Complete Data Science Training With Python For Data Analysis

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

Last updated 2022-01-10 | 4.6

- 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

What you'll 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

* Requirements

* Be Able To Use PC At A Beginner Level
* Including Being Able To Install Programs
* A Desire To Learn Data Science
* Prior Knowledge Of Python Will Be Useful But NOT Necessary

Description

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