Beyond Dawlish
Data analysts use a systematic process to transform raw information into insightful outcomes. Here is a step-by-step explanation of data analysts' work:
1. Understand the Business Problem
Objective: Define the goal or question the business must have answered.
Activities:
Meet with stakeholders or managers.
Define key metrics and success criteria.
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2. Collect the Data
Objective: Collect relevant data from different sources.
Activities:
Extract data from databases using SQL.
Pull data from data warehouses, APIs, Excel sheets, or web scraping.
Utilize Python, R, or business intelligence (BI) tools.
3. Clean and Prepare the Data
Actions:
Deal with missing values, duplicates, and inconsistencies.
Normalize or transform data (e.g., convert date formats, scale values).
Join datasets if necessary.
4. Explore and Analyze the Data (EDA)
Goal: Find patterns, trends, and outliers.
Actions:
Visualize data with tools such as Tableau, Power BI, or Matplotlib/Seaborn in Python.
Create hypotheses or more in-depth questions from findings.
5. Apply Analytical Techniques
Goal: Move past description to create predictions or recommendations.
Activities:
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Utilize regression, clustering, or classification models.
Predict future trends or determine major drivers of behavior.
Conduct A/B testing or simulations.
6. Present Findings
Goal: Clearly communicate insights to decision-makers.
Activities:
Produce reports, dashboards, or presentations.
Use visualizations and narratives to present findings.
Give actionable advice.
7. Iterate and Work Together
Goal: Improve analysis as additional data or feedback is received.
Tasks:
Obtain stakeholder feedback.
Re-run or modify analysis.
Work with data engineers, scientists, or business groups.
Commonly Used Tools
Languages: SQL, Python, R
Visualization: Tableau, Power BI, Excel
Data Storage: MySQL, PostgreSQL, Snowflake, Google BigQuery
Analytics Platforms: Google Analytics, Looker, SAS
Would you like a real-world example of a data analyst’s project to better understand this workflow?
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