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.
Visualize Data with Matplotlib and Seaborn: Create insightful visualizations, including histograms, bar graphs, and relational plots, to explore and communicate data trends.
Leverage Numpy for Numerical Analysis: Use Numpy arrays for efficient numerical computations, including generating data, indexing, and reshaping multi-dimensional arrays.
Explore Advanced Visualizations with Seaborn: Visualize multi-dimensional datasets and relational data to uncover deeper insights.
Utilize Regular Expressions for Data Parsing: Apply regex techniques to search and process text-based data effectively.
Perform Scientific Computations with SciPy: Use SciPy for advanced mathematical and scientific computations to support complex data analyses.
Clean and Prepare Data for Analysis: Master techniques for handling missing values, cleaning datasets, and transforming data for analytical workflows.
Essential Skills Gained
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.
Format
5 day course with lecture and hands-on labs.
Audience
Data Analysts or Administrators
Business Intelligence Professionals
Data Scientists
Software Developers
Description
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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