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Introduction to Data Analytics
Jul 3, 2024
Introduction to Data Analytics
Overview
Purpose
: Provide an understanding of what data analytics is and what a data analyst does.
Structure
: Video covers five key points on data analytics and roles in the field.
Interaction
: Encourages viewers to like, subscribe, and ask questions in the comments.
Step 1: What is Data Analytics?
Definition
: Process of analyzing raw data to extract useful insights for companies.
Importance
: Helps drive smart business decisions with interpreted data.
Role of Data Analysts
:
Interpret complex data to provide actionable insights.
Help companies make informed decisions.
Solve business problems and identify patterns in data.
Step 2: How is Data Analytics Used?
Broad Uses
:
Make faster and better business decisions.
Reduce business costs.
Develop new products and services.
Specific Uses
:
Predict future sales or purchasing behaviors.
Security and fraud protection.
Analyze marketing campaign effectiveness.
Boost customer acquisition and retention.
Increase supply chain efficiency.
Step 3: Daily Activities of a Data Analyst
Typical Roles and Responsibilities
:
Deliver user satisfaction surveys and report results.
Collaborate with business line owners to develop requirements and success metrics.
Manage and execute analytical projects.
Monitor systems for improvement opportunities.
Translate questions into analytical tasks and gather data.
Organize data from multiple sources.
Design, build, and maintain back-end code.
Define and implement data quality processes.
Evaluate and analyze key data to shape business strategies.
Look for job descriptions on platforms like LinkedIn, Indeed, and iCrunchData.
Step 4: Data Analysis Process
1. Define Questions
Determine why the analysis is conducted and what questions need answers.
Example: "What strategies can retain customers after a free trial?"
2. Collect Data
Gather data from primary sources (internal, CRM) and secondary sources (government portals, Google Trends).
3. Clean Data
Remove duplicates, anomalies, and missing data to ensure accuracy.
4. Analyze Data
Use techniques such as regression analysis, cluster analysis, and time series analysis.
5. Interpret and Share Results
Present findings in understandable formats like charts and graphs.
Collaborate with stakeholders on the next steps.
Reflect on data limitations and further analysis.
Step 5: Skills Needed for Data Analysts
Hard Skills
:
Mathematical and statistical ability.
Knowledge of programming languages (Python, SQL, Oracle).
Soft Skills
:
Analytical mindset and problem-solving skills.
Excellent communication skills for sharing insights with stakeholders.
Conclusion
Further Learning
: CareerFoundry offers a detailed article and a five-day short course on data analytics.
Encouragement
: Viewers are invited to ask questions in the comments for further clarification.
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Full transcript