Advanced Programming in SQL and Python – the course for business analysts

Advanced Programming in SQL and Python – the course for business analysts – this course aims to equip students with advanced skills in SQL and Python, focusing on their application in business analytics. Through seven mini analytical case studies, students will gain hands-on experience in solving real-world business problems.

Course Structure:

Week 1-2: Introduction and Setup

• Lecture: Overview of SQL and Python in business analytics.
• Lab: Setting up the environment (SQL databases, Python IDEs).
• Project 1: Data Import and Cleaning
  • SQL: Importing data from various sources, cleaning and preprocessing.
  • Python: Using pandas for data cleaning and manipulation.

Week 3-4: Data Exploration and Visualization

• Lecture: Techniques for data exploration and visualization.
• Lab: SQL queries for data exploration, Python libraries for visualization (matplotlib, seaborn).
• Project 2: Exploratory Data Analysis (EDA)
  • SQL: Writing complex queries to explore data.
  • Python: Visualizing data trends and patterns.

Week 5-6: Statistical Analysis

• Lecture: Statistical methods for business analytics.
• Lab: SQL functions for statistical analysis, Python libraries (numpy, scipy).
• Project 3: Statistical Analysis of Sales Data
  • SQL: Calculating statistical measures (mean, median, standard deviation).
  • Python: Performing hypothesis testing and regression analysis.

Week 7-8: Predictive Modeling

• Lecture: Introduction to predictive modeling techniques.
• Lab: SQL for data preparation, Python for model building (scikit-learn).
• Project 4: Predictive Sales Forecasting
  • SQL: Preparing data for modeling.
  • Python: Building and evaluating predictive models.

Week 9-10: Time Series Analysis

• Lecture: Time series analysis and forecasting.
• Lab: SQL for time series data manipulation, Python libraries (statsmodels).
• Project 5: Time Series Forecasting
  • SQL: Extracting and transforming time series data.
  • Python: Building time series models and forecasting.

Week 11-12: Machine Learning Integration

• Lecture: Integrating machine learning with SQL databases.
• Lab: SQL for data storage and retrieval, Python for machine learning (TensorFlow, Keras).
• Project 6: Customer Segmentation
  • SQL: Storing and retrieving data for machine learning.
  • Python: Building and deploying machine learning models.

Week 13-14: Advanced Topics and Final Project

• Lecture: Advanced topics in SQL and Python (optimization, big data).
• Lab: SQL performance tuning, Python for big data (PySpark).
• Project 7: Comprehensive Business Analytics Project
  • SQL: Optimizing queries for large datasets.
  • Python: Analyzing and visualizing large datasets.

Assessment:

• Projects: Each mini project will be assessed based on accuracy, efficiency, and creativity.
• Final Project: A comprehensive project integrating all learned skills.

Resources:

• Books: “SQL for Data Analytics” by Upom Malik, Matt Goldwasser, and Benjamin Johnston; “Python for Data Analysis” by Wes McKinney.
• Online Tutorials: SQLZoo, DataCamp, Coursera.

Advanced Programming in SQL and Python

Advanced Programming in Data Analysis

https://datascience.umcs.pl