Different Steps and Tools in a Data Analytics Project

Overview

Explore the fundamentals of data analytics, from collection and cleaning to visualization and predictive modeling, learning to transform raw data into actionable business insights.

Syllabus

• Introduction to Data Analytics
Overview of Data Analytics
Importance and Applications in Business
• Data Collection
Types of Data (Structured vs. Unstructured)
Data Sources and Acquisition Methods
Tools for Data Collection
• Data Cleaning and Preparation
Identifying and Handling Missing Data
Data Transformation Techniques
Tools for Data Cleaning
• Exploratory Data Analysis (EDA)
Descriptive Statistics and Data Summary
Data Visualization Techniques
Identifying Patterns and Insights
• Data Visualization
Principles of Effective Data Visualization
Tools for Creating Visualizations (e.g., Tableau, Power BI)
Case Studies and Real-World Applications
• Statistical Analysis
Hypothesis Testing
Correlation and Regression Analysis
ANOVA and Other Statistical Methods
• Predictive Modeling
Introduction to Machine Learning Algorithms
Model Selection and Evaluation
Tools for Predictive Modeling (e.g., Python, R)
• Communicating Insights
Crafting an Effective Data Story
Presenting Data-Driven Insights to Stakeholders
Tools for Reporting and Dashboards
• Project Management in Data Analytics
Lifecycle of a Data Analytics Project
Team Collaboration and Agile Practices
Tools for Project Management
• Ethical Considerations and Data Privacy
Understanding Data Privacy Laws and Regulations
Ethical Implications of Data Analysis
• Capstone Project
Apply Learned Concepts to a Real-World Data Analytics Problem
Final Presentation of Insights and Recommendations

---

Subjects

Data Science