Programming for Data Science

Many analytics teams can run reports but struggle to move past spreadsheets into repeatable, code-driven analysis. Without shared Python practices, work gets rebuilt by hand and AI prototypes can drift from production expectations. This course gives IT Directors and Training Managers a structured path from command-line basics to production-aware data work.

Role fit: Built for data analysts and aspiring data scientists. The instructor also shows when to skip ahead. Practitioners who already code can bypass the CLI material and start at the Anaconda setup. Team Leads can use this to standardize environments — including Conda, Jupyter, and Git/GitHub — across the group.

Time investment: About 21 hours across 26 skills of roughly 45–60 minutes each. Most skills end with a challenge so learners confirm the concepts before moving on. A realistic pace is 2–3 skills per week over two to three months.

Risk and governance: The OpenAI and Streamlit modules help teams distinguish app-building and MVP work from production-aware development. That makes the course useful for teams that are experimenting with internal AI tools and need shared expectations before moving beyond prototypes.

Change management: Python and R are taught side by side, so mixed-language teams can align on shared EDA workflows. Several skills also stand alone in the catalog. Managers can build targeted Playlists, such as Python and Pandas for analysts or the OpenAI skills for an AI pilot team, without assigning the full course.

IT teams complete this training to turn ad hoc spreadsheet work into repeatable, code-driven analysis that a Team Lead can review, reuse, and hand off. The course grounds each skill in practical data science tasks. Learners build projects from the command line inside Conda environments, then build a custom exploratory data analysis (EDA) class in Jupyter Notebook. That class adds dimensions, summary statistics, and histograms, so analysts stop rewriting the same code for every new dataset.

• Less repetitive, error-prone work: Your team writes reusable functions and loops instead of copying the same lines of code dozens of times. This shortens turnaround on recurring reports.
• Consistent, shareable environments: Analysts set up Anaconda environments with Pandas, NumPy, and Jupyter, then use consistent project structures that make work easier to review, reuse, and hand off. Onboarding gets faster and "works on my machine" problems become rarer.
• Broader data preparation and clearer insights: Practitioners wrangle data with Pandas and present findings with Matplotlib and Seaborn. Practitioners also run EDA in both Python and R.
• Responsible AI prototyping: Team members query OpenAI's API from Google Colab and build a Streamlit web app MVP. The training draws a clear line between non-production code and production code, which keeps API keys out of public repositories, and it covers matching the model to the task to control cost and efficiency.

The result for an IT Director is a more self-sufficient analytics team that makes better performance decisions, informed by data structures and Big O concepts.

Knowledge

• How Python data types, strings, sequences, slicing, and indexing underpin work with lists, dictionaries, and other collections
• Where NumPy, Pandas, Matplotlib, and Seaborn fit in the Python data science library stack, and how they build on one another
• Which core data structures (including graphs for relationship data) fit which analysis tasks, and how time complexity, space complexity, and Big O notation affect performance
• How R differs from Python as a statistical language, including primitive and non-primitive data types used in data exploration
• How AI language models and the OpenAI API work, including matching the right model to the task for efficiency and resource control
• Why API keys must stay out of public code, and the difference between non-production MVP code and production deployment

Ability

• Use the command line, Conda virtual environments, Jupyter Notebooks, and Git/GitHub repositories to set up repeatable data science projects
• Write reusable functions, loops, and object-oriented code, including a custom EDA class that reports dimensions, statistics, and histograms
• Wrangle data with Pandas and NumPy, then visualize results with Matplotlib and Seaborn
• Collect web data with Python and BeautifulSoup after learning the HTML and CSS basics behind web scraping
• Perform exploratory data analysis in both R and Python
• Query OpenAI language models from Google Colab and build AI-powered web app prototypes