Data analysts are in high demand across industries as companies increasingly rely on data to drive business decisions. But what exactly does a data analyst do? At a high level, data analysts collect, process and perform statistical analyses on data to uncover insights and trends. However, the specific responsibilities can vary depending on the industry and company.
On LinkedIn, data analyst job postings provide helpful insights into the day-to-day work. Common responsibilities include collecting data from various sources, processing and cleansing data to ensure accuracy and integrity, analyzing data to spot trends and patterns, building dashboards and visualizations to communicate insights, developing data modeling solutions, and collaborating with key stakeholders to identify business needs and opportunities. Technical skills like SQL, Python, R, Excel and data visualization tools are commonly required.
In this article, we’ll take a deeper look at the typical responsibilities of a data analyst based on LinkedIn job postings. We’ll also explore the skills, qualifications and certifications employers are looking for.
Typical Data Analyst Job Responsibilities
Data Collection
One of the first steps in any data analysis process is collecting the relevant data. Data analysts are responsible for identifying data sources, pulling data using queries and APIs, assembling datasets and storing data appropriately. Common data sources include company databases, CRM systems, web analytics tools, social media APIs, public government data and more.
Data Cleansing
Real-world data is often messy, containing errors, outliers and inconsistencies. Data analysts must detect and fix quality issues through data cleansing techniques. This may involve removing duplicate records, handling missing values, standardizing data formats, validating data integrity and more. Proper data cleansing ensures downstream analyses are accurate.
Exploratory Data Analysis
Before diving into modeling or statistical testing, data analysts conduct an initial investigation to understand the data better. By creating summaries and visualizations, they can spot anomalies, identify patterns and relationships, test hypotheses, and find interesting avenues for further analysis. Exploratory analysis helps analysts validate assumptions.
Statistical Analysis and Modeling
Data analysts apply statistical techniques and construct quantitative models to derive actionable insights. For example, they may build predictive models using machine learning algorithms to forecast future outcomes. Or they may run A/B tests to identify optimal parameters. Statistical programming languages like R and Python are commonly used.
Data Visualization
A key part of a data analyst’s job is visualizing and communicating data insights. They create dashboards, reports, charts and graphs to summarize findings tailored to different audiences, from executives to end users. Data visualization makes complex data easier to digest. Popular data visualization tools include Tableau, Power BI and Looker.
Collaboration and Requirements Gathering
Data analysts don’t work in silos. They frequently collaborate with engineering teams, product and business teams, designers, and company leaders. Effective collaboration ensures analysts are focused on solving the right business problems with data. Analysts may interview internal teams to identify requirements, define metrics, and determine what data is needed.
Project Management
Data analysts may manage projects end-to-end, which includes scoping data requirements, developing project plans and timelines, coordinating tasks across teams, monitoring progress, and delivering polished reporting and visualization solutions on schedule. Strong project management ensures analytics projects run smoothly.
Monitoring and Reporting
On an ongoing basis, data analysts develop standardized reports and dashboards to monitor KPIs, track progress over time, and quickly surface insights. Automated reporting saves time so analysts can focus on more advanced analysis. Best practices include clearly documenting data sources, assumptions, and methods.
Process Improvement
Data analysts don’t just extract insights from data – they recommend ways to optimize processes. By continuously monitoring performance data, they identify bottlenecks, issues and improvement opportunities. Analysts have a bird’s-eye view that can lead to innovative solutions.
Must-Have Skills for Data Analysts
SQL and Programming
SQL skills are absolutely essential for extracting, manipulating and analyzing data stored in relational databases. Python and R are programming languages commonly used for statistical analysis and modeling. Coding skills allow automating repetitive tasks.
Analytics Tools Experience
Companies use specialized analytics platforms like Looker, Tableau, Power BI, and Qlik to visualize data and build dashboards. Data analysts must have hands-on experience with relevant platforms used at their company.
Statistical Analysis
Data analysts should have a solid grasp of statistical concepts they can apply to draw insights, such as Bayesian analysis, A/B testing, regression, hypothesis testing, sampling, and more. Statistical programming languages like Python, R and SAS are used.
