Applied Data Science with Python Workshop by Tonex
The Applied Data Science with Python Workshop by Tonex is a comprehensive training program designed to equip participants with practical skills in leveraging Python for effective data science applications. This hands-on workshop focuses on real-world scenarios, enabling participants to apply their learning immediately in their professional roles.
The Applied Data Science with Python Workshop by Tonex offers a dynamic learning experience for professionals eager to harness the power of Python in data science applications. Participants delve into Python programming essentials, master data manipulation with Pandas, and refine data visualization using Matplotlib and Seaborn.
The workshop covers machine learning with Scikit-learn, statistical analysis, and explores big data processing with Apache Spark. With a focus on practicality, attendees develop hands-on skills through projects and case studies, empowering them to build and deploy machine learning models. This workshop is tailored for data scientists, analysts, and software engineers, providing a solid foundation for applied data science proficiency.
Learning Objectives:
- Master fundamental Python programming for data science.
- Acquire proficiency in data manipulation and analysis using Pandas.
- Explore data visualization techniques with Matplotlib and Seaborn.
- Gain expertise in machine learning using popular Python libraries such as Scikit-learn.
- Understand the principles of statistical analysis for data-driven decision-making.
- Develop skills in working with big data through tools like Apache Spark.
- Learn how to build and deploy machine learning models in real-world applications.
- Enhance problem-solving abilities through hands-on projects and case studies.
Audience: This workshop is ideal for data scientists, analysts, software engineers, and professionals seeking to enhance their skills in applied data science using Python. Participants should have a basic understanding of Python programming and a keen interest in leveraging data for actionable insights.
Course Outline:
Introduction to Python for Data Science
- Python basics and syntax
- Data types and structures
- Control structures and functions
- Error handling and debugging
Data Manipulation and Analysis with Pandas
- Introduction to Pandas and DataFrames
- Data cleaning and preprocessing
- Merging and reshaping datasets
- Time series analysis with Pandas
Data Visualization with Matplotlib and Seaborn
- Creating basic plots and charts
- Customizing visualizations for effective communication
- Exploratory data analysis through visualizations
- Geospatial data visualization
Machine Learning with Scikit-learn
- Supervised and unsupervised learning algorithms
- Model evaluation and hyperparameter tuning
- Feature engineering and selection
- Introduction to deep learning with TensorFlow and Keras
Statistical Analysis for Data Science
- Descriptive statistics and data distributions
- Inferential statistics and hypothesis testing
- Correlation and regression analysis
- Bayesian statistics for decision-making
Big Data Processing with Apache Spark
- Introduction to big data and Spark
- Data processing with Spark RDDs
- Spark DataFrames and SQL
- Machine learning with Spark MLlib
Building and Deploying Machine Learning Models
- Model development and training
- Model evaluation and validation
- Model deployment in production environments
- Monitoring and updating deployed models
Hands-on Projects and Case Studies
- Applying learned concepts to real-world scenarios
- Collaborative projects for practical experience
- Case studies from various industries
- Q&A and guidance for individual projects