specializations and future career plans. “If the company has a specific requirement where they’re looking for everybody to get a certain level of proficiency within certain tools, you should be ready to answer questions about your ability and readiness to receive training and development for those skill sets,” he added.

Sample Questions: The Big Picture

• “What technology tools have you used?”
• “How did you use data in your last role to help drive the business?”
• “What was the end goal of the most recent initiatives or projects that you were working on?”
• “What stakeholders did you interact with on a regular basis? How did you share your findings?”
• “Did you supervise or manage teams? What specifically was your role within the data team?”

These “big picture” questions pop up frequently. Make sure you can describe how your analysis played a crucial role in your previous employers’ planning and strategies. Come prepared with stories about how you utilized your “soft skills” (such as empathy and communication) to not only lead teams, but also convey your results and ideas to other stakeholders throughout the organization, such as senior executives.

Recruiters and hiring managers want to know you can yield meaningful results for organizations and take a 35,000-foot view on crucial problems. If you can demonstrate that you’re more than capable of abstract thought and in-depth strategizing, your chances of landing the job will exponentially increase.   

With data analytics tools embracing technologies such as artificial intelligence (A.I.) and machine learning (ML), DevSkiller CTO Tomasz Nurkiewicz said candidates should also be ready to answer simple questions about their familiarity with A.I. and ML.

“There are a few very simple algorithms like linear regression, which if someone claims to know anything about like machine learning, they should know what it is,” he said.

Sample Questions: Data Skills

• “What are natural networks, and how can how can they help in each position?”
• “What is clustering?”
• “What is linear regression?”
• “How has your analysis supported business decisions?”

Drew pointed out that, on both the business and data side, it's extremely helpful when you can apply a business brain and logic to the data you're reviewing or assessing. “You want to get across that you truly understand the business and the market that they're in,” he said. “The ultimate goal is to be able to not just take this information but give suggestive ideas to leadership on how the data can drive the business.”

He said an interview is a chance to demonstrate that you’re naturally inquisitive and really understand the power of this data. “You want a data analyst that has really strong communication skills,” he added. “If it's simply compiling the data, that's not necessarily as important. But when you're sharing and giving business insights and suggestions you need to be persuasive and share the argument behind the data and why you came up with the suggestion.”

Hiring managers will often frame these questions in a certain way; for instance, they might ask whether you’re comfortable talking to executives and sharing insights from data. “That comfort level of sharing ideas and supporting business decisions around the data is extremely beneficial,” Drew said. “This is also an opportunity for you to demonstrate your passion for data and the power it holds.”

Sample Questions: The Future of Data

• What excites you about data?
• Where do you think the future of data is headed?

If data analyst applicants just speak about technologies and tools and math, it suggests to the interviewer that they have absolutely no interest in in the business—and that’s a problem, since data analysts are ultimately there to serve the larger business. “I would look for balance—a person who sees a business problem and uses technology to solve that problem,” Nurkiewicz said. “You create value by solving problems with code.”

When it comes to any tests given as part of the interview process, data analysts may expect to be asked to comb through data sets to look for patterns and offer some insights into what they find. These kinds of open-ended questions can reveal how the analyst thinks and demonstrate whether or not they understand the company’s business goals.

“This way you are also making sure the candidate knows what Hadoop or HDFS is and knows how to load data from that file system and then doing some aggregation in the tool of choice,” Nurkiewicz said. “If you are at least moderately proficient with your tools, you should be able to come up with certain relationships.”

Key Concepts in Data Analysis

If you're a tech professional looking to expand your skill set and career opportunities, data analysis is a valuable path to explore. This field involves collecting, cleaning, and interpreting data to uncover insights that can drive decision-making. Here are some key concepts to understand:

1. Data Collection:

• Sources: Identify where data comes from, such as databases, APIs, sensors, or surveys.
• Types: Understand different types of data, including numerical (quantitative) and categorical (qualitative).

2. Data Cleaning:

• Errors: Recognize and correct errors or inconsistencies in the data, such as missing values or outliers.
• Formatting: Ensure data is in a consistent format for analysis.

3. Data Exploration:

• Summary Statistics: Calculate measures like mean, median, mode, and standard deviation to get a basic understanding of the data.
• Visualization: Create graphs, charts, and other visual representations to explore patterns and relationships.

4. Data Analysis Techniques:

• Descriptive Analysis: Summarize and describe the data to understand its key characteristics.
• Predictive Analysis: Use statistical models to predict future outcomes based on past data.
• Prescriptive Analysis: Recommend optimal decisions based on data-driven insights.

5. Data Interpretation:

• Insights: Extract meaningful conclusions from the analysis and communicate them effectively.
• Decision Making: Use the insights to inform strategic decisions and improve processes.

Bilay adds: “Data analysis relies on tools like Python, R, SQL, and Excel to extract insights from data. Python and R are powerful for data manipulation and statistical analysis, while SQL is key for querying databases and joining tables. Excel, though basic, remains widely used for quick analysis and visualization.”

