Advance Python

Goals

What will you learn in this course:

This course is designed to empower aspiring data analysts and data scientists with a mastery of Python programming. The comprehensive curriculum begins with a deep dive into Python fundamentals and progresses to advanced concepts, including list comprehension, generators, file handling, exception handling, and object-oriented programming. Extensive coverage of NumPy equips students with the skills to handle complex data, while Pandas is explored for data manipulation, cleaning, and various operations. The course also delves into advanced data visualization techniques using Matplotlib, Seaborn, and Plotly, with a focus on geospatial data analysis. Machine learning is a key component, featuring Scikit-learn for linear regression, logistic regression, SVM, decision tree, random forest, unsupervised learning, and model validation. Practical application is emphasized through case studies and projects, such as house rent prediction and heart disease prediction. Hands-on learning and expert-led sessions ensure a thorough understanding, preparing students for rewarding opportunities in data science and analytics. Unlocking one's potential in Python programming and data analysis is the ultimate goal of this course, providing a pathway to proficiency in the dynamic field of data science.

Prerequisites

What are the prerequisites for this course?

1. Basic Programming Knowledge:

   • Familiarity with basic programming concepts, such as variables, loops, conditional statements, and functions, would be beneficial.

2. Introductory Python Skills:

   • While the course covers Python comprehensively, having a basic understanding of Python syntax and programming structures beforehand would help participants grasp advanced Python concepts more easily.

3. Mathematics and Statistics Fundamentals:

   • Some familiarity with fundamental mathematical and statistical concepts may be useful, especially when delving into machine learning topics.

4. Data Analysis Awareness:

   • An awareness of the basics of data analysis concepts could be beneficial, though the course likely covers these aspects as part of its curriculum.