Start of course: June

Get a Free Data Scientist Сourse and become a part of NIX team

Master a profession in 4 months, study well, and get a job at NIX soon after training

A data scientist is responsible for extracting insights from data using statistical and machine learning techniques, interpreting complex datasets to uncover patterns and trends, and working closely with stakeholders to address business objectives

This role involves exploring and analyzing datasets, developing predictive models, conducting statistical analysis, interpreting findings to inform business decisions, and collaborating with cross-functional teams to identify data-driven solutions

Format Hybrid
Language English, B2
Course duration 4 months
Lesson duration 8 hours
Training Schedule Regular meetings at NIX Office

Skills gained throughout the course

Hard Skills

Solid math and programming skills
Basic knowledge of SQL, REST, GIT, and docker
Practical experience with LLM customization approaches (prompt engineering and RAG)
Knowledge of Computer Vision and Natural Language Processing techniques and algorithms
Knowledge of Classic ML algorithms together with deep learning techniques. Practical experience with Tensorflow/Keras and Pytorch
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Educational program

1
DATABASE BASIC
#Lecture #Practice
2
SQL BASIC
#Lecture #Practice
3
SQL ADVANCED. AGGREGATE FUNCTIONS
#Lecture #Practice
4
PYTHON – CORE. BASICS
#Lecture #Practice
5
PYTHON – CORE. SETUP, CODESTYLE, ENV
#Lecture #Practice
6
PYTHON – CORE. OOP
#Lecture #Practice
7
PYTHON – CORE. ADVANCED. FUNCTIONS
#Lecture #Practice
8
PYTHON – CORE. ADVANCED. ITERATORS, GENERATORS, LIST COMPREHENSION
#Lecture #Practice
9
PYTHON – CORE. ADVANCED. LOGGING, CONTEXT MANAGER, EXCEPTIONS
#Lecture #Practice
10
PYTHON CORE. TESTS
#Lecture #Practice
11
PYTHON CORE. ALGORITHMS & DATA STRUCTURES
#Lecture #Practice
12
CLIENT-SERVER
#Lecture #Practice
13
HTTP/HTTPS
#Lecture #Practice
14
REST
#Lecture #Practice
15
Math | Random Variables
#Lecture #Test
16
Math | Metrics
#Lecture #Test
17
Math | Probability
#Lecture #Test
18
Math | Distributions
#Lecture #Test
19
Classical ML | Anomaly Detection | Statistical Techniques
#Lecture #Practice
20
Classical ML | Anomaly Detection | Machine Learning Techniques
#Lecture #Practice
21
Classical ML | Classification | K-Nearest Neighbors
#Lecture #Practice
22
Classical ML | Classification | Logistic Regression
#Lecture #Practice
23
Classical ML | Classification | Naive Bayes Classifier
#Lecture #Practice
24
Classical ML | Classification | Support Vector Machine
#Lecture #Practice
25
Classical ML | Clusterization | DBSCAN
#Lecture #Practice
26
Classical ML | Clusterization | K-means
#Lecture #Practice
27
Classical ML | Decision Trees | Decision Tree + Classical ML | Decision Trees | Random Forest
#Lecture #Practice
28
Classical ML | Regression | Decision Tree Regression
#Lecture #Practice
29
Classical ML | Regression | Linear Regression
#Lecture #Practice
30
Classical ML | Time Series
#Lecture #Practice
31
Introduction To Neural Networks | Neuron + Introduction To Neural Networks | Neural Network
#Lecture #Practice
32
Introduction To Neural Networks | Activation Function
#Lecture #Practice
33
Introduction To Neural Networks | Loss Function + Introduction To Neural Networks | Training
#Lecture #Practice
34
Introduction To Neural Networks | Convolution
#Lecture #Practice
35
Gradient Boosting | Gradient Boosting Theory
#Lecture #Practice
36
Gradient Boosting | LGBM + Gradient Boosting | XGBoost
#Lecture #Practice
37
NLP | Regular Expressions
#Lecture #Practice
38
NLP | Tokenization + NLP | Word To Vector
#Lecture #Practice
39
NLP | Text Classification + NLP | Text Clusterization
#Lecture #Practice
40
NLP | Transformer
#Lecture #Practice
41
NLP | LLM customization
#Lecture #Practice
42
NLP | Tools and Frameworks
#Lecture #Practice
43
NLP | LLM evaluation
#Lecture #Practice
44
NLP | Popular modern LLMs
#Lecture #Practice
45
NLP | Practice
#Practice
46
Final NLP task
#Practice
47
Computer Vision | Image Classification | Intro + Computer Vision | Image Classification | LeNet
#Lecture #Practice
48
Computer Vision | Image Classification | AlexNet + Computer Vision | Image Classification | VGG
#Lecture #Practice
49
Computer Vision | Image Classification | GoogLeNet (Inception)
#Lecture #Practice
50
Computer Vision | Image Classification | ResNet
#Lecture #Practice
51
Final CV task
#Practice
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Our expert team guarantees tangible outcomes and advancement in your professional journey

Our mentors consist of practicing managers boasting extensive years of practical experience. They will pass on their experience and knowledge to you during the months of training

#Security #Highload systems #SRE #IoT #Micro frontends #Web 3.0 #Cloud engineering

What we expect from you

  • You have sufficient English (Upper-intermediate (B2) and higher)

  • You are familiar with computer terminology

  • Basic knowledge of Python

  • Basic knowledge of math (statistics and calculus)

  • Basic knowledge of classic machine learning and deep learning concepts

  • Willingness to learn and evolve

Your course recruitment path: steps to success

1 Sign up for a Data Science Course
2 Undertake technical skills test
3 Have an interview with our team
4 Start training and continue NIX career

NIX is a well-known provider of IT outsourcing software and services

Our team cooperates with many countries and significantly contributes to developing the international IT market. In addition, we actively promote our business and improve our skills. Our specialists have successfully developed the latest projects, from e-commerce to cloud technologies, for the world’s largest Fortune 500 companies

Internet Services and Software
Logistic
Medicine
E-commerce
Education
Finance and Banking

Working with NIX, you will get

Everything for comfortable work

Personal and professional development

Mentoring support

The team approach to project implementation

Developing your expertise

Informal corporate culture

Answering important questions

  • Why are your courses free of charge?

    It allows those looking to leap into an IT career to gain the necessary knowledge and get employment at NIX

  • Will I find a job with the skills I got from your course?

    Yes, with the help of our courses, you will learn how to present your qualifications throughout all stages of selection to NIX, properly prepare for an interview, and secure your ideal position in working with us. Additionally, you will have enough skills to apply to other IT companies

  • Will you have a vacancy for me after the course?

    After successful completion of our courses, you will have the opportunity to join our team immediately after training

  • Are your courses fully offline?

    While most of this course content is delivered online, we do have some offline lessons located in Budapest, Hungary. Our course’s online format offers unparalleled flexibility and convenience. Students can access the course materials anytime, allowing them to learn at their own pace and around their personal and professional commitments

    Additionally, in-person sessions provide a hands-on, interactive learning experience. Within the physical classroom setting of these sessions, our instructors can immediately address any doubts or questions. These offline lessons also present a fantastic networking opportunity, allowing you to connect with industry professionals and fellow learners who might become future colleagues or collaborators

  • What English level is required?

    The required English proficiency for our courses is Upper-Intermediate (B2 level) and above. This ensures effective participation and comprehension of course materials

  • If I don’t have the expected skills, can I start training?

    To complete the training, you need to master the skills specified in the program for each course

Start of training: June

Sign up for a course and get a job as a Data Scientist in NIX in 4 months

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