Data Wrangling
Real-world data requires manipulation and cleansing to fill gaps, identify outliers, resolve inconsistencies, transform variables, and more. Data wrangling skills are key for shaping messy data into an analysis-ready format using Python, R, or tools like Trifacta.
Math and Algorithms
Data analysts apply math concepts like algebra, calculus, and algorithms like decision trees, clustering, and artificial neural networks. Math aptitude and computational thinking enable building advanced analytic models.
Problem Solving
At their core, data analysts are problem solvers that use data to improve business outcomes. They must think critically to ask the right questions then apply analytical techniques to derive data-driven solutions.
Data Visualization
Data analysts transform abstract data into intuitive visuals and interactive dashboards. They must adeptly use data visualization platforms like Tableau, Power BI, and D3.js to communicate insights. Design skills are a plus.
Business Acumen
Understanding the company’s business goals, competitive landscape, and industry trends helps analysts identify high-impact projects. Business acumen guarantees analytics efforts tackle relevant business problems.
Communication Skills
Data analysts must convey technical findings and recommendations clearly to both technical and non-technical audiences through reports, presentations, and visualization. Strong communication skills ensure insights are actionable.
Educational Background
Data analyst positions typically require a bachelor’s degree, although some may accept candidates with an associate degree plus work experience. Relevant majors include computer science, information systems, mathematics, statistics, analytics, or business administration with an analytics focus. Master’s degrees in data or business analytics are advantageous for senior roles.
While a degree provides foundational analytical skills, many companies also list preferred certifications for data analysts on LinkedIn job posts. Certifications demonstrate hands-on proficiency. Here are some of the most commonly required certifications:
– Microsoft Certified: Power BI Data Analyst Associate
– Microsoft Certified: Azure Data Fundamentals
– AWS Certified Big Data Specialty
– Google Analytics Individual Qualification
– Tableau Desktop Certified Associate
– Oracle SQL Expert
– SAS Base Programming for SAS 9 Credential
– IBM Certified Data Analyst Professional
– Qlik Sense Data Architect Certification
Data Camp, Coursera, and Udacity offer online courses to gain certifications and skills in areas like Python, R, SQL, statistics, machine learning, and popular analytics tools. Analysts should proactively upgrade skills as new data tools and techniques emerge.
Advancement Prospects
Data analysts enjoy strong career growth opportunities. Here are some common positions data analysts may be promoted to with several years of experience:
– Senior Data Analyst: Leads complex analytics projects and may manage more junior analysts. Expectations include expert technical knowledge and strategic business thinking.
– Analytics Manager: Manages a team of data analysts and oversees all analytics projects within a business unit. Key duties include mentoring analysts, monitoring workflows, and ensuring insights drive decisions.
– Data Scientist: Applies advanced machine learning algorithms and statistical modeling to extract deeper insights from data. Roles require a higher level of statistical and programming expertise compared to analysts.
– Data Engineer: Focuses on building and maintaining data pipelines, infrastructure, and analytics systems. Technical skills like ETL processes, database administration, and cloud platforms are emphasized over analysis.
– Product/Business Intelligence Manager: Leads analytics strategy for an entire product or business area. Responsibilities include identifying high-value opportunities, guiding technical roadmaps, and translating insights into products and processes.
With the rapid growth in data jobs, experienced analysts can take on leadership, specialist, or product development roles. Soft skills like communication, presentation, and strategic thinking also grow in importance as analysts advance in their careers.
Conclusion
Data analysts play a critical role in extracting valuable insights from data to guide business strategy and operations. Although specific day-to-day responsibilities vary, most analysts share core skills in statistical programming, manipulating data, visualization, and analytical modeling. Curiosity, critical thinking, collaboration, and business acumen characterize top performing analysts. With the exponential growth of data, skilled analysts will continue to be in high demand across industries. Those looking to break into the field should focus on developing technical proficiencies through online learning, bootcamps and hands-on projects.