SQL Mastery for Data Analysts

SQL (Structured Query Language) is a foundational language for anyone working with data, and data analysts are no exception. Its versatility, power, and ubiquity make it an indispensable tool in their toolkit. This article delves into why mastering SQL is crucial for data analysts and explores how they leverage it in their daily workflows.

The Role of SQL in Data Analysis

SQL serves as the primary language for interacting with relational databases, which store structured data in tables. For data analysts, these databases are often the repositories of the raw materials they work with. SQL enables them to:
 

• Extract Data: Retrieve specific data points or entire datasets from databases based on predefined criteria.
• Transform Data: Clean, filter, and manipulate data to prepare it for analysis.
• Aggregate Data: Summarize and group data to identify patterns and trends.
• Join Data: Combine data from multiple tables to create a more comprehensive view.
• Analyze Data: Perform statistical calculations and apply analytical techniques to extract insights.

How Data Analysts Use SQL in Practice

To understand how SQL is used in data analysis, let's consider a common scenario: analyzing customer behavior to identify potential churn. A data analyst might follow these steps:
 

1. Data Extraction: Use SQL to extract relevant customer data from a database, including purchase history, demographics, and customer support interactions.
2. Data Cleaning: Clean the data by handling missing values, correcting inconsistencies, and formatting data appropriately. SQL functions like COALESCE, TRIM, and CASE are essential for this task.
3. Data Transformation: Create new features or transform existing ones to facilitate analysis. For instance, the analyst might calculate customer lifetime value or create a recency-frequency-monetary (RFM) score.
4. Data Aggregation: Group customers by segments (e.g., high-value, low-value) or time periods to identify trends in purchasing behavior. SQL's GROUP BY and HAVING clauses are invaluable for this.
5. Data Joining: Combine customer data with other relevant datasets, such as product information or marketing campaign data, to gain a more complete picture.
6. Data Analysis: Apply statistical functions and analytical techniques to analyze the data. For example, the analyst might calculate churn rates, conduct hypothesis testing, or build predictive models using SQL-based tools or libraries.
7. Data Visualization: Create visualizations (e.g., charts, graphs) to present the findings in a clear and understandable manner. While SQL itself doesn't handle visualization directly, it provides the foundation for data preparation that enables the use of visualization tools.

Beyond Basic Queries: Advanced SQL Techniques

While basic SQL operations are essential, data analysts often need to employ more advanced techniques to tackle complex tasks. These include:
 

• Window Functions: Calculate running totals, moving averages, and rank data within groups.
• Common Table Expressions (CTEs): Create temporary result sets that can be referenced multiple times within a query.
• Subqueries: Nest queries within other queries to perform more complex filtering and aggregation.
• Stored Procedures: Create reusable code blocks to automate common tasks and improve query performance.

The Benefits of Mastering SQL

Proficiency in SQL offers numerous advantages for data analysts:

• Efficiency: SQL allows for efficient data manipulation and analysis, saving time and effort.
• Flexibility: SQL can be used with a wide range of databases, providing flexibility in data sources.
• Collaboration: SQL is a common language understood by many data professionals, facilitating collaboration and knowledge sharing.
• Career Advancement: SQL skills are highly sought after in the data analytics field, opening doors to new opportunities and higher salaries.

If you’re totally new to SQL, make sure you spend time mastering the basic concepts before moving onto the harder technical stuff. “Joining tables in SQL involves combining rows from two or more tables based on a related column, Bilay points out. “The most common types are INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, each determining how rows are matched across tables.”

Being able to manage massive amounts of data and executing complex queries makes you a more efficient data analyst. You’ll often be tasked with finding correlations within large datasets, too, making the ability to query effectively critical for business continuity and growth.

Advanced Data Analytics Techniques

Bilay notes: “Data science interviews often include technical, conceptual, and behavioral questions. You might face coding challenges, especially in Python or R, and be asked about statistical methods, machine learning algorithms, and case studies. Knowing the difference between supervised (with labeled data) and unsupervised learning (finding patterns in unlabeled data) is important.

“In data analyst interviews, case studies may require analyzing a dataset, identifying trends, and making recommendations. Statistical methods like regression analysis, hypothesis testing, and ANOVA are commonly discussed, as they are crucial for making data-driven inferences.”

As the field of data analytics continues to evolve, data analysts who can leverage advanced techniques are in high demand. These techniques enable professionals to extract deeper insights from complex datasets, solve more challenging problems, and contribute significantly to business success. This article explores some of the key advanced data analytics techniques that data analysts should strive to master.

Machine Learning

Machine learning, a subset of artificial intelligence, involves training algorithms to learn from data and make predictions or decisions. Data analysts who understand machine learning can apply techniques like:

• Regression Analysis: Predict numerical outcomes (e.g., sales, customer churn).
• Classification: Categorize data into predefined classes (e.g., spam or not spam).
• Clustering: Group similar data points together (e.g., customer segmentation).
• Time Series Analysis: Analyze data collected over time to identify trends and patterns.

