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 (2124 reviews)
11 545
students
13 hours
content
Oct 2021
last update
$84.99
regular price
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
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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
Related Topics
1258298
udemy ID
18/06/2017
course created date
15/10/2019
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