Udemy

Advance Python | Python for Datascience

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  • 134 Students
  • Updated 7/2025
  • Certificate Available
4.4
(47 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
12 Hour(s) 30 Minute(s)
Language
English
Taught by
Selfcode Academy
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.4
(47 Ratings)
3 views

Course Overview

Advance Python | Python for Datascience

A Python-Based Datascience Roadmap

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

  • Part 2: Further exploration of Python basics

2. Advance Python Concepts:

  • List Comprehension and Generators

  • File Handling

  • Exception Handling

  • Object-Oriented Programming (OOPs)

  • Decorators and Metaclasses

3. NumPy (Expanded Library Coverage):

  • Arrays and Array Operations

  • Array Indexing and Slicing

  • Broadcasting and Vectorization

  • Mathematical Functions and Linear Algebra

  • Array Manipulation and Reshaping

4. Pandas (Expanded Library Coverage):

  • Pandas Data Structures

  • Data Transformation and Manipulation

  • Data Cleaning and Preprocessing

  • Joining, Merging, and Reshaping

5. Data Visualization:

  • Advanced Matplotlib Techniques

  • Seaborn for Statistical Visualization

  • Plotly for Interactive Visualizations

  • Geospatial Data Analysis

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

  • Linear Regression

  • Logistic Regression

  • SVM, Decision Tree, Random Forest

  • Unsupervised Learning

  • Model Validation Techniques

  • Hyperparameter Tuning and Model Selection

7. Case Studies and Projects:

  • House Rent Prediction

  • Heart Disease Prediction

  • Customer Segmentation

    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!   **



Course Content

  • 7 section(s)
  • 29 lecture(s)
  • Section 1 Introduction to Python
  • Section 2 Advanced Python Concepts
  • Section 3 NumPy (expand on the basic library coverage)
  • Section 4 Pandas (expand on the basic library coverage)
  • Section 5 Data Visualization
  • Section 6 Machine Learning with Scikit-learn (expand on the basic library coverage)
  • Section 7 Case Studies and Projects

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

Reviews

  • B
    Bhupender Kumar
    4.0

    good content

  • N
    Numan Shaikh
    3.0

    Not able to access the recourses and jupyter notebooks.

  • D
    Deepak Kumar
    5.0

    Transformative learning experience. Highly recommended!

  • N
    Nibediya Nayak
    5.0

    i felt satisfied great work.

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