Skills Needed for a Data Analyst- Core Competencies

As a data analyst, having the right technical skills is critical for succeeding in this data-driven role.

In this post, you’ll discover the essential competencies needed, from statistical analysis and programming proficiency to effective data visualization and storytelling techniques.

You’ll see key skills like Python, SQL, data modeling, dashboard creation, and more that allow analysts to extract powerful insights. You’ll also learn soft skills for critical thinking, communication, and continuous learning to complement the technical toolkit.

Table of Contents


Briefly introduce the article and outline the key skills data analysts need to be successful.

Data analysts play a critical role in helping organizations make data-driven decisions. To be successful in this role, data analysts need to master both technical and soft skills.

On the technical side, key skills include:

  • Statistical analysis and modeling
  • SQL and experience with relational databases
  • Proficiency in R, Python, or other programming languages
  • Data visualization techniques
  • Machine learning and algorithms
  • Big data tools and technologies

In terms of soft skills, data analysts need:

  • Strong analytical and critical thinking abilities
  • Math and quantitative skills
  • Communication and presentation aptitude
  • Business acumen

By developing expertise across both technical capabilities and soft skills, data analysts can provide immense value to organizations through data insights, modeling, and strategic recommendations.

Overview of a Data Analyst’s Role

Explain what a data analyst does on a day-to-day basis and why certain technical and soft skills are critical.

Data analysts are responsible for collecting, cleaning, and organizing data to uncover insights. On a daily basis, analysts may:

  • Access databases and data systems to extract relevant information
  • Clean and transform data to prepare it for analysis
  • Perform statistical analysis and data modeling to identify trends and patterns
  • Visualize data using charts, graphs, and dashboards
  • Generate reports and presentations to communicate findings
  • Make strategic recommendations based on analytical conclusions

To succeed in these core responsibilities, data analysts rely heavily on technical skills like SQL, Python, advanced Excel, and more. These skills allow them to efficiently wrangle data at scale and perform robust analytics.

Soft skills are also vital. Analysts must collaborate cross-functionally, translate analytical findings into business recommendations, and clearly present data stories to stakeholders. Strong communication skills ensure data-driven insights lead to actual business impact.

Identifying Essential Analyst Technical Skills

Highlight the top technical skills employers look for when hiring data analysts.

Based on common job postings, the most in-demand technical skills for data analysts include:

  • SQL: Extracting and manipulating data from databases using SQL is ubiquitous. Knowledge of joins, aggregations, window functions and more is expected.
  • Python and R: These programming languages are essential for statistical analysis, machine learning, and other advanced techniques. Familiarity with libraries like Pandas, NumPy, SciPy, and Sklearn is a plus.
  • Data Visualization: Creating interactive visualizations with tools like Tableau, Power BI, or Python libraries enables better data understanding.
  • Big Data Platforms: Experience with distributed systems like Hadoop, Spark, Kafka or cloud platforms like AWS is desirable to handle large datasets.
  • Excel and Spreadsheets: Excel skills like PivotTables, VLOOKUPs, and What-If Analysis are still valued for ad-hoc analysis before scaling to big data systems.
  • Statistics and Machine Learning: Mathematical maturity in techniques like regression, clustering, decision trees, and neural networks allows more advanced analysis.

Developing expertise in these technical areas will equip aspiring data analysts with the skills to succeed in the role and provide significant value to employers.

Is coding required for data analytics?

Yes, coding skills are essential for most data analytics roles. While the specific languages and depth of coding expertise varies, having programming knowledge opens up more job opportunities and allows you to work more efficiently with data.

Here are three key reasons coding is a vital skill for data analysts:

Data Wrangling

Cleaning, transforming, and preparing data for analysis usually requires writing scripts. Knowledge of SQL or Python enables analysts to efficiently wrangle large, complex data sets before modeling and visualization. Mastering data wrangling techniques is a fundamental coding skill for analysts.


Coding allows analysts to automate repetitive tasks like data imports, formatting, calculations, and reporting. Python and R make it easy to schedule and run data workflows, freeing up time for higher-value analysis. Programming skills are invaluable for boosting productivity.

Advanced Analytics

Understanding machine learning, AI, and statistical modeling requires coding knowledge. While some analytical tools have simplified interfaces, mastering underlying algorithms and models involves writing code. Expanding your coding skills enables more advanced analytics.

