Complete Data Science Training with Python for Data Analysis

Beginners python data analytics : Data science introduction : Learn data science : Python data analysis methods tutorial

4.47 (2127 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Python data analytics - Install Anaconda & Work Within The iPytjhon/Jupyter Environment, A Powerful Framework For Data Science Analysis

Python Data Science - Become Proficient In Using The Most Common Python Data Science Packages Including Numpy, Pandas, Scikit & Matplotlib

Data analysis techniques - Be Able To Read In Data From Different Sources (Including Webpage Data) & Clean The Data

Data analytics - Carry Out Data Exploratory & Pre-processing Tasks Such As Tabulation, Pivoting & Data Summarizing In Python

Become Proficient In Working With Real Life Data Collected From Different Sources

Carry Out Data Visualization & Understand Which Techniques To Apply When

Carry Out The Most Common Statistical Data Analysis Techniques In Python Including T-Tests & Linear Regression

Understand The Difference Between Machine Learning & Statistical Data Analysis

Implement Different Unsupervised Learning Techniques On Real Life Data

Implement Supervised Learning (Both In The Form Of Classification & Regression) Techniques On Real Data

Evaluate The Accuracy & Generality Of Machine Learning Models

Build Basic Neural Networks & Deep Learning Algorithms

Use The Powerful H2o Framework For Implementing Deep Neural Networks

Course Gallery

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Comidoc Review

Our Verdict

The Complete Data Science Training with Python for Data Analysis course serves as a solid foundation for beginners in data science using Python. While the course covers numerous topics, some areas may require additional exploration beyond the scope of the course. The use of real-world examples and access to the instructor's knowledge make this an appealing starting point for learning data analysis techniques.

What We Liked

Comprehensive introduction to data science with Python, covering a wide range of tools and techniques
Real-world data sets used in examples and exercises
Covers essential data analysis tasks such as data cleaning, exploration, and visualization
Instructor has deep knowledge of the subject matter

Potential Drawbacks

Some lectures could benefit from a deeper focus on specific datasets for better continuity
Later sections may lack clear explanations for certain commands and function calls
Outdated packages or data sets can be an issue, requiring independent research to find updated resources
Instructor's communication skills could be improved for more engaging lectures