Data Analyst Role

Cards · 50

Primary objective of a data analyst

To inspect, clean, transform, and model data to discover useful insights and support business decision-making.

Data collection

Gathering raw information from various internal and external sources such as databases, APIs, surveys, and logs.

Data cleaning (Data cleansing)

Identifying and fixing errors, duplicate records, missing values, and inconsistencies to make data reliable.

Exploratory Data Analysis (EDA)

Analyzing datasets visually and statistically to summarize main characteristics before formal modeling.

Descriptive analytics

Examining historical data to answer what happened over a given period.

Diagnostic analytics

Investigating data relationships and anomalies to understand why specific outcomes or trends occurred.

Predictive analytics

Using historical patterns and statistical models to forecast future events or trends.

Prescriptive analytics

Recommending specific courses of action based on data models and simulated scenarios.

Data visualization

Translating complex data findings into graphical representations like charts, maps, and heatmaps.

Dashboard design

Building dynamic, interactive interfaces that track key business metrics and KPIs in real time.

Reporting

Summarizing data insights into periodic or ad-hoc written presentations for stakeholders.

Stakeholder management

Collaborating with business leaders and non-technical teams to understand objectives and deliver insights.

Key Performance Indicator (KPI)

A quantifiable measure used to evaluate the success of an organization in meeting operational goals.

Business acumen

Understanding the core industry, operations, and revenue drivers to contextualize data questions.

Translating business questions into data tasks

Converting broad management goals into specific data queries, metrics, and analytical steps.

Data modeling

Structuring database tables and establishing relational links to support efficient querying and reporting.

Data quality assurance

Verifying that data remains accurate, complete, reliable, and up to date across pipelines.

Data governance compliance

Adhering to legal standards and internal policies regarding data security, privacy, and user access.

Data dictionary maintenance

Documenting data definitions, formats, constraints, and origins to ensure organizational clarity.

SQL in data analysis

Querying, filtering, aggregating, and joining relational databases to extract target datasets.

Spreadsheet software (Excel / Google Sheets)

Executing rapid calculations, lightweight modeling, pivot table analysis, and basic prototyping.

Business Intelligence (BI) platforms

Software tools like Power BI and Tableau used for end-to-end data reporting and visualization.

Python for data analysis

A programming language used for advanced data wrangling, automation, and statistical modeling.

R programming language

A language and environment optimized for specialized statistical computing and graphical data output.

ETL process (Extract, Transform, Load)

Pulling data from source systems, converting it into an analysis-ready format, and loading it into storage.

Data warehousing

Accessing and querying central repositories of integrated business data like Snowflake or BigQuery.

A/B testing analysis

Evaluating randomized controlled experiments to measure the impact of business changes or features.

Statistical hypothesis testing

Determining whether observed variations in data are statistically significant or caused by chance.

Cohort analysis

Tracking the behavioral patterns of specific groups of users sharing common characteristics over time.

Churn analysis

Measuring and explaining the rate at which customers discontinue using a product or service.

Customer segmentation

Grouping customers by shared behaviors, demographics, or value to enable targeted business strategies.

Funnel analysis

Mapping user progression through defined steps to identify points of high drop-off and friction.

Root cause analysis

Pinpointing the fundamental underlying reason for an unexpected change or performance dip.

Anomaly detection

Identifying data points, events, or observations that deviate markedly from expected norms.

Trend analysis

Reviewing data over sequential periods to spot long-term upward or downward movements.

Data storytelling

Combining data, visuals, and narrative to communicate insights persuasively to non-technical audiences.

Executive summary creation

Distilling extensive analytical findings into key takeaways and recommendations for top leadership.

Ad-hoc analysis

Performing spontaneous, customized data queries to answer sudden, specific business inquiries.

Data imputation

Replacing missing data points with calculated estimates such as means, medians, or predicted values.

Outlier treatment

Detecting and deciding whether to retain, adjust, or remove extreme statistical values.

Data validation

Testing data inputs against predefined rules to ensure correctness before analysis.

Metric definition

Standardizing how a business measurement is calculated so teams interpret numbers consistently.

Data analyst vs. Data engineer

Engineers build and maintain data pipelines; analysts extract and interpret data to inform decisions.

Data analyst vs. Data scientist

Analysts focus on answering existing business questions; scientists build complex ML algorithms and models.

Version control in data workflows

Tracking changes in SQL scripts, notebooks, and models using tools like Git to ensure reproducibility.

Reproducibility in analysis

Writing modular, well-documented queries and code so colleagues can replicate results independently.

Data ethics

Ensuring data is analyzed and presented honestly without bias, manipulation, or privacy infringements.

Process automation

Scheduling repetitive queries and reporting tasks to run automatically without manual intervention.

Continuous monitoring

Setting alerts and tracking KPIs continuously to catch performance anomalies as they occur.

Feedback loop integration

Evaluating the business outcomes of implemented recommendations to refine future analytical models.