In summary, while basic Excel skills may suffice for some junior roles, most data analysts are expected to know SQL or Python. Employers favor candidates with coding skills for unlocking deeper insights. Prioritizing programming knowledge early allows analysts to boost efficiency, automation, and innovation throughout their careers.

What qualifies you to be a data analyst?

While formal education is important, there are a variety of paths to becoming a data analyst. Here are some of the most common qualifications:


  • A bachelor’s degree in a quantitative field like statistics, computer science, or mathematics provides a solid foundation. Economics, business, and social sciences degrees can also be applicable.
  • Master’s degrees are less common but may be required for more advanced roles. Data science, analytics, information systems are good options.
  • Relevant coursework in statistics, data analysis methods, SQL, Python, R, data visualization, machine learning, etc.

Technical Skills & Experience

  • Proficiency in SQL, Python, R, spreadsheet tools like Excel
  • Statistical analysis and data modeling skills
  • Visualization skills to effectively communicate insights
  • Some professional experience in an analytical role, even internships, can demonstrate aptitude

Soft Skills

  • Communication skills to relay technical findings to non-technical stakeholders
  • Creativity and critical thinking to uncover insights
  • Collaborative abilities to work cross-functionally

While formal degrees are common, excellent analytical, technical, and communication skills can make up for education gaps in some cases. Continuing education and certifications can also strengthen a resume. The key is demonstrating core competencies in working with data to drive business decisions.

What skills do data analysts need for life?

Data analysts need a diverse set of technical and soft skills to succeed in their careers. Here is an overview of some of the key competencies needed:

Core Technical Skills

  • Statistical analysis and probability: Having strong math and statistics skills is crucial for data analysts to compute metrics, build models, and draw insights from data patterns. Knowledge of probability, statistical modeling, A/B testing, regression analysis, etc. helps analysts carry out quantitative analysis.
  • SQL and relational databases: Understanding SQL queries and database schema allows analysts to efficiently extract and manipulate data stored across complex relational databases.
  • Programming languages like R or Python: Proficiency in languages like R and Python enables analysts to process and analyze data, build custom scripts and algorithms, implement machine learning models, and automate repetitive tasks.
  • Data visualization: Creating compelling visualizations like charts, graphs, and dashboards to derive actionable business insights from data analysis. Common tools include Tableau, Power BI, etc.
  • Big data and analytics: Working with large, unstructured big data sets using frameworks like Hadoop and Spark to uncover hidden patterns and drive data-driven decisions.

Essential Soft Skills

  • Analytical thinking: Logically evaluating data, asking probing questions, and connecting insights across datasets calls for sharp analytical abilities.
  • Data interpretation: Translating raw statistical figures and technology outputs into meaningful narratives and recommendations for business stakeholders.
  • Communication and presentation: Conveying technical analysis and insights through clear verbal, written, and data visualization modes to people without a statistical background.
  • Creativity: Designing solutions to broadly-defined business issues by exploring data from creative angles.
  • Business acumen: Understanding how companies operate, including functions like sales, marketing, finance, and operations, helps better apply analysis.
  • Collaboration: Partnering with diverse cross-functional teams to ensure analysis objectives align with broader organizational goals.

Mastering the above well-rounded mix of technical data science and soft business skills is key for data analysts to deliver impactful insights.

How much SQL is needed for data analyst?

The amount of SQL knowledge needed for a data analyst role can vary depending on the specific responsibilities and tools used. However, here is a general overview:

Fundamentals Are Key

As a baseline, data analysts should have a solid grasp of SQL fundamentals. This includes being able to:

  • Write basic SELECT statements to query and filter data
  • Use common functions like COUNT, SUM, AVG
  • Join multiple tables together
  • Create basic aggregates and calculations

With these core competencies, an analyst can retrieve and analyze datasets stored in relational databases.

Deeper Knowledge Opens Up Possibilities

However, more complex analysis often requires deeper SQL knowledge. Understanding concepts like:

  • Nested queries
  • Window functions
  • Common table expressions
  • Stored procedures

gives analysts more flexibility to wrangle data. Especially when working with large, interconnected datasets.

So while basic SQL is essential, mastering more advanced features allows analysts to be more productive. Leaning on IT less for custom extracts. And enabling more impactful analysis.

