08-08-2024, 09:40 PM
![[Image: ed7deecca372b374f86c3944bc66c60c.jpg]](https://i123.fastpic.org/big/2024/0809/0c/ed7deecca372b374f86c3944bc66c60c.jpg)
Data Science Tools: Python, Pandas, Machine Learning, EDA
Published 6/2024
Created by Bluelime Learning Solutions
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 122 Lectures ( 8h 49m ) | 2.1 GB
Learn Data Science Skills with: Python, Pandas, NumPy, Matplotlib, Seaborn, Machine Learning, Data Prep, and EDA
What you'll learn:
Utilize essential data science libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Differentiate between structured and unstructured data.
Gain proficiency in Python programming language for data analysis.
Understand the fundamental concepts of data science.
Differentiate between data science, data engineering, and data analysis.
Recognize the applications and industry impact of data science.
Install Python and set up a development environment on Windows and macOS.
Familiarize with Jupyter Notebook and use it for interactive data analysis.
Explore and manipulate data using Pandas DataFrames.
Create and manipulate Pandas Series for efficient data handling.
Load datasets into Pandas and perform initial data inspection and cleaning.
Transform and analyze data using Pandas methods.
Visualize data using Matplotlib and Seaborn for insights and reporting.
Utilize statistical techniques for data exploration and hypothesis testing.
Define machine learning and its application in data science.
Understand supervised, unsupervised, and reinforcement learning techniques.
Preprocess data for machine learning models, including handling missing values and encoding categorical variables.
Build, train, and evaluate machine learning models using scikit-learn.
Measure model performance using metrics like accuracy, confusion matrix, and classification report.
Deploy a machine learning model for real-time predictions and understand model interpretability techniques.
Requirements:
Basic Computer Literacy
No prior programming experience required, but familiarity with the basics of programming concepts (e.g., variables, loops, conditional statements) is beneficial.
Access to a computer with internet connectivity.
Ability to install software, including Python and necessary libraries (installation instructions will be provided).
Willingness to learn and explore new tools and technologies (e.g., Jupyter Notebook).
![[Image: dJNS0RlH_o.jpg]](https://images2.imgbox.com/52/3d/dJNS0RlH_o.jpg)
What you'll learn:
Utilize essential data science libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Differentiate between structured and unstructured data.
Gain proficiency in Python programming language for data analysis.
Understand the fundamental concepts of data science.
Differentiate between data science, data engineering, and data analysis.
Recognize the applications and industry impact of data science.
Install Python and set up a development environment on Windows and macOS.
Familiarize with Jupyter Notebook and use it for interactive data analysis.
Explore and manipulate data using Pandas DataFrames.
Create and manipulate Pandas Series for efficient data handling.
Load datasets into Pandas and perform initial data inspection and cleaning.
Transform and analyze data using Pandas methods.
Visualize data using Matplotlib and Seaborn for insights and reporting.
Utilize statistical techniques for data exploration and hypothesis testing.
Define machine learning and its application in data science.
Understand supervised, unsupervised, and reinforcement learning techniques.
Preprocess data for machine learning models, including handling missing values and encoding categorical variables.
Build, train, and evaluate machine learning models using scikit-learn.
Measure model performance using metrics like accuracy, confusion matrix, and classification report.
Deploy a machine learning model for real-time predictions and understand model interpretability techniques.
Requirements:
Basic Computer Literacy
No prior programming experience required, but familiarity with the basics of programming concepts (e.g., variables, loops, conditional statements) is beneficial.
Access to a computer with internet connectivity.
Ability to install software, including Python and necessary libraries (installation instructions will be provided).
Willingness to learn and explore new tools and technologies (e.g., Jupyter Notebook).
![[Image: signature.png]](https://softwarez.info/images/avsg/signature.png)
