Advance Python | Python for Datascience

Advance Python | Python for Datascience | Udemy
English | Size: 4.02 GB
Genre: eLearning

A Python-Based Datascience Roadmap

What you’ll learn
The course is designed to provide students with a strong foundation in advanced Python programming, data analysis, and machine learning.
Students will learn advanced programming concepts, including list comprehensions, file I/O operations, exception handling, and lot more advance python concepts.
Data manipulation and analysis using the NumPy and Pandas libraries, covering data cleaning, preprocessing, and transformation techniques.
Data visualization using Matplotlib, Seaborn, and Plotly for creating informative and visually appealing plots and charts.
Implementation and evaluation of various machine learning algorithms, such as supervised and unsupervised learning, using the Scikit-learn library.
Optional exploration of advanced topics like natural language processing, web scraping, time series analysis, and recommender systems for a more comprehensive u

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! **

Who this course is for:
Students interested in exploring data analysis, cleaning, and preprocessing techniques using Python will find this course helpful in understanding how to work w
For students keen on expanding their knowledge beyond basic programming, this course delves into advanced Python concepts, object-oriented programming, and more
Students those who wants to learn advanced Python concepts, and are intrested towards the field of datascience
Students and professionals in the field of machine learning and artificial intelligence looking to strengthen their understanding of Python for implementing and
Data analysts and data scientists seeking to leverage Python for advanced data manipulation, analysis, and visualization tasks.
Intermediate Python developers aiming to enhance their skills and delve deeper into advanced programming concepts.
Software engineers interested in expanding their knowledge of Python for various applications, including web development, data processing, and automation.

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