Ongoing Learning

Given the progressive nature of data analysis, continuous SQL learning is key. As responsibilities grow, and tools evolve, new query methods open up. Whether that’s new SQL dialects, or integrations with Python/R.

Setting aside dedicated time to further develop SQL skills ensures analysts can get the most from the data available. Unlocking deeper insights as an organization’s data landscape matures.

Statistical Analysis and Data Interpretation

Data analysis relies heavily on statistical methods and probabilistic thinking to draw meaningful insights. Mastering some fundamental skills in this area provides a solid analytical foundation for aspiring data analysts.

Probability and Statistics Fundamentals

Grasping key concepts in probability and statistics allows analysts to quantify and analyze patterns in data sets. Important skills include:

  • Statistical modeling using distributions, confidence intervals, hypothesis testing, regression analysis, etc.
  • Applying statistical methods like A/B testing, time series analysis, cohort analysis, etc.
  • Understanding randomness, variability, correlation vs causation, statistical significance

Learning resources: introductory probability/stats course, Khan Academy

Data Visualization Techniques

Impactful data visualizations turn complex data sets into accessible, engaging graphics. Key skills include:

  • Using visualization tools like Tableau, Power BI, D3.js
  • Design principles for charts/graphs based on data types and insights
  • Creating interactive dashboards to illustrate key trends

Data Analysis Tools and Software

Data analysts use a range of tools and platforms for various data tasks:

  • SQL for accessing, manipulating databases
  • Python/R for statistical analysis, machine learning
  • Spreadsheets like Excel for data organization, modeling
  • Notebooks (Jupyter, Colab) for prototyping code
  • Big data platforms (Hadoop, Spark) for large data sets

Learning the fundamentals of these tools empowers analysts to handle diverse data challenges.

Data Analyst Skills Checklist

Well-rounded data analysts require both technical aptitude and soft skills:

Technical Skills

  • SQL
  • Python or R
  • Statistics
  • Data visualization
  • Machine learning
  • Microsoft Excel

Soft Skills

  • Communication & storytelling
  • Critical thinking
  • Business acumen
  • Creativity & problem solving

Cultivating expertise across all of these areas leads to highly capable, insightful analysts. The journey may seem daunting, but taking it one step at a time by building real projects pays dividends in the long run.


Mastery of Programming Languages

Proficiency in languages like Python and R enables automation and advanced analysis.

Python for Data Analysis

Python is one of the most popular programming languages for data analysis due to its extensive libraries specifically geared towards data manipulation, visualization, and machine learning. For example, key Python libraries like Pandas, NumPy, Matplotlib, and Scikit-Learn provide data analysts with the capabilities to process, analyze, and gain insights from data.

Learning Python allows data analysts to automate repetitive tasks, perform complex statistical modeling, create insightful data visualizations, apply machine learning algorithms on datasets, and more. With Python’s versatility, efficiency, and wide adoption, it is a must-have skill for any aspiring or current data analyst.

R or Python: Choosing Your Tool

While both R and Python offer robust capabilities for data analysis, each language has strengths for certain scenarios:

  • R tends to provide greater depth and specialization for statistical analysis and modeling. Its ecosystem is tailored specifically for data science applications.
  • Python offers more versatility and scalability across the full data analysis workflow. With Python’s general-purpose nature, it can connect and unify various processes including data access, cleaning, analysis, reporting and more.

For newcomers, Python’s gentle learning curve and versatility makes it a popular starting point. Meanwhile, R’s specialized domain knowledge in statistics lends well to roles focused heavily on analytics and modeling work.

Ultimately, both languages are staples in the data science toolbox. Learning one as a primary and having familiarity with the other allows data analysts to utilize the best tool for any given task.

Automation with Programming Languages

Programming languages like Python and R allow data analysts to automate repetitive, manual processes around data tasks. This saves significant time and enhances productivity.

Some examples include:

  • Automated data collection from files/databases into analysis-ready formats
  • Streamlined data cleaning and preprocessing with scripts
  • Analysis workflow encapsulation into reusable functions/modules
  • Automated report/dashboard generation from templates
  • Configurable machine learning pipelines for predictive models
  • Scheduling repetitive ETL (Extract, Transform, Load) jobs

Programming enables codifying manual effort into scripts that can rerun with minimal future overhead. Data analysts should identify their most frequent repetitive tasks and determine what aspects can be automated. This allows focusing efforts on higher-value analysis and interpretation.

