Python for Data Science and Machine Learning Bootcamp

  • Created by Jose Portilla
  • Course Duration 25 hours
  • Price USD$329.000 VND
  • User Rating 4.6
  • Platform Udemy
  • Course Link Explore Course
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!


Are you ready to embark on your journey to becoming a highly skilled Data Scientist? Discover the immense potential of Python as you dive into the world of data analysis, create stunning visualizations, and harness the power of cutting-edge machine learning algorithms. Whether you’re a beginner with some programming experience or an experienced developer looking to make the leap into Data Science, this comprehensive online course is the perfect guide for you. With the demand for Data Scientists skyrocketing and the average salary exceeding $120,000, now is the ideal time to venture into this rewarding career. Join us and solve the world’s most intriguing problems through the power of data!

Course Overview

This comprehensive course is carefully designed to equip you with the essential skills and knowledge required to excel in the field of Data Science. Comparable to other costly Data Science bootcamps, our course offers an extraordinary learning experience at a fraction of the price. With over 25 hours of on-demand video, 13 in-depth articles, 5 downloadable resources, and a wealth of hands-on exercises, you’ll gain a deep understanding of Python’s capabilities for data analysis and machine learning. Every lecture is accompanied by detailed code notebooks, ensuring you have a comprehensive reference for future projects.

What You’ll Gain

  1. Proficiency in Python for Data Science and Machine Learning: Acquire a strong foundation in Python programming and unleash its full potential for data analysis and machine learning applications.
  2. Data Analysis with NumPy and Pandas: Learn to utilize NumPy and Pandas libraries to handle numerical data and perform complex tasks with ease. Master the art of data manipulation, cleaning, and exploration.
  3. Visualizations with Matplotlib, Seaborn, and Plotly: Harness the power of Matplotlib, Seaborn, and Plotly libraries to create captivating and informative visual representations of your data. Transform complex information into compelling visuals that enhance your storytelling.
  4. Machine Learning with Scikit-Learn: Explore the world of machine learning algorithms using Scikit-Learn. Dive into linear regression, K-nearest neighbors, K-means clustering, decision trees, random forests, support vector machines, and more.
  5. Big Data Analysis with Spark: Gain hands-on experience in analyzing large datasets using Spark, a powerful distributed computing framework. Discover how to leverage Spark’s capabilities to extract valuable insights from big data.
  6. Natural Language Processing (NLP) and Neural Networks: Unleash the power of NLP techniques to analyze text data, build spam filters, and extract meaningful insights from unstructured text. Dive into the fascinating world of neural networks and deep learning.
  7. Real-World Projects and Applications: Apply your newly acquired skills to real-world projects and gain practical experience that prepares you for challenges in the industry.

Who This Course is For

  • Individuals with some programming experience who want to venture into the field of Data Science.
  • Experienced developers looking to expand their skillset and explore the exciting realm of Data Science and Machine Learning.

Course Curriculum

  1. Introduction to Data Science and Machine Learning
    • The emergence of Data Science
    • Why Data Science is a rewarding career
    • Overview of the course curriculum
  2. Python Programming for Data Science
    • Introduction to Python and its applications in Data Science
    • Setting up your Python environment
    • Basic Python syntax and data types
    • Control flow and loops
    • Functions and modules in Python
  3. NumPy: Handling Numerical Data
    • Introduction to NumPy and its importance in Data Science
    • Creating NumPy arrays
    • Performing array operations and manipulations
    • Statistical analysis with NumPy
  4. Pandas: Data Analysis Made Easy
    • Introduction to Pandas and its role in data manipulation
    • Working with Pandas Series and DataFrames
    • Data cleaning and preprocessing with Pandas
    • Exploratory data analysis using Pandas
  5. Data Visualization with Matplotlib
    • Introduction to Matplotlib and its visualization capabilities
    • Creating basic plots and customizing visualizations
    • Plotting with multiple subplots
    • Advanced visualization techniques with Matplotlib
  6. Statistical Plots with Seaborn
    • Overview of Seaborn and its advantages in statistical visualization
    • Creating univariate and bivariate plots
    • Visualizing distributions and relationships in data
    • Enhancing plots with Seaborn’s styling options
  7. Interactive Visualizations with Plotly
    • Introduction to Plotly and its interactive plotting features
    • Creating interactive line plots, scatter plots, and bar charts
    • Customizing Plotly visualizations with annotations and layouts
    • Building interactive dashboards with Plotly
  8. Machine Learning with Scikit-Learn
    • Introduction to machine learning and its applications
    • Linear regression for predictive modeling
    • K-nearest neighbors for classification tasks
    • K-means clustering for unsupervised learning
    • Decision trees and random forests for ensemble learning
    • Support vector machines for classification and regression
  9. Big Data Analysis with Spark
    • Understanding big data and its challenges
    • Introduction to Apache Spark and its ecosystem
    • Processing and analyzing big data using Spark’s DataFrame API
    • Applying machine learning algorithms with Spark MLlib
  10. Natural Language Processing (NLP)
    • Introduction to NLP and its applications
    • Text preprocessing and cleaning
    • Feature extraction from text data
    • Building NLP models for sentiment analysis and text classification
  11. Neural Networks and Deep Learning
    • Fundamentals of neural networks
    • Building and training neural networks using TensorFlow
    • Deep learning techniques for image classification
    • Transfer learning for leveraging pre-trained models
  12. Real-World Projects and Applications
    • Applying Data Science and Machine Learning techniques to solve real-world problems
    • Hands-on projects and case studies
    • Best practices and tips for successful project implementation


Unlock the doors to an exciting career in Data Science and Machine Learning by enrolling in our comprehensive Python for Data Science and Machine Learning course. With over 25 hours of in-depth video content, hands-on exercises, and comprehensive code notebooks, you’ll gain the skills needed to tackle complex data challenges. Stand out in the competitive job market and become a sought-after data scientist. Enroll today and unlock a world of opportunities!


Q1: Can I enroll in this course without any programming experience?

Absolutely! This course is designed for both beginners with no programming experience and experienced developers looking to transition into Data Science. The course starts with the fundamentals of Python programming and gradually progresses to advanced topics.

Q2: How long do I have access to the course materials?

Once you enroll, you’ll have full lifetime access to the course content. You can learn at your own pace and revisit the materials whenever you need to brush up on your skills or explore advanced topics.

Q3: Are there any prerequisites for this course?

While programming experience is beneficial, it is not mandatory. However, familiarity with basic programming concepts will help you grasp the course content more effectively. Additionally, having admin permissions to download files will be required for certain exercises and projects.

Q4: Will I receive a certificate upon completion of the course?

Yes! Upon completing the course, you will receive a certificate of completion, which you can showcase to potential employers or add to your professional portfolio.

Q5: Can I access the course on my mobile device or TV?

Yes, you can access the course on both mobile devices and TVs. Enjoy the flexibility of learning on the go or immersing yourself in the course content on a larger screen.

Now that you’re equipped with the essential information, take the first step towards a successful career in Data Science and Machine Learning. Enroll in our Python for Data Science and Machine Learning course today and unlock a world of endless possibilities!