Programming for Data Science

Data teams often lose time when analysis work depends on one-off scripts, inconsistent environments, or manual data cleanup. This associate-level course gives IT Directors and Training Managers a structured way to standardize programming fundamentals for data analysts and data scientists with roughly 3–5 years of data science experience.

The time investment is about 21.5 hours per learner, covering command-line use, development environment setup, Python and R fundamentals, reusable functions, automation with loops, Pandas, NumPy, visualization, web scraping, Git/GitHub, complexity analysis, and introductory AI app workflows with OpenAI and Streamlit. For change management, Team Leads can assign this as a baseline path before advanced analytics or machine learning projects so practitioners share common tooling and code practices.

CBT Nuggets helps support rollout with Playlists for structured assignment and Team Reporting to track progress across the team.

IT teams complete this training to make data work more repeatable, reviewable, and easier to operationalize across analysts and data scientists. The course maps to practical tasks teams face when building internal reporting, exploratory analysis, and prototype data apps.

• Standardize analyst environments: Practitioners learn command-line access and development environment setup so onboarding is less dependent on tribal knowledge.
• Reduce manual data prep: Teams build skills with Python functions, loops, built-in methods, Pandas, and NumPy to clean, transform, and analyze data more consistently.
• Improve stakeholder reporting: Matplotlib and Seaborn lessons help teams turn analysis into visual outputs that are easier for business partners to interpret.
• Support prototype workflows: Git/GitHub, BeautifulSoup web data collection, OpenAI API querying in Colab, and Streamlit app creation help teams move from ad hoc analysis toward shareable prototypes.

Knowledge

• Core data science domains, roles, and how programming supports data workflows
• Command-line basics and development environment setup for data science work
• Python data types, strings, sequences, math operators, functions, loops, methods, and OOP concepts
• Pandas, NumPy, Matplotlib, and Seaborn fundamentals for wrangling and visualization
• Web scraping concepts, Git/GitHub repositories, data structures, memory, complexity, and Big O notation
• R fundamentals, EDA with R and Python, and introductory OpenAI/Streamlit workflows

Ability

• Write reusable Python code to automate common data tasks
• Clean, reshape, and analyze datasets with Pandas and NumPy
• Create visualizations that communicate patterns and findings
• Collect web data with Python and BeautifulSoup
• Compare data structures and reason about performance using complexity concepts
• Query language model APIs and build a basic AI web app with Streamlit