დაწყების თარიღი: 10.10.2025
სამშაბათი და ოთხშაბათი 19:00-21:00
Data analysis is a contemporary applied science focused on the efficient collection, processing and interpretation of data in order to make the best decisions based on the information obtained. This field requires a combination of both technical and analytical skills: the combination of programming, statistics, computer science, modeling, and AI systems makes it one of the most sought-after, widely used, and indispensable directions among information technology professions.
Data Analysis and Artificial Intelligence with Python is a hands-on course designed to introduce students to the Python programming language, its associated core libraries, and software. This is because Python is a general-purpose programming language that is the first choice for data science, AI, and scripting. After learning the basics, students will move on to algorithmic problem solving, data analysis, visualization, intelligent systems, and machine learning models based on Python, as well as deploying their projects in real-world environments, on servers, and across systems.
After completion of the course, the participants will work on final project.
Within the specialization the participants will obtain the profession of data analyst, also Python developer პროფესიას and კურსის დამთავრებისთანავე დამოუკიდებლად შეძლებენ:
– Developing solutions to medium and complex tasks in Python, writing programs
- Write algorithms on Python
- Linux Servers, scripting and automation on systems
– Work as a Python developer
- Gather, process, analyze and visualize the data
- Start working with AI systems
- Work with databases with Python
– Independently build smart systems and programs
– Program simulation of rationality and intuition
– Creating and training machine learning models
Upon successful completion of the course, a certificate from the Scientific Cyber Security Association will be issued. Students will also have the opportunity to take an exam on the platform and receive an international certificate.
Duration of the Specialization
Price of the Specialization
1 completed project upon graduation
The training course is a way to learn the theoretical and practical application of modern standards in the field of programming. Modern, dynamic and multiparadigm language "Python" will be studied in it. which includes object-oriented, procedural, functional and imperative programming.
Course Duration: 10 Meetings/20 Hours;
Meeting 1:Why Python? Introduction to Python; How Python works; First script; Simple operations; Variables in Python; Integers; Floats; Strings; Input
Meeting 2:
If-else, elif; branching programs; random/pseudo-random numbers.
Meeting 3:
Iteration; for loop; while loop; methods for terminating and continuing a loop.
Meeting 4:
Strings; Working with strings;
Meeting 5:
Implementation and definition of functions; arguments in a function; local and global variables; function scopes.
Meeting 6:
Working with files; lists; operations on lists.
Meeting 7:
Tuples; Dictionaries; Operations on tuples and dictionaries;
Meeting 8:
ლექსიკონები, ოპერაციები ლექსი
Meeting 9:
try – except ბლოკი; შეცდომების მართვა და მისი გამოწვევა; Exeption კლასი; როგორ შევქმნათ საკუთარი Exeption კლასის მემკვიდრე; ოპერატორების გადატვირთვა.
Meeting 10:
Classes: Introduction; Examples of Classes; An Environmental Perspective on Classes; Adding Methods to Classes; Set of Integers (Class Example).
Examination
Within the course, the student will learn to solve real analytical problems using the Python programming language. The course covers: algorithms using Python, testing and debugging using Python, working with classes in Python, algorithmic complexity and data structures.
Course Duration: 10 Meetings/20 Hours;
Meeting 1:
Simple Algorithms, Bisection search; Conversion algorithms, Dealing with floats
Meeting 2
Fractions, fraction conversion Newton Raphson
Meeting 3:
Scope in functions, scope details, key and default arguments, Built-in functions Stings recall-all methods, Recursion, Factorial
Meeting 4:
Scope in recursion, Iteration vs recursion, Mathematical induction, Tower of Hanoi, Palindromes
Meeting 5:
Fibonacci, GCD, Modules, Usage of Tuples, algorithms
Meeting 6:
Lists methods recalls, functions as objects, List of functions, Map, Algorithms with lists
Meeting 7:
Dictionaries operations recall, Leveraging dictionaries properties, Algorithms with dictionaries
Effective recursion using dictionaries
Meeting 8:
Testing and debugging, Classes of tests, testing approaches, Black box testing, Glass box testing, Bugs, Categories of bugs, Debugging in Practice, Tests
Meeting 9:
Exceptions recall, assertions, Programs with exceptions, Exception control as flow Classes recall
Built in methods, Hierarchies, Inheritance, Algorithms with classes
Meeting 10:
Class override, Class overload, Class generators, Algorithmic complexity, Complexity classes, recursion complexity, Searching and sorting algorithms
Examination
The training course is a way to learn the theoretical and practical application of modern standards in the field of programming. Modern, dynamic and multiparadigm language "Python" will be studied in it. which includes object-oriented, procedural, functional and imperative programming.
