Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Data Science for Everyone: From Zero to Pro
Rating: 3.4 out of 5(5 ratings)
2,200 students

Data Science for Everyone: From Zero to Pro

Learn Data Analysis, Machine Learning & Python — No Prior Experience Needed!
Created byKusum Kumari
Last updated 7/2025
English
English [Auto],

What you'll learn

  • Understand the core concepts of data science and how it applies in real-world scenarios.
  • Gain a strong foundation in Python programming for data analysis and machine learning.
  • Clean, transform, and visualize data using Pandas, NumPy, Matplotlib, and Seaborn.
  • Query and manage data with SQL and connect to real-world databases.

Explore related topics

Course content

4 sections • 21 lectures • 8h 33m total length

  • Introduction6:11

    Explore data science as a multidisciplinary field turning raw data into insights with Python, NumPy, visualization, and scikit-learn to drive decisions.

  • Python for data science23:16

    Master Python for data science to transform data through the full workflow, using NumPy, pandas, scikit-learn, and visualization tools for data cleaning, exploration, and predictive modeling.

  • Statistic & probability for data science28:49

    Master statistics and probability for data science, quantifying uncertainty, exploring descriptive and inferential statistics, and applying regression and Bayesian methods as foundations of machine learning in real-world problems.

  • Machine learning fundamentals22:53

    Explore the core foundations and concepts of machine learning, including supervised, unsupervised, reinforcement, and semi-supervised learning. Follow the ML workflow from data collection and import to model deployment.

  • Deep Learning with TensorFlow or PyTorch33:07

    Explore deep learning frameworks TensorFlow and PyTorch, compare static and dynamic computation graphs, features, and performance, and assess real-world applications for researchers, developers, and organizations.

  • Natural Language Processing (NLP)27:00

    Explore natural language processing and how NLP enables computers to understand, generate, and interpret human language, including tokenization, NLU, NLG, and real-world applications like translation, sentiment analysis, and information extraction.

Requirements

  • Basic understanding of mathematics, including: High school-level algebra Basic statistics (mean, median, standard deviation)
  • Some familiarity with programming concepts: Variables, loops, and functions Experience with any language (Python, Java, C++, etc.) is helpful — Python preferred but not required.

Description

This comprehensive Data Science course is your all-in-one guide to becoming a job-ready data professional — even if you're starting from scratch.

You’ll go from beginner to advanced, learning everything from Python programming and data visualization to building real-world machine learning models. Through interactive lessons and hands-on projects, you'll gain the practical skills and confidence to work with data, solve real business problems, and build a career in data science.

Whether you’re aiming to become a Data Scientist, Data Analyst, Machine Learning Engineer, or just want to use data more effectively in your current job — this course will give you the tools to succeed.

  1. Python programming for data analysis

  2. Working with data using NumPy, Pandas, Matplotlib, and Seaborn

  3. Writing SQL queries to extract insights from databases

  4. Statistics & probability for data-driven decision-making

  5. Machine learning with Scikit-learn: regression, classification, clustering

  6. Introduction to Deep Learning, NLP, and Time Series Analysis

  7. Building and presenting real-world data science projects

  8. Preparing for data science job interviews and building a professional portfolio

  9. Python, Jupyter Notebooks, Google Colab

  10. Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn

  11. SQL (PostgreSQL or MySQL)

  12. Real datasets from Kaggle, UCI, and open APIs

  13. By the end of this course, you’ll have built a strong portfolio, mastered the essential data science workflow, and be ready to land your first job or freelance opportunity in the data space.

Who this course is for:

  • Anyone looking to start a career in data science, machine learning, or AI.
  • Students or graduates of any field (engineering, finance, biology, business, etc.) ready to transition into tech.

Report abuse