Machine Learning Algorithms

Beyond statistical analysis, data analysts should also gain familiarity with machine learning techniques for pattern recognition and predictive modeling.

Some common algorithms to learn include:

  • Regression methods like linear regression, logistic regression, nonlinear regression
  • Classification algorithms such as SVM, random forests, neural networks
  • Clustering techniques like K-means, hierarchical clustering
  • Anomaly detection using isolation forests, local outlier factor

With libraries like Scikit-Learn in Python and Caret in R, analysts can readily apply machine learning models without extensive coding. Understanding the core concepts of different algorithms allows selecting appropriate models for a given data problem.

Applying machine learning engenders deeper business insights – analysts can move beyond reporting historical trends to predicting outcomes and driving impactful decisions. Augmenting analysis with machine intelligence will be a key facet of the data analyst role moving forward.

Database Management and Big Data Competencies

Experience collecting, cleaning, manipulating, and storing various data types is critical for data analysts. This section covers key competencies related to working with databases and big data.

SQL Proficiency and Relational Databases

Working with large, complex data from multiple sources using SQL and relational database management is a fundamental skill for data analysts. Key aspects include:

  • Understanding how to query databases using SQL statements to extract, filter, aggregate, and transform data
  • Designing and optimizing SQL queries for performance
  • Normalizing database schemas for efficient storage and integrity
  • Identifying relationships between datasets and joining data from multiple tables
  • Experience with relational databases like MySQL, PostgreSQL, Microsoft SQL Server, etc.

For example, a data analyst may use SQL JOIN statements to combine transaction data from multiple ecommerce database tables to analyze customer purchase patterns over time.

Big Data Sets and Scalability Challenges

With the rapid growth of data, understanding the complexities and tools required for handling big data is critical. Key knowledge areas include:

  • Working with large, unstructured datasets requiring specialized big data technologies like Hadoop, Spark, etc.
  • Building data pipelines to collect, process and analyze streaming or real-time data
  • Identifying scalability bottlenecks and optimizing performance for large data workloads
  • Leveraging cloud data platforms like AWS, GCP for storing and processing big data

For instance, a data analyst at a social media company may use big data platforms to analyze billions of daily user activity events to uncover trends.

Data Modeling Techniques

Data modeling plays a key role in structuring and organizing data to enable effective analysis. Key skills include:

  • Developing conceptual, logical and physical data models using modeling techniques like UML
  • Mapping the relationships between data elements, entities and attributes
  • Designing optimized model schemas for analytical and reporting needs
  • Validating data quality, completeness and integrity against business requirements

For example, a data analyst may create a star schema data model for optimizing business intelligence dashboards and reports.

Data Mining Algorithms and Applications

Data mining algorithms allow analysts to programmatically discover patterns and extract useful information from data. Understanding common algorithms and how to apply them is important, including:

  • Using classification, clustering, regression and decision trees for predictive analytics
  • Applying association rule learning to uncover relationships between variables
  • Building and comparing performance of data mining models
  • Identifying appropriate techniques for different analytical needs

For instance, a data analyst at a bank may leverage random forest algorithms to predict likelihood of loan defaults based on historical customer data.

Data Visualization and Reporting

Data visualization and reporting are critical skills for data analysts to communicate insights effectively. Here are some best practices to build impactful data stories.

Crafting Compelling Data Stories

Crafting compelling data narratives starts with understanding your audience and what insights would be most relevant to them. Some tips include:

  • Focus on the key takeaways and conclusions rather than just presenting charts. Guide viewers through the logical flow.
  • Use annotations and callouts on charts to highlight key points.
  • Keep visualizations simple yet impactful. Avoid cluttered charts that are hard to decipher.
  • Use color, layout, and visual hierarchies intentionally to lead the viewer’s eye.
  • Tell a story – take viewers on a journey from problem to insights to recommendations.

Utilizing PowerBI for Advanced Visualizations

PowerBI is one of the most popular tools for interactive data visualization. Some key features include:

  • Drag-and-drop interface to easily build reports
  • Library of custom visuals like heat maps, funnel charts
  • Drill down capabilities to explore data
  • Parameters for user interactivity
  • Integration with other data sources

Leverage these to create more advanced and insightful visualizations.

