Introduction to Data Analytics

Overview

Data Analytics is a major topic in business.
By the end of this video, you'll understand what data analytics is and what a data analyst does.

Companies collect vast amounts of raw data.
Raw data is useless until analyzed for valuable insights.
Data Analytics: The process of analyzing raw data to pull out useful insights for business decisions.
Data analysts transform complex data into actionable insights, enabling informed business decisions.
Business Intelligence (BI): Using data analytics to find patterns and solve business problems.
Insights can reveal customer behaviors, employee engagement, and more.

Data is ubiquitous and has infinite uses.
General Uses:
- Faster and better business decisions
- Cost reduction
- Development of innovative products and services
Specific Uses:
- Predicting future sales or purchasing behaviors
- Enhancing security and fraud protection
- Analyzing marketing campaign effectiveness
- Boosting customer acquisition and retention
- Increasing supply chain efficiency

Typical Responsibilities:
- Manage and report on user satisfaction surveys.
- Develop requirements and success metrics with business line owners.
- Execute and evaluate analytical projects.
- Monitor processes to identify improvement opportunities.
- Translate questions into analytical tasks.
- Gather, collate, and organize data from various sources.
- Design, build, test, and maintain back-end code.
- Implement data quality criteria and processes.
- Work with teams to evaluate data for shaping business strategies.

Step 1: Define Questions
- Establish why you're conducting analysis and what questions need answers.
- Identify required data sources.
Step 2: Collect Data
- Gather data from primary (internal) or secondary (external) sources.
Step 3: Clean Data
- Prepare data for analysis by removing duplicates, anomalies, and inaccuracies.
Step 4: Analyze Data
- Analyze data using techniques like regression analysis, cluster analysis, time series analysis.
Step 5: Interpret and Share Results
- Present findings in understandable formats (charts/graphs).
- Discuss with stakeholders to inform business decisions and reflect on data limitations.

Mathematical and Statistical Ability
- Comfort with crunching numbers.
Programming Knowledge
- Familiarity with languages like Python, Oracle, SQL.
Analytical Mindset
- Ability to dig deeper and understand underlying issues.
- Strong problem-solving skills.
Tool Proficiency
- Know which tools to use for different types of analysis.
Communication Skills
- Effectively share insights with stakeholders and the company.