A Beginners Guide To The Data Analysis Process

About this video

• Video Title: A Beginner's Guide To The Data Analysis Process
• Channel: CareerFoundry
• Speakers: Will
• Duration: 00:10:20

Overview

This video provides a step-by-step guide to the data analysis process, outlining five key stages: defining the question, collecting the data, cleaning the data, analyzing the data, and sharing the results. It aims to introduce each stage and mention relevant tools for each.

Key takeaways

• Define the Question: This initial step involves clearly defining the objective or problem statement, which may require understanding the business goals and framing the problem precisely. Tools like Databox, DashaRoo, Grafana, Freeboard, and Dashbuilder can aid in visualizing business metrics and KPIs.
• Collect the Data: Once the objective is clear, a strategy for data collection is needed. Data can be quantitative or qualitative and falls into three categories: first-party (directly collected by the company), second-party (first-party data of other organizations), and third-party (aggregated from numerous sources). Data management platforms (DMPs) like Salesforce DMP, SAAS, Xplenty, Pymcore, and Dswarm can help aggregate data.
• Clean the Data: This crucial step involves preparing the data for analysis by removing errors, duplicates, outliers, and irrelevant observations, as well as structuring and filling in missing gaps. Data analysts often spend a significant portion of their time on this. Tools like OpenRefine, Python libraries (Pandas), R packages, and enterprise tools like Data Ladder can be used.
• Analyze the Data: After cleaning, the data is analyzed using various techniques depending on the goal. Broadly, analysis falls into four categories: descriptive (what happened), diagnostic (why it happened), predictive (identifying future trends), and prescriptive (making recommendations).
• Share Your Results: The final step is to interpret and present the insights clearly and unambiguously to stakeholders. This often involves using reports, dashboards, and visualizations. Honest communication, including highlighting data gaps or areas open to interpretation, is essential. Tools like Google Charts, Tableau, Datawrapper, Infogram, and Python libraries (Plotly, Seaborn, Matplotlib) can be used for visualization.