Data Analytics with Python: intermediate

What is the course about?

This intermediate course builds on foundational Python programming to develop more sophisticated data analytics skills. You'll learn how to extract, clean, transform, and analyze complex datasets using pandas, NumPy, and other specialized libraries. The course emphasizes practical applications in business contexts, with hands-on projects that simulate real-world analytics challenges.

A significant focus will be on creating live data solutions through web scraping and database integration. You'll learn how to build automated systems that continuously collect, process, and store data from online sources, creating dynamic databases that update in real time. This will enable you to work with the most current information for your analyses.

The course places strong emphasis on machine learning for predictive analytics and forecasting. You'll learn how to build, evaluate, and optimize various machine learning models to extract meaningful patterns from data. Through techniques like cross-validation, hyperparameter tuning, and feature engineering, you'll develop the skills to create accurate forecasting models that can drive business decisions and strategy.

You'll also explore advanced visualization techniques and statistical analysis methods to derive and communicate insights effectively. By the end of the course, you'll be able to create end-to-end data solutions that span from automated data collection to optimized predictive models and interactive dashboards.

What will we cover?

1. Advanced data manipulation with pandas (multi-indexing, groupby operations, pivot tables)
2. Data cleaning and preprocessing techniques for handling messy real-world datasets
3. Statistical analysis and hypothesis testing using scipy and statsmodels
4. Advanced data visualization with matplotlib, seaborn, and plotly
5. Time series analysis and forecasting techniques
6. Web scraping with BeautifulSoup and Selenium for data collection
7. Building and maintaining live databases from scraped data
8. Working with different data formats (JSON, APIs, SQL databases)
9. Creating and managing automated data pipelines for real-time analytics
10. Exploratory data analysis methodologies
11. Machine learning with scikit-learn: building, evaluating, and optimizing models
12. Model selection, hyperparameter tuning, and cross-validation techniques
13. Predictive modeling and forecasting for business decision-making
14. Best practices for reproducible analysis with Jupyter notebooks
15. Creating interactive dashboards with Dash or Streamlit that connect to live data sources

What will I achieve?
By the end of this course you should be able to...

1. Import, clean, and prepare complex datasets from various sources for analysis
2. Apply advanced pandas techniques to manipulate and transform data efficiently
3. Create insightful and professional data visualizations that effectively communicate findings
4. Perform statistical analysis to test hypotheses and validate insights
5. Build and deploy web scraping solutions to collect data from online sources
6. Create and maintain live databases that update with real-time data
7. Develop data pipelines that automatically collect, process, and store information
8. Build, evaluate, and optimize machine learning models for predictive analytics
9. Apply model selection and hyperparameter tuning to improve model performance
10. Create accurate forecasting models for business planning and decision-making
11. Develop complete data analysis workflows from raw data to actionable insights
12. Present findings through interactive dashboards connected to live data sources
13. Apply best practices for reproducible and maintainable data analysis code

What level is the course and do I need any particular skills?

This is an intermediate-level course. Participants should have completed an introductory Python course or have equivalent experience with basic Python programming.

How will I be taught, and will there be any work outside the class?

Course is conducted via face to face and online (Hybrid). The course uses a blend of instructional methods, including:

• Guided hands-on coding exercises in Jupyter notebooks
• Mini-lectures to introduce new concepts and techniques
• Collaborative group projects analyzing real-world datasets
• Code reviews and discussion of best practices
• Independent problem-solving challenges

Are there any other costs? Is there anything I need to bring?

Laptops are optional as all CityLit computers are equipped with all necessary software. There are no additional costs for software as we will use only open-source tools and libraries

When I've finished, what course can I do next?

Please click here to view our Programming and Maths courses.