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Professional Diploma in Data Analysis

 

Department of Information Technology — Novera International University

 

1. Program Overview:

An intensive professional and applied program extending over 3 months (80 hours of approved study load), aimed at preparing and qualifying professional data analysts capable of transforming raw data into effective insights and strategic decisions. The program covers applied statistics tools, programming in Python and its specialized libraries, database management and querying using SQL, data cleaning and exploration, and the design of interactive dashboards and advanced analytical reports.

2. Key Objectives:

  • Mastering statistical and programming analysis: understanding fundamental statistical concepts and using Python to process and analyze data.
  • Working with data structures and libraries: mastering NumPy for numerical data processing and Pandas for managing, structuring, and cleaning data and handling missing values.
  • Querying and visualizing data: writing advanced SQL queries for databases and designing interactive charts using Matplotlib and Seaborn.
  • Building dashboards and preparing reports: measuring and tracking Key Performance Indicators (KPIs), building interactive dashboards, and preparing analytical reports to support decision-making.

3. Target Audience:

  • Beginners and individuals wishing to pursue a professional career path in Data Analysis (Data Analysts) and Business Intelligence (BI).
  • Statistics and reporting personnel in companies and government and private institutions.
  • Students and graduates of Information Technology, Computer Science, Business Administration, Economics, and Statistics programs.
  • Professionals working in marketing, sales, and finance who wish to use data to improve performance and decision-making.

4. Study Plan Modules (12 Interactive and Applied Modules):

1. Introduction to Data Analysis and Its Fundamental Concepts.

2. Fundamentals of Statistics for Data.

3. Python for Data Analysis.

4. NumPy and Numerical Data Processing.

5. Pandas and Data Structuring and Organization.

6. Data Cleaning and Handling Missing Values.

7. Exploratory Data Analysis and Statistical Analysis (EDA).

8. Data Visualization Using Matplotlib and Seaborn.

9. Databases and SQL for Data Analysis.

10. Key Performance Indicators and Analytical Reporting (KPIs & Reporting).

11. Interactive Dashboards.

12. Data Analysis Project and Final Report.

5. Program Data and Organizational Assessment:

  • Field and specialization: Information Technology / Data Analysis.
  • Duration and sessions: 3 months (evening period) | from 20 to 25 lectures (two lectures per week, two hours per lecture).
  • Distribution of credit hours (80 total hours):

50 hours: interactive live lectures (remote / online).

20 hours: required readings and open resources.

7 hours: chapter questions and self-assessment.

3 hours: final review and completion examination.

  • Attendance and assessment requirements: an attendance rate of no less than 75% of the interactive lectures, in addition to assignments and practical applications.
  • Final project: an online assessment test (25 multiple-choice questions + 5 applied practical case questions), in addition to submitting an integrated applied data analysis project and a final analytical report.

 

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