Deep Learning

Deep learning is a specialized form of machine learning that uses neural networks with multiple layers to learn complex patterns from large datasets. It's particularly effective for tasks involving:

• Image and Video Analysis: Recognizing objects, faces, and actions in visual data.
• Natural Language Processing (NLP): Understanding and processing human language, including text and speech.

Natural Language Processing (NLP)

NLP techniques allow computers to understand, interpret, and generate human language. Data analysts can apply NLP to:

• Sentiment Analysis: Determine the sentiment expressed in text data (e.g., positive, negative, neutral).
• Text Classification: Categorize text documents into predefined categories (e.g., news articles, product reviews).
• Text Summarization: Create concise summaries of lengthy text documents.

Time Series Analysis

Time series analysis focuses on data collected over time. Advanced techniques include:

• ARIMA Models: Autoregressive Integrated Moving Average models for forecasting time series data.
• Prophet: A Facebook open-source forecasting tool that combines statistical models with machine learning.
• Decomposition: Breaking down time series data into trend, seasonality, and residual components.

Bayesian Statistics

Bayesian statistics provides a framework for updating beliefs based on new evidence. It's useful for:

• A/B Testing: Comparing the effectiveness of different treatments or interventions.
• Recommendation Systems: Suggesting products or content based on user preferences.
• Risk Assessment: Quantifying uncertainty and making informed decisions.

Reinforcement Learning

Reinforcement learning involves training agents to make decisions in an environment to maximize rewards. It's applicable to:

• Game Playing: Developing AI agents that can play games like chess or Go.
• Robotics: Controlling robots to perform tasks in complex environments.
• Personalized Marketing: Optimizing marketing campaigns based on customer interactions.

Advanced Visualization

Effective visualization is crucial for communicating insights to stakeholders. Advanced techniques include:

• Interactive Dashboards: Creating dynamic visualizations that allow users to explore data interactively.
• 3D Visualization: Visualizing complex data in three dimensions for better understanding.
• Geographic Information Systems (GIS): Analyzing spatial data to identify patterns and relationships.

Cloud Computing and Big Data Technologies

As datasets grow in size and complexity, cloud computing and big data technologies become essential. Data analysts should be familiar with:

• Cloud Platforms: AWS, Azure, GCP, and their data analytics services.
• Big Data Frameworks: Hadoop, Spark, and their components for processing large-scale data.
• NoSQL Databases: MongoDB, Cassandra, and other databases designed for unstructured data.

By mastering these advanced data analytics techniques, data analysts can position themselves as valuable assets to organizations. They can contribute to innovative solutions, drive data-driven decision-making, and unlock new career opportunities.

Mastering Data Visualization Tools

Tableau and Power BI are unique platforms data analysts will use on a daily basis, depending on which your company has invested in. Though unique, both offer dashboard-centric views that help you quickly and easily understand the data you interact with.

Moreover, each make high-level findings easy for stakeholders to understand at a glance.

For most data analysts, Tableau is the platform of choice for its interoperability with other platforms, but also because it’s familiar for those who have a firm grasp on SQL. Tableau offers similar joins to SQL—inner, left, right, and full outer—within a visual interface. It also provides data blending for combining data from different sources, offering flexibility for complex datasets.

Real-World Applications: The Data Analysis Project

Bilay tells Dice: “Questions in data analyst interviews vary from technical queries about tools and methods to general problem-solving approaches. Data analysts typically fall into four categories: those focused on reporting, exploration, predictive analytics, or data engineering.”

A successful data analysis project involves data collection, data cleaning, data prepration, modeling clean data, creating visualizations of modeled data, communicating your insights, and offering actionable items from your findings.

If you’re asked about your experience with a project, be sure your example touches on each of those steps and what action was ultimately taken. Hiring managers want to know your data analysis is sound, and how leaders. In former roles chose to act – and what results came of their actions – is a story every hiring manager wants to hear.

Planning Your Career in Data Analytics

“Career goals in data analytics often include advancing technical skills, mastering analytics techniques, and moving into leadership roles,” adds Bilay. It’s important to set realistic, incremental goals for your career trajectory. Taking on a job you’re not ready for can be detrimental to your growth.

As you progress in your career, focus on soft skills. Communication is critical for a Data Analyst; you might have incredible analysis, but if you can’t communicate it effectively, your work won’t shine.

A typical data analyst career can evolve rapidly. New technologies appear often, and there are always new use cases you can learn from. Continuous learning is important for your career growth, and is a sly way to gain an advantage in an interview.

Stay connected with other data analysts on LinkedIn and other networking forums. Subscribe to any newsletters that help keep you up to date on trends, too. Staying at the forefront of knowledge while maintaining your skillset will position you well for career growth and advancement for years to come.