Udemy – Advance Python Python for Datascience

Udemy – Advance Python | Python for Datascience
English | Tutorial | Size: 4.02 GB

A Python-Based Datascience Roadmap


Students should have understanding of fundamental Python concepts, including variables, data types, loops, and functions.
A genuine interest in working with data, conducting data analysis, and implementing machine learning models is crucial to fully benefit from the course content.
A foundational knowledge of basic mathematical concepts, such as algebra and statistics, will be helpful for comprehending certain aspects of data analysis, machine learning, and numerical computing.


Ready to advance your Python skills? Our easy-to-follow Advanced Python course is tailored for learners of all levels, This course is crafted for students aspiring to master Python and dedicated to pursuing careers as data analysts or data scientists. It comprehensively covers advanced Python concepts, providing students with a strong foundation in programming and data analysis, focusing on data analysis, visualization, and machine learning.

Discover the power of Python in handling complex data, creating engaging visuals, and building intelligent machine-learning models.

Course Curriculum:

1. Introduction to Python:

Part 1: Dive into Python fundamentals (27:42)

Part 2: Further exploration of Python basics (27:49)

2. Advance Python Concepts:

List Comprehension and Generators (24:07)

File Handling (17:58)

Exception Handling (15:12)

Object-Oriented Programming (OOPs) (26:34)

Decorators and Metaclasses (16:06)

3. NumPy (Expanded Library Coverage):

Arrays and Array Operations (31:38)

Array Indexing and Slicing (29:39)

Broadcasting and Vectorization (26:43)

Mathematical Functions and Linear Algebra (21:55)

Array Manipulation and Reshaping (22:52)

4. Pandas (Expanded Library Coverage):

Pandas Data Structures (23:26)

Data Transformation and Manipulation (23:36)

Data Cleaning and Preprocessing (29:30)

Joining, Merging, and Reshaping (22:56)

5. Data Visualization:

Advanced Matplotlib Techniques (28:08)

Seaborn for Statistical Visualization (20:53)

Plotly for Interactive Visualizations (28:52)

Geospatial Data Analysis (25:31)

6. Machine Learning with Scikit-learn (Expanded Library Coverage):

Linear Regression (24:19)

Logistic Regression (15:55)

SVM, Decision Tree, Random Forest (23:56)

Unsupervised Learning (26:54)

Model Validation Techniques (23:10)

Hyperparameter Tuning and Model Selection (30:49)

7. Case Studies and Projects:

House Rent Prediction (55:10)

Heart Disease Prediction (44:28)

Customer Segmentation (12:41)

Why Choose Our Course?

In-depth Modules Covering Python, NumPy, Pandas, Data Visualization, and Machine Learning

Hands-on Learning with Real-world Case Studies

Expert-led Sessions for Comprehensive Understanding

Unlock Your Potential in Data Science and Python Programming

With hands-on practice and expert guidance, you’ll be prepared for rewarding opportunities in data science and analytics.

** Join us now to become a proficient Python data analyst and unlock a world of possibilities! **

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