Обзор статистики рынка труда в Кувасае

Biz sizdan nima kutyapmiz: - iqtisodiyot yoʻnalishi boʻyicha bakalavr maʼlumoti; - bank faoliyatida 3 yil yoki kreditlash yo'nalishida 2 yil ish tajribasi; - rus va ingliz tillarini o'rta darajada bilish; - bank mahsulotlarini sotish va JUSBP tahlilini qila olish; - Microsoft office dasturlarida ishlay olish. Qulayliklar: - qimmatli tajriba orttirish imkoniyati; - qarorlar tez qabul qilinadigan va gʻoyalar amalga oshiriladigan professional jamoa; - bank sohasida karyera qurish va rivojlanish imkoniyati; - Ilgʻor jamoa va qulay ish sharoitlari. Ish joyi: Farg'ona viloyati, Quvasoy shahri, Alisher Navoiy koʻchasi 1-uy Agar siz bank sohasida tajribaga ega boʻlishga, yangi kasbiy koʻnikmalarni rivojlantirishga va egallashga tayyor boʻlsangiz, salohiyatingizni roʻyobga chiqarish va istiqbolli sohada ishlashni oʻrganish uchun ajoyib imkoniyatdir. Ish faoliyatingiz davomida siz zamonaviy texnologiyalar va bank sektorining tamoyillari bilan tanishishingiz mumkin boʻladi. Biz xodimlarimizni qadrlaymiz va ularning karyeralarini qurishi uchun eng yaxshi ish sharoitlari va istiqbollarini taʼminlashga tayyormiz. Hamkorbank jamoasiga qoʻshiling va biz bilan tez rivojlaning!

Striving to gain market-oriented knowledge and skills to jumpstart your career in IT? Apply for this program and shape your professional path with EPAM experts. Details If you are eager to thrive in the data-driven world and master one of the most trending professions, then this expert-led program is what you need. For 3 months of intensive learning, you will explore self-study materials, complete tasks and engage with mentors at Q&A sessions. By participating, you will have the opportunity to: Gain a strong foundation in software engineering Dive deep into statistics and data analysis Learn regression, classification and clustering techniques Explore deep learning and neural network basics If you show good results and successfully pass a technical interview, you can join the next stage and immerse yourself in one of the mentoring programs: Computer Vision, Natural Language Processing, ML Engineering, Time Series or Recommendation Systems. What do we offer? Education within an IT company. As a leading software engineering company, we will help you explore emerging technologies and best practices, ensuring you are equipped with the knowledge that the current market seeks. Top-notch learning materials. Our curriculum is designed by Data Scientists with extensive project experience and tested in hundreds of training runs. Practice-oriented approach. This comprehensive program focuses on providing you with hands-on experience and practical application of the concepts learned. Deep dive into the specialization. Our graduates become highly skilled specialists ready to face complex technical challenges and work with the world's leading customers. Support from experienced mentors. We will guide you at all training stages, covering your open questions and sharing feedback on assigned tasks. Training process The program consists of two stages: The first one lasts 3 months and requires ~12 hours of weekly engagement. You will explore self-study materials, complete assigned tasks and discuss your questions with our mentors at regular weekly Q&A sessions. If you show good results in the first stage and successfully pass a technical interview, we will invite you to the next stage. It usually lasts 3.5 months and requires ~20 hours of weekly engagement. You will join one of the proposed mentoring programs: Computer Vision, Natural Language Processing, ML Engineering, Time Series or Recommendation Systems. Upon program completion, you will be able to continue your career path according to demonstrated skills and available opportunities at EPAM. What is required for training: English level B2 (Upper-Intermediate) or higher Proficiency in mathematical analysis (main topics of derivatives, integrals, extrema of functions in multidimensional real space) Skills in linear algebra (vectors, matrices, tensors, linear equations, eigenvalues and eigenvectors, quadratic forms) Knowledge of probability theory (definition of probability, conditional probability, Bayes theorem, expectation) Command of statistics (basic concepts, hypothesis testing, concept of likelihood, estimation of distribution parameter) Understanding of basic optimization concepts and methods (stationary points, Lagrange multipliers, gradient descent) Knowledge of data structures and algorithms (sorting algorithms, algorithm complexity) Familiarity with Python fundamentals (NumPy and Pandas in particular)