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Data Science

  • 5.0 Rate

  • 32 Lecture

  • 96 hours

  • 16 Weeks

Data Science can be defined as a combination of mathematics, business, algorithms, and machine learning techniques that help us find patterns in data and make discoveries that will significantly simplify and make decision-making more effective for organizations.

The course will cover all important areas of Data Science. Using Python, the most commonly used programming language for data analysis, we will cover the full cycle of data science - initial data processing, preparation for modeling, building machine and deep learning algorithms, training, application, and turning them into products.

Throughout the program, students will work independently on an individual project.

Outcome

Skills Acquired: AI, Machine Learning, LLM, Deep Learning, SQL, Python, Panda, Numpy.

  • Understand, correctly use, and communicate to business concepts such as: Artificial Intelligence (AI), Machine Learning, Deep Learning, Data Science, Large Language Models (LLM)

  • Determine when complex Machine Learning algorithms are needed and when simple data analysis is sufficient

  • Process Big Data using Python

  • Use the main Python libraries necessary for big data analysis: Numpy, Pandas, Matplotlib, Spark

  • Build, train, use, and adapt machine learning algorithms to real business problems

  • Turn built models into products

Mar 19 2700₾

Tue 20:00-23:00, Sat 12:00-15:00

Feb 17 2700₾

Mon - Thur 20:00-23:00

Split your payment
TBC installment
BOG installment

Who is this course for?

Beginners

The course is designed for those who want to learn data science from scratch and be able to apply it in their current or new activities. It's also for those who have experience in data analytics but lack experience in programming and using machine learning algorithms.

Analysts

For analysts who use analytical tools (Excel, SQL, Power BI, etc.), create analytical reports, and want to learn programming. For those who have formal and/or informal education in analytics/mathematics.

Programmers

For programmers who have coding experience and want to learn artificial intelligence.

Program includes

Alumni Club

After successfully completing the final exam, graduates will be automatically enrolled in the Alumni Club. This membership grants them access to exclusive events, content, and special offers from our partner companies

Work Based Learning

The course includes practice-based learning, including assignments/exercises and individual projects.

Bilingual Certification

Upon successful completion of the course, students will receive a bilingual certificate.

Graduate feedback

5.0 Rate

Syllabus

Importance and overview of Data Science
Data Science cycle
Data Science applications and examples
Overview of working environments: Jupyter Notebook, marimo notebook, Poetry, Pyenv, Anaconda, Google Colab
Python syntax and core concepts
Logical operators, loops, functions
Data structures (Lists, Sets, Tuple, Dictionary)
List comprehension, Generators, Lambda functions
Classes and Objects
NumPy arrays and operations
Broadcasting, vectorization, and matrix operations
Important functions and their applications
Series, DataFrame, and basic operations
Data selection, transformation, and aggregation
DataFrame merging

Pick your suitable time

Lecturers

Tsitsino Tepnadze

Data Science

Tsitsino Tepnadze

Data Science

Tsitsino has over 3 years of experience in Data Science. Currently, she works in Germany at Point 8 company, where she holds the position of Senior Data Scientist. She is involved in various projects that include big data processing and visualization, as well as creating models for forecasting, object recognition, and data classification. Tsitsino holds a Ph.D. from the University of Tromsø in Norway (Department of Computer Science and Computational Engineering) and has many years of research experience. Additionally, she worked at the Technical University of Dortmund, where she lectured in Calculus and Artificial Intelligence.

FAQs for this course

A: Data Science is at the forefront of technological innovation, combining statistics, programming, and domain expertise to extract meaningful insights from data. It empowers organizations to make data-driven decisions, predict future trends, and solve complex business challenges. As businesses increasingly rely on data, Data Scientists are in high demand, offering excellent career opportunities with competitive compensation. This field allows you to work on diverse projects across industries while contributing to technological advancement and business growth.
A: Prior programming knowledge is not required to start the course. The main prerequisites are analytical thinking abilities, basic knowledge of mathematics, and English language proficiency at B2 level. Experience with Excel or other analytical tools is desirable. Strong motivation and interest in working with data are also essential for course admission. While previous experience in programming or data analysis would be an advantage, it is not a mandatory requirement.

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