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Data Science & Analytics
The Data Science & Analytics Professional Course is designed to equip learners with practical skills to collect, process, analyze, and interpret data for decision-making. The course focuses on transforming raw data into meaningful insights using statistical methods, programming tools, and visualization techniques.
Learners will gain hands-on experience in data cleaning, data analysis, visualization, predictive modeling, and reporting using industry-standard tools. The program prepares learners for careers in data analysis, business intelligence, research, and AI-related fields.
Module 1: Introduction to Data Science
- Meaning of data science
- Importance of data in decision-making
- Data science lifecycle
- Types of data (structured & unstructured)
- Real-world applications
Module 2: Basics of Data Analytics
- Data analysis process
- Descriptive vs predictive analytics
- Types of analytics (descriptive, diagnostic, predictive, prescriptive)
- Business intelligence overview
- Data-driven decision making
Module 3: Statistics for Data Science
- Mean, median, mode
- Standard deviation and variance
- Probability basics
- Data distribution
- Correlation and regression
Module 4: Introduction to Python for Data Science
- Python basics
- Variables and data types
- Loops and functions
- Libraries (NumPy, Pandas)
- Data manipulation
Module 5: Data Collection and Cleaning
- Data sources
- Data scraping basics
- Missing data handling
- Data formatting
- Data preprocessing
Module 6: Data Visualization
- Importance of visualization
- Charts and graphs
- Matplotlib and Seaborn
- Dashboard creation
- Storytelling with data
Module 7: Exploratory Data Analysis (EDA)
- Understanding datasets
- Pattern identification
- Outlier detection
- Data summarization
- Insight extraction
Module 8: Databases and SQL
- Introduction to databases
- SQL basics
- Data queries (SELECT, INSERT, UPDATE)
- Joins and relationships
- Data retrieval techniques
Module 9: Excel for Data Analysis
- Advanced Excel functions
- Pivot tables
- Data cleaning in Excel
- Charts and dashboards
- Automation basics
Module 10: Machine Learning Introduction
- What is machine learning
- Types of machine learning
- Supervised vs unsupervised learning
- Model training basics
- Real-world ML applications
Module 11: Predictive Analytics
- Forecasting techniques
- Regression models
- Classification models
- Model evaluation
- Accuracy and performance
Module 12: Data Visualization Tools
- Power BI introduction
- Tableau basics
- Interactive dashboards
- Business reporting
- Data storytelling
Module 13: Big Data Concepts
- What is big data
- Data volume, velocity, variety
- Hadoop and Spark overview
- Cloud data systems
- Real-time data processing
Module 14: Data Ethics and Privacy
- Data protection laws
- Ethical data usage
- Privacy principles
- Responsible AI usage
- Cybersecurity in data
Module 15: Business Intelligence
- BI concepts
- Reporting systems
- KPI tracking
- Business dashboards
- Decision support systems
Module 16: Practical Data Projects
- Data cleaning projects
- Sales analysis projects
- Survey data analysis
- Dashboard creation
- Predictive modeling tasks
Module 17: Industry Applications
- Healthcare analytics
- Finance and banking data
- Marketing analytics
- Government data systems
- Education data analysis
Module 18: Capstone Data Science Project
- Full data analysis project
- Data collection and cleaning
- Visualization and modeling
- Insights presentation
- Final report and portfolio
Educational Requirements
- Basic secondary education or equivalent
- Basic mathematics knowledge
- Basic English communication skills
Technical Requirements
- Basic computer literacy
- Interest in data and analysis
- Willingness to learn programming
Equipment Requirements
- Laptop or desktop computer
- Internet connection
- Minimum 8GB RAM recommended
Personal Attributes
- Analytical thinking
- Problem-solving skills
- Attention to detail
- Logical reasoning
- Curiosity about data
Suitable For
Students, researchers, business analysts, ICT professionals, entrepreneurs, government staff, and anyone interested in data-driven decision-making.
Learning Materials
- Data science handbooks
- Statistics guides
- Python programming notes
- Machine learning references
- Business analytics templates
Software Tools
- Python (Jupyter Notebook)
- Excel / Google Sheets
- Power BI
- Tableau (intro)
- MySQL / PostgreSQL
Practical Resources
- Data cleaning exercises
- Visualization projects
- Dashboard development tasks
- Predictive modeling practice
- Real-world datasets
Online Learning Resources
- Kaggle datasets and tutorials
- Data science courses
- YouTube analytics training
- Google data analytics resources
- Python programming tutorials
Support Services
- Personal tutor support
- Data mentorship
- Internship placement
- Project supervision
- Career guidance
Assessment
- Data analysis assignments
- Visualization projects
- SQL tasks
- Machine learning exercises
- Final capstone data science project