Course Details
Programming For Data Science
This training course is designed to introduce participants to the essential programming concepts and tools used in the field of data science. Over the course of 5 days, participants will learn the fundamentals of programming languages such as Python or R, and how to use them to analyze and manipulate data. The training will cover topics such as data structures, control structures, functions, and object-oriented programming. Participants will also learn how to use popular data science libraries such as Pandas, Numpy, and Scikit-learn to analyze and visualize data, and how to create machine learning models using these libraries. Throughout the course, participants will engage in hands-on exercises and projects to reinforce their understanding of programming concepts and their application to data science. By the end of the training, participants will have a solid foundation in programming for data science and be able to apply this knowledge to real-world data problems.
Learning Outcomes:
- Proficiency in one or more programming languages such as Python or R, and an understanding of how to use them for data manipulation, analysis, and visualization. - Familiarity with popular data science libraries such as Pandas, Numpy, and Scikit-learn, and the ability to use them to solve real-world data problems. - An understanding of the principles and techniques of machine learning, and the ability to create and evaluate machine learning models. - Hands-on experience with programming projects and exercises that reinforce their learning and provide practical experience in applying programming concepts to data science.
Course Outline:
Overview of data science and the role of programming
Introduction to programming languages such as Python or R
Setting up a programming environment
Data structures and types
Control structures (loops and conditionals)
Functions and modules
Working with data frames and tables
Data aggregation and summarization
Data visualization with libraries such as Matplotlib or ggplot2
Overview of machine learning concepts and techniques
Supervised learning and unsupervised learning
Linear regression, decision trees, and clustering algorithms
Object-oriented programming for data science
Introduction to popular data science libraries such as Pandas, Numpy, and Scikit-learn
Applying programming skills to real-world data problems
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