Course

Data Science - February 2025

Хиймэл оюун ухааны цогц сургалт нь танд Deep Learning инженер, Machine Learning инженер, Applied Machine Learning инженер, Дата судлаач гэх мэт мэргэжлээр амжилттай ажиллахад шаардлагатай мэдлэг ур чадварыг олгоно. AI модулиар та бүх зүйлийн суурь болдог математик зарчим, их хэмжээний өгөгдлөөс хэрэгцээтэй мэдээлэл гаргаж авах арга барил, мөн машиныг оновчтой шийдвэр гаргахад сургах зэрэг сэдвийн хүрээнд суралцах болно.

Topics

Introduction for Python
Introduction for Python

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Resources
- Чиглүүлэх хичээл - бичлэг 1
- Чиглүүлэх хичээл - бичлэг 2
Data Acquisition
Data Acquisition

• Getting to know pandas;
• Acquiring data from different sources;
• CSV files and Excel files;
• Web Services. JSON and XML;
• Databases;
• Web crawling and scraping;
• Merging different sources. Constraints and validity.

06-Feb 18:00PM

Outside - 301
Online - Zoom live Stream
Resources
Data Tidying and Cleaning
Data Tidying and Cleaning

• Tidy data: normalization;
• Subsetting and sorting and reshaping;
• Data summarization and aggregation;
• Combining datasets;
• Data transformations;
• Handling outliers and missing values;
• Data cleaning process.
Resources
Data Visualization. Exploratory Data Analysis
Data Visualization. Exploratory Data Analysis

• Analytical graphs: principles, creation and examples;
• Plots: histograms, scatterplots, line plots and pie charts. Usages and examples;
• Enhancing plots: colors, labels and formatting;
• Telling the correct story;
• Exploratory data analysis: motivation, principles and applications.
Resources
Working with Images
Working with Images

• Processing images: transformations and information extraction;
• Image histograms;
• Fourier transform. Image spectrum;
• Image morphology;
• Introduction to convolutional neural networks;
• Image generation.
Resources
Working with Text
Working with Text

• Processing text: information extraction;
• "Bag of words" model and n-grams;
• TF-IDF;
• Introduction to language models. Practical applications;
• Text generation.
Resources
Regression Models
Regression Models

• Regression: definition and problem statement;
• Linear regression. Ordinary Least Squares;
• Multiple linear regression;
• Logistic regression: problem statement;
• Logistic regression application.
Resources
Data Science Project Architecture
Data Science Project Architecture

• Data science process: problem, data, algorithms, models and presentation;
• Organizing code, research and other artifacts;
• Performance and security;
• Reproducible research; evidence-based research;
• Ethical considerations and governance.
Resources
Data Science in Production
Data Science in Production

• Data pipelines and automation;
• Scaling workflows to handle large volumes of data;
• Cloud services;
• Data monitoring: dashboards;
• Introduction to DataOps and MLOps. Emerging trends in data science.
Resources
- Lecture
- Material Summary
Exam Preparation and Q&A
Exam Preparation and Q&A

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Regular Exam
Regular Exam

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Retake Exam
Retake Exam

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Who is the target audience for the course?

The course is suitable for people with previous experience in programming Python (at the level of Programming Basics - performing simple calculations and logical checks, working with loops) and Jupyter Notebook. Completion of the course "Math Concepts for Developers" or equivalent knowledge such as working with matrices, vectors, numerical methods and complex numbers, skills for analysis, design, implementation and documentation of solutions are required. The required level of English required for the course must meet B2.

How to enroll in the course?

To sign up for the course, contact MLC Business School and follow the provided steps. After a successful payment, you will be enrolled in the training.

What is the deadline for enrolling in the course and when does it start?

Enrollment in the course is open until 05 February. The training starts on 06 February 2025. You can find a detailed schedule of classes in the Topics section.

How and where are the classes be held?

You can study online in real time. Immediately after each lesson, you also get access to the lesson recording and learning resources.

What is the date of the exam and what does it include?

The exam will be held online and includes practical exercises. The dates of the exam are March 15th (Final Exam) and March 22nd (Retake exam).

Do I get a certificate after the exam?

After passing the exam, you acquire a certificate issued by SoftUni if your score is above 70%.

How much is the course fee and what does it include?

Online

400 USD

Online training in real-time

Lifetime access to lesson recordings and learning content

Help from a mentor in understanding the learning materials

Access to a closed Facebook group with all other course participants

Taking a regular exam and receiving a certificate

SoftUni gives you a 100% guarantee of the quality of this course. The most important thing for us is that you acquire the necessary skills and knowledge. In the event that the training fails to fulfil your expectations, we guarantee a full refund of the amount you have paid. You can receive a refund until the fifth lesson of the course.