Display working knowledge of Excel for Data Analysis.

Perform basic spreadsheet tasks including navigation, data entry, and using formulas.

Employ data quality techniques to import and clean data in Excel.

Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.

Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.

Explain the important role charts play in telling a data-driven story. 

Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.

Build and share interactive dashboards using Excel and Cognos Analytics.

Learn Python - the most popular programming language and for Data Science and Software Development.

Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

Access and web scrape data using APIs and Python libraries like Beautiful Soup.

Play the role of a Data Scientist / Data Analyst working on a real project.

Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.

Apply Python fundamentals, Python data structures, and working with data in Python.

Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

Analyze data within a database using SQL and Python.

Create a relational database and work with multiple tables using DDL commands.

Construct basic to intermediate level SQL queries using DML commands.

Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story

Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble

Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps

Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library

Apply different techniques to collect and wrangle data

Showcase your Data Analysis and Visualization skills

Create a data analysis report and a compelling presentation

Demonstrate proficiency with various Python Libraries

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights

Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.