Data Visualization Best Practices

Keep these principles in mind:

  • Clarity – Avoid clutter and clearly highlight important info
  • Accuracy – Data and conclusions presented should be precise
  • Aesthetics – Use layout, color, fonts intentionally to guide attention
  • Relevance – Only highlight what is useful for the audience/purpose

Following best practices improves reports and builds credibility.

Interactive Dashboards and Real-Time Reporting

Real-time dashboards enable data-driven decision making through:

  • Automated, continuous data updates
  • Customizable visuals and layouts
  • Alerts and notifications to flag trends
  • Drill down to granular data
  • Parameter toggling for scenario analysis

These facilitate analysis and rapid response to business insights.

In summary, compelling data visualization and reporting unlocks the value of data. Mastering techniques to craft impactful narratives and insights is an essential data analyst skill.

Essential Data Analysis Tools and Software

Data analysts rely on a variety of tools and software to collect, organize, analyze, and visualize data. Choosing the right programs is crucial for performing effective analysis. Here we explore some of the most essential data analysis tools and how to use them.

Microsoft Excel for Data Analysts

Excel is likely the most ubiquitous data analysis tool. Analysts use Excel for:

  • Organizing data sets
  • Calculating metrics like averages, percentages, and more using formulas
  • Creating charts and graphs to visualize data trends and relationships
  • Building financial models to forecast business outcomes
  • Performing statistical analysis with add-on tools

With some coding knowledge, analysts can also automate repetitive tasks with Macros and VBA scripts.

While Excel provides analysts with ample basic analysis functionality, it does have limitations with large, complex data sets which is why learning complementary programs is advised.

Advanced Analytics with PowerBI and Tableau

PowerBI and Tableau take data visualization and interactive dashboards to the next level. These business intelligence platforms connect to various data sources, providing analysts with robust options to:

  • Visualize key data stories through custom, interactive charts and graphs
  • Create informative dashboards with advanced filtering and drilling down capabilities
  • Combine data from multiple sources into unified views
  • Automate updates to reports and dashboards
  • Collaborate with stakeholders through built-in sharing options

The main difference between PowerBI and Tableau comes down to pricing models and some functionality differences.

Data Analysis Toolkits

Beyond Excel, PowerBI and Tableau, analysts use other software including:

  • SQL and Relational Databases: Query, organize and connect datasets for analysis.
  • R and Python: Perform statistical modeling and machine learning.
  • Big Data Tools: Store and process high volume, high velocity data.
  • Notebooks (Jupyter, Colab): Document steps of data analysis work.

Choosing data analysis software depends on the specific needs and use cases analysts aim to address.

Integrating Tools for Comprehensive Analysis

Rather than relying on a single tool, analysts often combine programs into an analysis toolkit that meets the full scope of needs across collecting, organizing, analyzing, and visualizing data insights.

Key integration examples include:

  • Using Python and R to build machine learning models off data processed and organized in Excel.
  • Connecting Excel data to PowerBI dashboards for interactive reporting.
  • Building SQL databases then analyzing with Python or Tableau to uncover trends.

Determining the right toolkit involves assessing business requirements, data challenges, stakeholder needs, and analyst skill sets. With the wealth of analysis software now available, analysts can mix and match programs for comprehensive, impactful data insights.

Developing Soft Skills for Data Analysts

Beyond technical ability, critical thinking and communication enable impact.

Communication and Storytelling

Clearly conveying technical findings to non-technical stakeholders is a crucial skill for data analysts. This involves translating complex data insights into compelling narratives that drive business decisions.

To develop strong communication skills:

  • Structure findings as logical stories, with context, analysis, and clear calls-to-action
  • Use data visualizations and examples to simplify complex concepts
  • Practice explaining analysis to non-technical colleagues and incorporating their feedback
  • Summarize key takeaways and recommendations concisely for executives

With strong communication and storytelling abilities, data analysts empower diverse stakeholders to leverage data insights.

Critical Thinking and Problem Solving

Critical thinking enables data analysts to effectively break down problems, question assumptions, and uncover smarter solutions. Key aspects include:

  • Defining the root causes of issues by gathering relevant qualitative and quantitative data
  • Challenging existing notions and developing hypotheses to expand perspectives
  • Designing structured experiments to test theories and reveal findings
  • Synthesizing information to derive meaningful and actionable conclusions

Sharpening critical thinking and structured problem-solving skills allows analysts to drive greater business value from data.