Course Duration: 10 Meetings/20 Hours;
Meeting 1:
Basics – Introduction, System Installation, File System
Navigation commands
Meeting 2
Basic 2 – Introduction, Rights Management, User Management
Meeting 3:
Package managers, universal package managers, software maintenance in the system, containers
Meeting 4:
Bash Scripting
Meeting 5:
Services, automation of programs
Meeting 6:
anaconda პაკეტ მენეჯერი სისტემაში, python ბიბლიოთეკების და აპლიკაციების მართვა სისტემაში
Meeting 7:
python scripting in the system, working with files os, sys libraries in python,
Meeting 8:
datetime objects in Python, time-oriented scripting
Meeting 9:
Processes, process wrapping in Python, subprocess library, bash integration in Python scripts
Meeting 10
Parallelization of processes in the system, Python threads
Examination
Machine learning is the most effective direction of AI, which can detect patterns in data and predict future events with some accuracy, but is very sensitive to the input data. In this course, students will learn data processing, merging and normalization, preparing information for training a model, various machine learning algorithms, and evaluating their accuracy and efficiency.
Course Duration: 10 Meetings/20 Hours;
Meeting 1:
Numpy library, array creation, indexing and slicing, reshape, broadcasting and vectorization.
Meeting 2
Pandas library, Series and DataFrame structures, reading/writing data (CSV, Excel), indexing, basic data cleaning techniques, general analysis
Meeting 3:
Data combining, groupby operations, time series processing, performance optimization
Meeting 4:
Machine learning – supervised learning: Linear regression, classification
Meeting 5:
Machine learning – supervised learning: Decision trees, Random forests
Meeting 6:
Machine learning – unsupervised learning: clustering, K-means
Meeting 7:
Machine learning – unsupervised learning: hierarchical clustering, dimensionality reduction (PCA)
Meeting 8:
Machine learning – ensemble models, using multiple models in parallel
Meeting 9:
Machine learning – transfer learning Remodeling, retraining, compression of a trained model
Meeting 10:
Statistical assessment of model accuracy, visualization of results, matplotlib, seaborn libraries
Exam/Mini Project
AI (Artificial Intelligence) is one of the fastest growing fields in computer science. It is used in various areas such as machine learning, data analysis, automation, optimization, modeling and simulation, decision making, etc. The course starts with fundamental topics such as data structures and algorithms. The course includes a discussion of the main techniques and methods of AI.
Course Duration: 10 Meetings/20 Hours;
Meeting 1:
Python lists, dictionaries, classes, representing information as graphs
Meeting 2
Functions in Python, recursion
Meeting 3:
Search problem, uninformed search DFS, BFS algorithms
Meeting 4:
Heuristic functions, informed search, gtreedy-first search A* search
Meeting 5:
Game Theory, minimax algorithm
Meeting 6:
Optimization problem, local search; algorithms hill climbing, stimulated annealing
Meeting 7:
Genetic algorithms, linear programming
Meeting 8:
Constraint satisfaction problem, CSP algorithms
Meeting 9:
Logical agents, model checking algorithm
Meeting 10:
Statistical agents, probability theory as an algorithm
Examination/ Mini Project
Within the framework of the final project, students will use the knowledge gained during the specialization subjects. The final project will include the identification of a practical case, it will be necessary to process raw data, prepare information, select and train a model, statistically evaluate and visualize the results, and prepare a final report. Students will complete the project individually. In case of successful completion of the final project, the student will receive a specialization certificate.
Professor at Caucasus University; Head of Cyber Security; President at Scientific Cyber Security Association;
Python Developer; Lecturer at Scientific Cyber Security Association.
Python Developer; Lecturer at Scientific Cyber Security Association
Price of the specialization can be paid in two installments.
დაწყების თარიღი: 10.10.2025
ორშაბათი - პარასკევი: 19:00 – 21:00
Teaching mode: Offline/Online
Price: GEL 1250
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