Build a Strong Python Foundation: Master the fundamentals of Python programming, including functions, data structures, and control flow, as a basis for data science.
Use Jupyter Notebooks for Data Science Workflows: Learn to create and manage Jupyter Notebooks for organizing and presenting data analyses effectively.
Manipulate Data with Pandas: Work with DataFrames to clean, modify, and analyze structured data using Boolean masks, time series, and groupby operations.
Interact with Databases: Connect to and query relational databases like SQLite and PostgreSQL, as well as NoSQL databases like MongoDB, to manage and analyze data.
This course offers a comprehensive introduction to Python for data science, equipping participants with the skills to manipulate, analyze, and visualize data effectively. Starting with foundational Python programming, the course progresses to cover essential tools like Pandas for data manipulation, Matplotlib and Seaborn for visualization, and Numpy for numerical computations. Participants will learn to work with relational databases like SQLite and PostgreSQL, as well as NoSQL databases like MongoDB, to manage and analyze large datasets. The course also explores using Jupyter Notebooks for organizing analyses, cleaning and preparing data, and performing advanced computations with SciPy. By the end, attendees will be prepared to tackle real-world data challenges and make data-driven decisions confidently.
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier