Data Science with Python (Expert Instructor Recording)

We are looking for an expert in @@@@DATA Science with Python@@@@ to record a training course. Training content will be provided along with slides, lab and good instructor notes. The project involves fixing any errors in the course material and providing very good interactive videos with captions while teaching the course the videos will be 10 to 15 minutes each and we expect 4 to 6 videos for each of the 20 lessons and labs integrated together. ******************* Course Description This beginner-friendly, practical foundation course teaches learners how to execute an end-to-end data science workflow. Students gain hands-on skills in Python, SQL, statistics, data visualization, and Generative AI to solve business problems. Throughout 20 lessons, learners progressively build and package a portfolio-ready business data science solution. Tools Used Programming Languages & Data Manipulation: Python, SQL, NumPy, Pandas Visualization & Dashboards: Matplotlib, Seaborn, Plotly Machine Learning & AI: scikit-learn, Generative AI tools/assistants Environments & Version Control: Jupyter (Notebook/Lab), Relational Databases, Git, GitHub, Cloud Development Environments Lesson Breakdown Lesson 1 — Introduction to Data Science & the Data Science Workflow Overview: Covers the data science lifecycle, analytics types, translating business issues into analytical questions, and project success criteria. Lab: Define the capstone project domain, target audience, business goals, and initial charter. Lesson 2 — Python Fundamentals for Data Science Overview: Introduces basic Python syntax, data types, structures, conditional logic, and loops for data handling. Lab: Write a foundational Python script to define capstone parameters, assumptions, and basic calculations. Lesson 3 — Python Programming for Data Analysis Overview: Focuses on reusable functions, list/dictionary comprehensions, file I/O, exception handling, and custom modules. Lab: Create a reusable Python module containing functions to load data, validate inputs, and handle errors. Lesson 4 — Jupyter, NumPy & the Data Science Ecosystem Overview: Explores interactive Jupyter environments, NumPy array operations, vectorized math, and project file organization. Lab: Initialize the capstone workspace structure, install required dependencies, and build a initial data dictionary. Lesson 5 — Pandas Fundamentals Overview: Teaches Pandas Series/DataFrames, file importing, structural inspection, filtering, sorting, and metric aggregation. Lab: Import the main capstone dataset, profile its structure, calculate initial metrics, and document key attributes. Lesson 6 — Data Cleaning & Transformation with Pandas Overview: Highlights strategies for locating and resolving missing data, duplicates, incorrect data types, and formatting issues. Lab: Build a repeatable Pandas cleaning workflow and export a clean, analysis-ready capstone dataset. Lesson 7 — Combining & Reshaping Data Overview: Covers dataset merging, multi-dimensional GroupBy actions, pivot tables, and reshaping between wide/long formats. Lab: Join the main capstone data with secondary sources to create a unified consolidated analytical dataset. Lesson 8 — Collecting Data from APIs, Web Sources & Files Overview: Explores external data retrieval via APIs, JSON parsing, basic web scraping concepts, and data licensing. Lab: Identify, pull, and integrate an external public or API dataset into the capstone to add business context. Lesson 9 — SQL Fundamentals for Data Science Overview: Teaches relational database structure, standard SQL SELECT queries, basic filtering, ordering, and aggregations. Lab: Load project data into a relational database and execute SQL queries answering five core business questions. Lesson 10 — Advanced SQL for Data Analysis Overview: Covers SQL joins, subqueries, CTEs, CASE logic, and window functions for comparative analysis. Lab: Build an advanced SQL analytical layer combining calculated fields, joins, and window functions. Lesson 11 — Python, SQL & Databases for Data Science Overview: Connects Python to relational databases, moving query results directly into Pandas DataFrames. Lab: Write an automated Python workflow to query the database and pull the required analysis DataFrames. Lesson 12 — Data Visualization with Matplotlib & Seaborn Overview: Guides plot selection to visualize statistical distributions, trends, relationships, and outliers visually. Lab: Generate an EDA visual report answering key project questions and documenting distinct data patterns. Lesson 13 — Interactive Visualization & Dashboards Overview: Focuses on interactive plotting with Plotly and constructing business-oriented dashboards for stakeholders. Lab: Build Dashboard v1 featuring interactive elements, key KPIs, visual trends, and performance metrics. Lesson 14 — Statistics & Probability for Data Science Overview: Covers central tendency, dispersion, probability distributions, sampling variability, bias, and correlation vs. causation. Lab: Calculate descriptive statistics, check for analytical biases, and compile statistical evidence supporting project findings. Lesson 15 — Introduction to Machine Learning with Python Overview: Introduces supervised/unsupervised learning, train/test splits, regression vs. classification, and model evaluation via scikit-learn. Lab: Prepare feature/target data, construct a simple baseline predictive model, and record its performance. Lesson 16 — Generative AI for Data Science Overview: Teaches how to prompt AI for code generation, debugging, and SQL optimization while checking for hallucinations. Lab: Use AI to accelerate three capstone tasks (code, SQL, visual scripts) and log human validation steps. Lesson 17 — AI-Assisted Data Preparation, Analysis & Visualization Overview: Leverages AI assistance to refine data cleaning pipelines, generate deeper EDA queries, and enhance visual code. Lab: Revisit previous capstone steps using AI tools, compare outcomes against originals, and incorporate improvements. Lesson 18 — Data Ethics, Responsible AI & Data Storytelling Overview: Examines privacy, bias, and responsible AI practices alongside methods for crafting compelling business data narratives. Lab: Draft an executive narrative detailing the business problem, methodology, ethical risks, and final recommendations. Lesson 19 — Git, GitHub & Cloud Data Science Environments Overview: Details version control workflows, Git repository management, GitHub README structuring, and cloud environments. Lab: Package all capstone code, notebooks, data instructions, and documentation into a polished GitHub repository. Lesson 20 — End-to-End AI-Driven Data Science Capstone Overview: Focuses on synthesizing the end-to-end workflow, evaluating limitations, and presenting technical results to non-technical teams. Lab: Finalize and deliver the full 13-part AI-Assisted Business Data Science Solution and executive presentation.