Data Science and Analytics

In the field of Data Science and Analytics at Paris Metropolitan University, students will gain a comprehensive understanding of the entire data lifecycle, from data collection and preprocessing to analysis and interpretation. They will learn advanced statistical techniques, machine learning algorithms, and data visualization methods to extract meaningful insights from complex datasets.

By the end of the program, students can expect to possess the skills and knowledge required to tackle real-world data challenges, make data-driven decisions, and contribute to solving complex problems across industries such as finance, marketing, healthcare, and technology, driving innovation and informed decision-making.

This curriculum aims to provide a comprehensive understanding of data science and analytics concepts, techniques, and tools. Students will gain hands-on experience through projects, working with real-world datasets to extract insights, build predictive models, and communicate findings effectively. By the end of the program, students will be equipped with the skills to work with large datasets, apply statistical and machine learning algorithms, and make data-driven decisions in various domains. Graduates will be prepared for roles such as data scientist, data analyst, or data engineer, and can contribute to organizations by leveraging data to drive informed strategies and insights.

Course information

  1. Educational Background: Possess a high school diploma or its equivalent. Prior coursework or experience in mathematics or computer science may be advantageous but is not mandatory.
  2. Language Proficiency: Demonstrate proficiency in the English language, as the course may be delivered in English. Applicants may need to provide evidence of their English language skills, such as a valid English proficiency test score.
  3. Application Form: Complete the university’s application form, providing personal and educational information.
  4. Statement of Purpose: Submit a statement of purpose explaining their motivations, career goals, and expectations from the program. This statement helps the admissions committee assess the applicant’s alignment with the program’s objectives.
  5. Identification Documentation: Submit a clear copy of their ID or passport to verify their identity.
  6.  
  1. Coursework: Attend and actively participate in all modules and classes, demonstrating a commitment to learning and a solid understanding of the subject matter.
  2. Assignments and Projects: Complete assignments, projects, and assessments assigned during the course, showcasing the application of knowledge and practical skills.
  3. Examinations: Successfully pass any required examinations or assessments that are part of the course evaluation.
  4. Capstone Project: Successfully complete the final project-based learning module, which involves building a software application or system. This project serves as a culmination of the skills and knowledge acquired during the program.
  5. Attendance: Maintain satisfactory attendance and participation in classes and activities throughout the course duration.
  6.  

Module 1:

  • Introduction to Data Science and Analytics
  • Exploratory Data Analysis: Data Cleaning, Data Visualization, and Descriptive Statistics
  • Introduction to Statistical Concepts and Probability

Module 2:

  • Data Manipulation and Transformation with Python or R
  • Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, and Random Forests
  • Model Evaluation and Validation Techniques

Module 3:

  • Unsupervised Learning: Clustering Techniques (K-Means, Hierarchical, DBSCAN)
  • Dimensionality Reduction: Principal Component Analysis (PCA) and Feature Selection
  • Introduction to Time Series Analysis and Forecasting

Module 4:

  • Advanced Machine Learning: Support Vector Machines (SVM), Ensemble Methods (Bagging, Boosting), and Neural Networks
  • Feature Engineering and Feature Extraction
  • Model Optimization and Hyperparameter Tuning

Module 5:

  • Text Mining and Natural Language Processing (NLP)
  • Advanced Data Visualization and Interactive Dashboards
  • Big Data Analytics: Introduction to Apache Hadoop and Spark

Module 6:

  • Advanced Topics in Data Science: Recommendation Systems, Anomaly Detection, and Deep Learning for Image Analysis
  • Ethics and Responsible Data Science Practices
  • Capstone Project: Applying Data Science and Analytics Techniques to Solve a Real-world Problem

Course Details:

  • Assessment Method: Combination of Coursework and Capstone Project
  • Program Duration: 3 months
  • Study Mode: Online
Apply Here

“Have inquiries? Connect with us.”