Data science programming courses

Python and Data Science

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Price (KGS)
10000 soms per month

Duration
5 months - length of each class 2 hours (3 acad.hours)

Schedule
Mon, Wed, Fr 19:00 - 21:00

Start date of classes
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Course Description:

Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.

In this practical course, you will start from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas Data Frames. Along the way, you’ll learn about Python functions and control flow. Plus, you’ll look at the world of data visualizations with Python and create your own stunning visualizations based on real data.

The purpose of this course is to supplement students with the basic knowledge of data mining techniques. These techniques will include descriptive ones such as clustering, association analysis and sequence analysis and, predictive ones such as decision trees and logistic regression. The theoretical lectures will be coupled by applied studies where the necessary skills for using a data mining software package (RapidMiner, Weka and Python) will also be given. By the end of the course, the students will be able to identify the real life problems where a data mining approach will be useful and apply some of the alternative techniques that can be used to solve those problems.

Learning Outcomes:

1. Understand the basic application areas of data mining
2. Identify where a descriptive and where a predictive technique should be used
3. Know clustering methods in general and K-means algorithm in detail
4. Understand how basic association analysis methods work
5. Understand Python language basics and apply to data science
6. Practice iterative data science using Jupyter notebooks
7. Analyze data using Python libraries like pandas and numpy
8. Create stunning data visualizations with matplotlib, folium and seaborn
9. Build machine learning models using scipy and scikitlearn
10. Demonstrate proficiency in solving real life data science problems