Python for Data Science
- Created By raju2006
- Last Updated December 21st, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Python is the language of data science and this Python for Data Science class will expose you to the most important libraries (i.e., NumPy, Pandas, Matplotlib, and Scikit-learn) that will enable you to effectively do data science using Python.
Long Description:
Discover the power of Python programming with our comprehensive training course. Python's popularity opens doors to a myriad of opportunities, from data analytics and web development to database management. Join us as we delve into Python 3, starting with a hassle-free installation process. Explore the world of Python through interactive Jupyter Notebooks, designed for an engaging learning experience. Our hands-on approach guides you through Python's structure, including Functions, Methods, and Object-Oriented Programming, along with their benefits. You'll also tackle two key milestones to ensure your understanding. Embrace the future of coding with our Python training course.
Course Code/Duration:
BDT20 / 1 Day
Learning Objectives:
After this course, you will be able to:
- Install Anaconda on a personal computer.
- Understand the various options for performing data science.
- Understand the reasons for Python’s popularity in data science.
- Learn the primary libraries for data science in Python including NumPy, Pandas, Matplotlib and Scikit-learn.
- Perform exploratory data analysis using Pandas.
- Use Matplotlib and Seaborn to perform data visualization.
- Prepare data for machine learning
- Apply machine learning on a variety of datasets
- Understand the data science process
- Understand the big picture and the importance of data science in business, industry, and technology.
- Basic Python Programming.
- Developers, Business Analysts want to learn how to program in Python. Anyone willing to develop a solid foundation for data science.
- Developers, Business Analysts want to learn how to program in Python. Anyone willing to develop a solid foundation for data science.
Course Outline:
- Course Introduction
- Overview of data science
- Understand the reasons for Python’s popularity in data science
- Installing Anaconda
- Milestone 1: Learn how to use Jupyter Notebooks
- The data science process
- Essential Python data science libraries
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
- Data Visualization
- Line Chart
- Scatterplot
- Pairplot
- Histogram
- Density Plot
- Bar Chart
- Boxplot
- Customizing Charts
- Prepare data for machine learning
- Milestone 2: Perform exploratory data analysis using Pandas
- Milestone 3: Apply machine learning algorithms using Scikit-learn
- Conclusion: Data Science in the real world, next steps
Structured Activity/Exercises/Case Studies:
- Milestone Project 1: Install and setup Anaconda/Jupyter Notebooks
- Milestone Project 2: Perform exploratory data analysis using Pandas
- Milestone Project 3: Apply machine learning algorithms using Scikit-learn
Training material provided:
Yes (Digital format)
