Introduction to Python for Data Science (8 ECTS)

Objective

This course is designed to introduce the students to the concept of data science and data science challenges. It takes the students from the basics of Python programming to exploring many different types of data. This course provides a unique opportunity for the student to get hands-on experience with popular Python libraries for data science such as NumPy, Pandas, and Matplotlib. By the end of this course, the students will know about the data science workflow. Moreover, they learn the basics of the Python programming environment, including fundamental Python programming techniques such as lambdas, manipulating large arrays, reading and manipulating tabular data, and visualization.

This course is 100% virtual, thanks to the comprehensive tutorial videos and content prepared for this course.

Content

1. Introduction
What is Data Science? – Who is a Data Scientist? – Demands for Data Scientists – Data Science Workflow – Data Science Challenges – Programming in Data Science – Python for Data Science

2. Getting Started with Python
Jupyter Notebook – Anaconda – Anaconda Installation – Getting Started with Jupyter Notebook – Python Syntax – Introduction about Python syntax – Important characteristics of Python syntax – Examples without detailed description about the examples – Input and Output – Variables – Data Types – Python Operators – Arithmetic Operations – Comparison Operation – Logical Operations – String Operations

3. Python Data Structures
Introduction – Lists – List Indexing – List Slicing – List Manipulation: Add New Elements – List Manipulation: Change and Remove Elements – Tuple – Accessing Tuple Elements – Working with Tuples – Set – Set Manipulation – Dictionary – Accessing Dictionary Elements – Dictionary Manipulation

4. Python Programming Fundamentals
Conditions: Introduction – Conditions: if – Conditions: else – Conditions: elif – Loops: Introduction – Loops: for – Loops: for in data structures – Loops: while – Loops: break, continue – Functions: Introduction – Functions: user-defined functions I – Functions: user-defined functions II – Comprehensions

5. Introduction to Numpy
Introduction to NumPy – Array – Arrays Primary Functions – Intrinsic NumPy Array Creation – Creating Random Arrays – Standard Mathematics Operations – Broadcasting in NumPy – Vector and Matrix Mathematics – Statistics in NumPy – Common Mathematics Functions – Filtering – Copy and View

6. Data Manipulation with Pandas
Introduction to Pandas – Series and DataFrame – Input and output – Summary and Statistics – Column Selection – Creating New Column – Removing Column – Location Selection – Filtering – Group by– Useful Functions – Handling Missing Data– Apply Function – Concatenation – Merging

7. Data Visualization with Matplotlib
Introduction to Matplotlib – Plot – Bar Plot – Histogram – Pie Chart – Scatter Plot – Plot Attributes – Subplot

8. Final Tasks
Project – Self-study Essay

Location and time

Online TechClass environment.

Materials

Lecture slides, tutorial videos, quizzes, exercises

Teaching methods

- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study

Employer connections

N/A

Exam schedules

N/A

International connections

N/A

Completion alternatives

N/A

Student workload

Lectures = 75h
Exercises = 75h
Self-study = 50h
Quizzes = 5h
Project = 50h
Total = 255 hours

Content scheduling

Lectures = 75h
Exercises = 75h
Self-study = 50h
Quizzes = 5h
Project = 50h
Total = 255 hours

Evaluation scale

Hyväksytty/Hylätty

Assessment criteria, approved/failed

The student will pass this course after submitting the required quizzes, assignments, and the final project.

Assessment methods and criteria

Exercises 65%
Quizzes 5%
Project 20%
Essay 10%