Course

Data Science - February 2025

uring this course, participants will form the right set of knowledge and skills for a successful professional start in the Data Science field. After completing the training, the participants will be able to manage the entire process of collecting and processing data: from asking the right questions to publishing the result. In addition, they will have a developed portfolio that demonstrates this knowledge. During the course, participants will answer practical questions such as: • How do you choose the right method for data analysis?; • How do you apply the scientific method for decision-making?; • How do you use statistical methods to draw conclusions?; • How do you collect and clean data to prepare it for analysis and modeling?; • How is data modeling performed?; • What is Exploratory data analysis and how is it applied? The course ends with guidelines for building a complete data science application - code architecture and research structure. The training is practically oriented and new concepts will be reinforced by writing code.

Topics

Чиглүүлэх хичээл
Чиглүүлэх хичээл

Python хэлний ерөнхий ойлголт
Resources
- Чиглүүлэх хичээл - бичлэг 1
- Чиглүүлэх хичээл - бичлэг 2
Data Acquisition
Data Acquisition

• Getting to Know Pandas;
• Acquiring Data from Different Sources;
• CSV files, Excel Files;
• Web Services. JSON and XML;
• Databases;
• Web Crawling and Scraping;
• Merging Different Sources. Constraints and Validity.
Resources
Data Tidying and Cleaning
Data Tidying and Cleaning

• Tidy Data: Normalization;
• Subsetting and Sorting, 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, Examples;
• Plots: Histograms, Scatterplots, Line Plots, Pie Charts. Usages and Examples;
• Enhancing Plots: Colors, Labels, 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
Нэмэлт хичээл - Data Transformation
Нэмэлт хичээл - Data Transformation

No description yet
Resources
- Video - Record
Шалгалтын бэлтгэл хичээл
Шалгалтын бэлтгэл хичээл

Data Science хөтөлбөрийн шалгалтын мэдээллийг хүргэж байна.

Шалгалтын “Final Assignment_Data Science” файл дээрх даалгаврын дагуу хавсралтаар илгээсэн CSV файл дээрх дата дээр ажиллаж хийх даалгавар байгаа.

Шалгалтын даалгавар хураалгах хугацаа: 2025-03-16-ны Ням гаригийн 18:00 цаг хүртэл хурааж авна.

Шалгалтын даалгаврыг Zip файл болгож project@mlctraining.mn хаягаар илгээнэ.

Та өөрийн овог нэрийг бичихээ мартуузай.
Resources
- Video - Record

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Who is the target audience for the course?

The course is suitable for people with basic programming knowledge (variables, control constructs, lists, functions) and knowledge of mathematics at grade 12 level. It is desirable that students have knowledge of Python and elements of "higher" mathematics, such as the basics of statistics and mathematical analysis. 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 15rd (Final Exam) and March 22th (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.