Collaboration and Teamwork

Data analysts must collaborate effectively across functions. Key skills include:

  • Partnering with colleagues in IT, engineering, product, and business teams to align on objectives
  • Exploring different viewpoints and building consensus to meet diverse stakeholder needs
  • Establishing clear processes for requesting, accessing, and managing data inputs
  • Communicating transparently on project timelines, blockers, and key milestones

Smooth cross-functional teamwork enables higher quality, integrated analysis that accelerates decision-making.

Adaptability and Continuous Learning

As technology evolves, analysts must continuously expand their skillsets. Best practices are:

  • Proactively learning new data tools, programming languages, and cloud platforms
  • Experimenting with machine learning and visualization techniques
  • Understanding developments in data collection, storage, processing, and analytics
  • Attending conferences and certifications to stay updated on industry trends

Embracing adaptability and lifelong learning helps analysts maximize their career potential and leadership impact over time.

Preparing for a Data Analyst Internship

Internships provide invaluable opportunities to gain real-world experience in data analysis. As an intern, you’ll apply classroom concepts to solve actual business problems. Here’s what to expect and how to prepare.

Skills Required for Data Analyst Internship

Data analyst interns are expected to have basic proficiency in:

  • Statistical analysis: Calculating metrics like mean, median, mode, standard deviation using Excel or Python.
  • SQL: Querying databases using SELECT, WHERE, JOINs, aggregations, and more.
  • Data visualization: Creating charts/graphs in Excel, Tableau, Power BI to spot trends.
  • Programming: Python or R for data manipulation, analysis, and modeling. Focus on Pandas, NumPy, SciPy, and scikit-learn libraries.
  • Data modeling: Mapping relationships between data variables via ER diagrams, dimensional modeling, etc.
  • Domain knowledge: Understanding of the business, industry, common metrics, and KPIs.

Real-World Applications

As an intern you’ll get hands-on with:

  • Data pipelines: Extracting, transforming, loading data from various sources.
  • Dashboards: Building interactive reports and visualizations to deliver insights.
  • Analytics projects: Working through the data analysis lifecycle – ask, prepare, process, analyze, share, act.

This teaches you how skills directly apply in workplace settings.

Internship Goals and Objectives

Set 1-3 specific, measurable goals for your internship:

  • Analyze campaign performance by creating funnel reports in Google Analytics.
  • Build Tableau dashboard tracking weekly sales KPIs.
  • Create predictive model forecasting customer churn using Python and present findings.

Quantify achievements by percentage lift in key metrics, model accuracy, stakeholder feedback, etc.

Gaining Practical Experience

Maximize learning by:

  • Asking questions – don’t hesitate to ask!
  • Shadowing team members to understand their responsibilities.
  • Finding a mentor who can share industry/domain knowledge.
  • Volunteering for special projects to expand your skillset.
  • Building your portfolio with analysis samples and deliverables.

Following this advice will lead to a rewarding internship poised to kickstart your data analytics career!


Summary of Core Data Analyst Competencies

To summarize, some of the most critical skills needed to become a proficient data analyst include:

  • Statistical analysis and probability – Being able to derive insights from data requires an understanding of statistics and probability concepts. This allows analysts to correctly interpret results.
  • Programming – Languages like SQL, Python, and R are integral for data manipulation, analysis, and visualization. Analysts should be adept at least one.
  • Data visualization – Creating compelling visualizations is key for effectively communicating insights from data analysis. Skills in tools like Tableau help.
  • Machine learning – With the rise of AI and automation, knowledge of machine learning algorithms and models is becoming increasingly important for data analysts.
  • Communication and storytelling – Soft skills in translating data findings into impactful narratives and actionable business recommendations are invaluable for data analysts.

Continuing Education and Skill Development

To build on core data analysis competencies, analysts should:

  • Take online courses in programming, statistics, machine learning, etc. Coursera, Udemy, and LinkedIn Learning offer quality options.
  • Participate in local data science meetups and conferences to connect with the community.
  • Stay updated on latest data technologies by following influential thought leaders on blogs and social media.
  • Work on personal data analysis projects, document processes in a portfolio, and collaborate with others to get new perspectives.
  • Consider obtaining industry certifications in data analysis tools and methodologies to signal competencies to employers.

Related posts