Elevate Your Excel Skills with Python

In this very hands-on course, specifically designed for non-programmers, discover how Python can supercharge your Excel skills. You ll learn how to clean and organize data like a pro, automate the tedious tasks that eat up your valuable time, and create compelling data stories that impress every audience.

This is the perfect course if:

1. You're an Excel user looking to level up your data analysis skills.
2. You wish to automate repetitive tasks and enhance productivity in your daily workflow.
3. You're interested in creating more dynamic and interactive visualizations than Excel alone can provide.
4. You re a non-programmer, but want to harness the capabilities of a programming language that's easy to learn and very powerful in terms of data handling.
5. You aspire to make data-driven decisions using advanced analysis techniques.
6. You are ready to future-proof your career by adding a highly desirable skill in the job market.

Day 1

1. Introduction to Python: Learn the basic commands and structures in Python, simple enough for anyone to follow.
2. Python and Excel working together: Watch a quick demo of Python enhancing Excel data, transforming it into insightful dashboards (20 min).
3. Tackling Excel Frustrations with Python: Understand how Python can help overcome common Excel challenges such as handling large datasets, dealing with complex formulas, and limitations in data visualization.
4. Combining Python with Excel: Understand how Python can complement Excel for analyzing data, focusing on how Python can enhance Excel's capabilities.
5. Setting up Python on your computer: A simple guide to get Python up and running on your machine.
6. Python's powerful data tool: Learn how to load and transform data from Excel files, explore data more in-depth, and create simple visual representations.
7. Data Cleaning with Python and Excel: Learn how to tidy up your Excel data with Python - remove duplicate data, handle missing entries, and identify unusual data points to ensure accurate analysis.
8. Summarizing and Grouping Excel Data with Python: Learn how to use Python to group and summarize Excel data, and create quick summary tables for faster analysis.
9. Simple Visualization: Learn to use Python's visual tool to create basic graphs from Excel data like line charts, scatter plots, and bar charts.

Day 2

1. Advanced Visualization with Excel Data: Learn how to create interactive graphics using data from Excel files, including heatmaps, box plots, and geographical maps for more in-depth insights. (We'll also talk about how to share these dashboards).
2. Advanced Data Manipulation with Python and Excel: Learn sophisticated ways to work with Excel data in Python, such as combining datasets, working with dates and times, and handling text data for more efficient analysis.
3. Building Interactive Dashboards from Excel Data: Learn how to use Python's visual tool to create interactive dashboards and web-based applications from Excel data to present and share data effectively.
4. Automating Your Analysis Process with Python and Excel: Learn how to use Python to automate your data analysis process in Excel, saving time and boosting productivity. This can be especially helpful for tasks that you perform regularly in Excel.
5. Putting it all together note: the instructor will select use cases from the list below that mostly closely correspond to the job role needs of the students in the class.
   
   • Project Management: Python can automate tracking and updating project schedules. For instance, one can use Python to automatically generate Gantt charts, calculate critical paths, and analyze resource utilization.
   • Budgeting and Forecasting: Python can be used to automate the creation and updating of budgets and forecasts, incorporating historical data and predictive analytics.
   • Expense Tracking: Python can be used to develop custom expense tracking applications where data from various sources can be automatically consolidated, categorized, and analyzed.
   • HR Analytics: Python can be used to analyze employee data to understand trends in hiring, attrition, performance, etc. It can also be used for workforce planning and predictive modeling for attrition and recruitment.
   • Payroll Processing: Python can automate complex payroll calculations and generation of payslips, which could save considerable time and reduce errors.
   • Financial Modeling: Python can be used for more complex financial models that go beyond Excel s capabilities. This could include Monte Carlo simulations, advanced regression models, and optimization models.
   • Inventory Management: Python can be used to automate inventory management, including tracking inventory levels, identifying reorder points, and optimizing inventory carrying costs.
   • Sales Analytics: Python can be used to analyze sales data, identify trends, calculate key performance indicators, and predict future sales.
   • Time Tracking: Python can be used to automate the collection, processing, and analysis of time tracking data, useful for understanding employee productivity, project costing, and more.
   • Report Generation: Python can automate the generation of regular reports, pulling data from multiple sources, processing it, and outputting it in a desired format (such as a PDF or Excel file). The reports can even be automatically emailed to relevant stakeholders.
   • Customer Segmentation: Python can be used to perform more sophisticated customer segmentation than Excel. It can handle larger datasets and use advanced clustering algorithms, such as K-means clustering, to identify distinct groups within the customer base.
   • Sentiment Analysis: Python can help analyze customer reviews or social media mentions for sentiment analysis. This could be used to gauge public opinion about a brand or product, identify potential crises, or find positive testimonials to highlight.
   • Predictive Analytics: Python can be used to predict customer behavior such as likelihood to churn, likelihood to convert, or future purchase behavior. These predictive models can help target marketing efforts more effectively.
   • Marketing Mix Modeling: Python can be used to analyze the effectiveness of different marketing channels and campaigns, and optimize the allocation of marketing spend across these channels.
   • A/B Testing Analysis: While A/B testing can be set up in many marketing software tools, Python can be used to perform more advanced analysis of the results, such as statistical tests to determine the significance of the results.
   • Web Scraping: Python can be used to automatically scrape data from the web for market research or competitive analysis. For example, you could scrape information about competitors' products, prices, and customer reviews.
   • SEO Analysis: Python can automate the process of checking a website s SEO performance, including tracking keyword rankings, checking for broken links, and analyzing backlinks.
   • Email Campaign Analysis: Python can be used to analyze the performance of email marketing campaigns, including tracking open rates, click-through rates, and conversion rates, and identifying trends and opportunities for improvement.
6. Best Practices and Next Steps: Best practices for using Python for data analysis, and resources for continuing to learn and improve to become a more efficient data analyst.

• Data Analysts and Data Scientists: Professionals in these roles often need to work with large volumes of data and conduct complex data manipulations and analysis, tasks that Python is well-equipped for.
• Project Managers: This class can help Project Managers automate repetitive tasks, track project progress more efficiently, and present data in an easily digestible manner.
• Finance and Accounting Professionals: Those involved in financial analysis, budgeting, and forecasting can use Python to handle large financial datasets and automate their data processing tasks.
• Human Resource (HR) Professionals: HR professionals can use Python to analyze employee data, automate report generation, and develop predictive models for employee attrition or recruitment.
• Marketing Professionals: Marketers can benefit from Python's ability to analyze customer data, perform sentiment analysis, and create interactive visualizations for presentations.
• Administrative Assistants: Administrative roles often involve a significant amount of data entry and report generation, tasks that can be automated with Python.
• Business Analysts: Business Analysts can use Python to conduct complex data analyses, create predictive models, and present findings through interactive dashboards.
• Operations Managers: These professionals can use Python to automate inventory management, optimize resource allocation, and analyze production data.
• Research Analysts: Python can be used to handle and analyze large datasets, create complex models, and visualize research results.

This class will also be beneficial for any professionals who regularly work with Excel and are looking for ways to enhance their productivity and data analysis capabilities. Prior programming experience is not a requirement!