Data Science and Analytics

1. Introduction

The Data Science and Analytics course equips participants with the skills and knowledge to collect, process, analyze, and interpret large datasets to drive informed business decisions. Participants will explore statistical analysis, data visualization, machine learning, and predictive analytics techniques. The program bridges theoretical concepts with practical applications across various industries.

2. Course Objectives

By the end of this course, participants will be able to:

• Understand the data science lifecycle and analytics processes.
• Collect, clean, and preprocess structured and unstructured data.
• Apply statistical and machine learning techniques to analyze data.
• Visualize and interpret data insights effectively.
• Develop predictive and prescriptive models for business decisions.
• Utilize tools such as Python, R, SQL, and data visualization software.

3. Targeted Group

This program is ideal for:

• Data analysts, data engineers, and data scientists
• Business analysts and decision-makers
• IT professionals and software developers
• Students and graduates aspiring to specialize in data analytics
• Managers and executives seeking data-driven decision-making skills

4. Course Duration

Total Duration: 8 days (40 hours)
Delivery Options:

• Instructor-led classroom sessions
• Live online sessions with interactive Q&A
• Hands-on practical exercises and case studies

5. Training Methodology

The course employs a blended, hands-on approach:

• Interactive lectures and concept discussions
• Case studies and real-world analytics scenarios
• Practical exercises using Python, R, SQL, and visualization tools
• Group projects to develop analytical models
• Assessments and quizzes to consolidate learning

6. Course Content

Module 1: Introduction to Data Science and Analytics

• Overview of data science, analytics, and data-driven decision making
• Types of data: structured, unstructured, semi-structured
• Data science lifecycle and tools

Module 2: Data Collection and Preprocessing

• Data acquisition from various sources
• Cleaning, transforming, and integrating datasets
• Handling missing data, outliers, and data normalization

Module 3: Statistical Analysis for Data Science

• Descriptive and inferential statistics
• Probability distributions and hypothesis testing
• Correlation, regression, and statistical modeling

Module 4: Data Visualization and Communication

• Principles of effective data visualization
• Tools: Tableau, Power BI, Matplotlib, Seaborn
• Storytelling with data and presenting insights

Module 5: Introduction to Machine Learning

• Supervised, unsupervised, and reinforcement learning
• Classification, regression, clustering, and recommendation systems
• Model evaluation metrics and performance measurement

Module 6: Predictive Analytics and Modeling

• Building predictive models for business problems
• Time-series forecasting and trend analysis
• Feature selection, engineering, and model optimization

Module 7: Big Data Analytics and Tools

• Introduction to big data platforms (Hadoop, Spark)
• Handling large-scale datasets
• Cloud-based analytics and real-time data processing

Module 8: Practical Projects and Case Studies

• End-to-end data analysis projects
• Applying analytics techniques to real business datasets
• Presenting actionable insights and recommendations
• Final assessment and review

7. Expected Outcomes

Upon completing this course, participants will be able to:

• Apply data science techniques to real-world problems.
• Develop predictive and analytical models to support decision-making.
• Interpret and communicate data insights effectively.
• Utilize industry-standard tools for data analytics and visualization.
• Handle large datasets and implement data-driven strategies.

8. Certificate of Completion

Participants who successfully complete all 8 modules and practical exercises will receive:

🎓 Certificate of Completion – Data Science and Analytics

Issued by: FOTADE Training, Research and Resource Development Centre

This certificate demonstrates the participant’s capability to leverage data science and analytics for informed business decisions.