Data Science, Analytics, and Big Data – Level 4

Module 1: Introduction to Data Science

• Objective: Understand the role of data science in modern industries and explore basic concepts in data processing and analytics.
• Key Topics:
  • Introduction to Data Science and its Applications
  • Data Science Lifecycle: Collection, Cleaning, Analysis, and Visualization
  • Types of Data: Structured, Semi-structured, and Unstructured
  • Tools and Libraries: Python, R, Jupyter, and Pandas
  • Data Science Workflow

Module 2: Data Analytics Foundations

• Objective: Learn the principles of data analytics and explore statistical methods used in data analysis.
• Key Topics:
  • Descriptive, Predictive, and Prescriptive Analytics
  • Basic Statistical Concepts: Mean, Median, Mode, Standard Deviation
  • Exploratory Data Analysis (EDA) Techniques
  • Data Preprocessing and Feature Engineering
  • Handling Missing Data and Outliers

Module 3: Programming for Data Science

• Objective: Develop programming skills necessary for data manipulation and analysis.
• Key Topics:
  • Python Programming Basics
  • Data Structures and Algorithms for Data Science
  • Data Wrangling with Pandas and Numpy
  • Visualizations with Matplotlib and Seaborn
  • Automating Data Tasks with Python Scripts

Module 4: Data Visualization and Communication

• Objective: Learn the principles of effective data visualization and communication of data insights.
• Key Topics:
  • Data Visualization Principles and Best Practices
  • Using Python Libraries for Data Visualization (Matplotlib, Seaborn)
  • Interactive Visualizations with Plotly
  • Dashboards for Data Analysis: Tools like Tableau and Power BI
  • Storytelling with Data: Presenting Insights to Stakeholders

Module 5: Introduction to Big Data

• Objective: Gain an understanding of big data concepts and technologies.
• Key Topics:
  • Characteristics of Big Data: Volume, Variety, Velocity, Veracity, and Value (5 Vs)
  • Big Data Tools and Ecosystem
  • Hadoop Architecture and Ecosystem (HDFS, MapReduce)
  • Introduction to Spark: Resilient Distributed Datasets (RDDs)
  • Big Data Storage and Processing

Module 6: Big Data Analytics

• Objective: Learn advanced techniques for analyzing big data using distributed computing platforms.
• Key Topics:
  • Data Analytics on Hadoop using Hive and Pig
  • Real-time Analytics with Apache Kafka and Spark Streaming
  • Machine Learning with Spark MLlib
  • Introduction to NoSQL Databases: MongoDB, Cassandra, and HBase
  • Data Warehousing and ETL Processes

Module 7: Machine Learning for Data Science

• Objective: Understand key machine learning algorithms and techniques used in data science.
• Key Topics:
  • Supervised vs. Unsupervised Learning
  • Regression Models: Linear and Logistic Regression
  • Classification Models: Decision Trees, Random Forest, and SVM
  • Clustering Techniques: K-means, Hierarchical Clustering
  • Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
  • Introduction to Neural Networks

Module 8: Advanced Big Data Techniques

• Objective: Dive deeper into big data technologies and techniques for data analysis and storage.
• Key Topics:
  • Advanced Hadoop Techniques: Data Partitioning, Performance Tuning
  • Spark for Advanced Data Analytics: Machine Learning Pipelines
  • Data Lakes and Cloud Storage Solutions (AWS, Azure, GCP)
  • Introduction to Deep Learning for Big Data
  • Natural Language Processing (NLP) with Big Data

Module 9: Ethical Considerations and Data Privacy

• Objective: Understand the ethical implications of working with data and how to ensure data privacy and security.
• Key Topics:
  • Data Ethics: Bias, Fairness, Transparency
  • Privacy Issues and GDPR
  • Ethical AI and Responsible Data Usage
  • Data Governance Frameworks
  • Security Risks in Big Data

Module 10: Capstone Project in Data Science and Big Data

• Objective: Apply the concepts and tools learned in the course to a real-world problem, creating a comprehensive data analysis solution.
• Key Tasks:
  • Define and Research a Data Science or Big Data Problem
  • Data Collection, Cleaning, and Analysis
  • Model Building and Evaluation
  • Create Visualizations and Communicate Results
  • Final Project Presentation and Report

Assessment Methods:

• Assignments: Weekly assignments based on theoretical and practical knowledge.
• Exams: Periodic exams testing theoretical knowledge.
• Capstone Project: A final project where students analyze a large dataset using the tools and techniques learned.
• Class Participation: Active participation in discussions and practical exercises.

Learning Outcomes:

By the end of this course, students should be able to:

• Understand and apply core concepts in data science and big data.
• Analyze and process large datasets using various tools and programming languages.
• Create effective visualizations and communicate insights from data.
• Implement machine learning models and analyze results.
• Work with big data technologies to process and analyze vast amounts of data efficiently.