FAQ - Data Analyst
What is Data Analysis
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It's about turning raw data into actionable insights.
A data analyst typically focuses on descriptive and diagnostic analysis, answering questions like "what happened?" and "why did it happen?"
What are the main types of Data Analysis?
There are four main types of Data Analysis:
Descriptive: Summarizes what happened in the past (e.g., "what were our sales last quarter?").
Diagnostic: Explores why something happened (e.g., "why did sales drop in a specific region?").
Predictive: Forecasts what might happen in the future (e.g., "how many customers will we gain next month?").
Prescriptive: Recommends a course of action to achieve a desired outcome (e.g., "what marketing campaign should we launch to increase customer sign-ups?").
What is your Data Analysis Process?
The process can be broken down into six main steps:
Ask - Define the question: Clearly state the problem being we are trying to solve or the questions you're trying to answer.
Prepare1 - Collect data: Gather relevant data from various sources (databases, surveys, APIs, etc.).
Prepare2 - Clean the data: Prepare the data by handling missing values, duplicates, and errors. This is often the most time-consuming step.
Process - Process the data. Extract, Load and Transform (ETL) the data.
Analyze - the data: Apply statistical techniques and use tools to find patterns and insights. Examine for insights and patterns.
Act - Interpret and communicate findings: Translate the findings into a clear, compelling story using visualizations (charts, dashboards) and present them to stakeholders.
What is Data Cleaning?
Data cleaning, also known as data profiling or data cleansing, is a critical process for ensuring data quality. This process involves using various methods to identify, correct, remove, and log any inaccurate, incomplete, or irrelevant data.
The importance of data cleaning cannot be overstated; the principle of "garbage in, garbage out" applies to data analysis—if your data is messy, your analysis will be flawed, leading to inaccurate conclusions and poor decisions.
Key components of data cleaning include:
- Deduplication
- Standardization
- Handling missing data
- Correcting errors
Additionally, it is essential to produce a data quality and data profiling report to summarize the findings.
What is your tool set?
Tools in my tool bag are:
SQL (Structure Query Language)
Excel
BI (Business Intelligence) Tools: Power BI, Tableau and MS Access.
Python
