group of students working on project together

Data Analysis and Visualization (Stand-Alone Certificate)

Data analysis and visualization program is created for beginners who have never programmed before and want to build a complete understanding of Python from the ground up as well as begin the artificial intelligent, machine learning or data science track.

Course Info

Type :
Duration :
30 Hours
Subject :
Database Management
Location :
AUC Tahrir Square
AUC New Cairo
Format :
Online or Face to Face
Status :

Registration Instructions, Policies and Procedures

Registration instructions and tips, coupled with a clear understanding of policies and procedures, lay the foundation for a fulfilling and successful academic experience. 

Data analysis is the process of extracting information from data. It involves multiple stages, including establishing a data set, preparing the data for processing, applying models, identifying key findings, and creating reports. Data analysis aims to find actionable insights that can inform decision-making. This course teaches you essential statistical knowledge and how to apply it to different business questions. After completing this course, you can analyze your data quickly and visualize your results using SPSS and Python. You don’t need any prior knowledge to enroll in this course. You will learn how to use Python to analyze and visualize your data using different libraries. You will explore the four crucial steps for any data analysis project: Reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools data analysts use daily. By the end of the course, you will be able to extract knowledge and answers from data confidently.

CDAV101Data Analysis and Visualization3

*Continuing education unit equals 10 contact hours.

A stand-alone certificate will be issued upon the successful completion of each course.

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

  1. Utilize statistical tools for working with data sets
  2. Practice SPSS data entry, editing and organizing datasets, and carrying out inferential statistical analysis
  3. Use Python to solve tasks
  4. Practice using NumPy for data analysis
  5. Manage pandas in a data analysis context
  6. Manage data import and cleaning for different types of data
  7. Practice using different data visualization libraries and extracting insights from them
  8. Analyze machine learning algorithms for working with datasets
 Fees (EGP)
Admission Fees150
Course Tuition Fees (per subject or level)3,500 

Placement Test Fees (if required)

Standardized English Proficiency Test (SEPT) 

Online English Placement Test (OEPT) 

Refund Policy