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AI (KI) Development, KI Manager Weiterbildung V.6


Content
  • Introduction to AI + Pyhton
  • Session 1 - Introduction to AI + Pyhton Basic Syntax
  • Session 1
  • 0-Introduction to AI.pptx
  • 1_0_Python_basic_syntax
  • Module 1 Learning Objectives
  • Lesson 1: Introduction to Programming & Python
  • Lesson 2: Python Reserved Words & Syntax
  • Lesson 3: Variables & Data Storage
  • Lesson 4: Compilers vs. Interpreters
  • Lesson 5: Common Programming Errors
  • Lesson 6: Comments in Python
  • Test1-Basic Intro
  • Session 2 Communication skills - Day1
  • Session 2
  • Pre-assessment
  • Communication skills - Day1.pptx
  • Post-assessment
  • Assignment - Building a Personal Professional Narrative
  • Communication skills Vedio
  • Communication and Soft Skills Lec1.pptx
  • Session 3 Introduction to Python
  • Session 3
  • Module 2: Variables and Basic Operations in Python
  • Lesson 1: Understanding Variables and Data Types
  • Lesson 2: Working with Variables
  • Lesson 3: Expressions, Statements, and Operations
  • Lesson 4: Getting User Input
  • Lesson 5: String Operations
  • Lesson 6: Comments in Python
  • Lesson 7: Writing Clean Code
  • Session 4 Advanced Communication Skills - Day 2
  • Session 4
  • Communication Skills Pre assessment - Day2
  • Advanced Communication Skills - Day 2.pptx
  • Communication Skills post assessment - Day2
  • Communication in Diverse Teams
  • assessment
  • Communication and Soft Skills Lec2 (1).pptx
  • Test
  • Session 5 - Operators and Control flow
  • Session 5
  • Module Title: Control Flow & Conditional Execution in Python
  • 1. Introduction to Boolean Expressions
  • 2. Logical Operators
  • 3. Conditional Statements in Python
  • 4. Chained Conditionals
  • 5. Nested Conditionals
  • 6. Catching Exceptions with try and except
  • 7. Glossary
  • 8. Exercises
  • Session 6 - Data structures + functions
  • Session 6
  • Module Objectives
  • 1. Data Structures in Python - 1.1 Tuples
  • 1.2 Lists
  • 1.3 Dictionaries
  • 1.4 Sets
  • 2. Functions in Python
  • 2.3 Built-in Functions
  • 2.4 Random Number Generation
  • 3. List, Set, and Dictionary Comprehensions
  • 3.1 List Comprehensions
  • 3.2 Set Comprehensions
  • 3.3 Dictionary Comprehensions
  • 4. Nested List Comprehensions
  • Exercises
  • Session 7 Time Management and Productivity
  • Session 7
  • Time Management and Productivity-Pre assessment
  • Time Management Lec1.pptx
  • To-do list:
  • Time Management Video
  • The most famous time management methodologies
  • Session 8 - Modules & Packages and File handling
  • Session 8
  • Module Learning Objectives:
  • Lesson 1: Modules & Packages
  • Lesson 2: File Handling in Python
  • Exercises
  • Session 9 Time Management and Productivity
  • Session 9
  • Time Management and Productivity
  • Which productivity method is right for you?
  • Time management Video
  • Time Management Lec2
  • Time Management and Productivity
  • Session 10 - OOP - UNDER REVIEW
  • Session 10
  • Session 11 - Operators in Python
  • Session 11
  • Lesson 1: Introduction to Operators and Basic Arithmetic Operations
  • Lesson 2: Order of Operations and Modulus Operator
  • Lesson 3: Strings, User Input, Boolean Expressions, and Logical Operators
  • Exercises
  • Session 12 Agile
  • Session 12
  • Agile Lecture 1.pptx
  • Agile Post assessment
  • Agile Lec1 Assignment
  • What is Agile?
  • Agile Manifesto
  • Session 13 - Control Flow
  • Session 13
  • Python Basics assignment
  • Control Flow in Python : Session Objectives
  • Lesson 1: Boolean Expressions and Comparison Operators
  • Lesson 2: Logical Operators and Conditional Statements
  • Lesson 3: Nested Conditionals and Exception Handling
  • Exercises
  • Session 14 Agile
  • Session 14
  • Agile Pre assessment Day2
  • Agile Lecture 2.pptx
  • Activity: Create a Product Backlog
  • Scrum Methodology | Agile Scrum Framework
  • Session Objectives
  • What is Scrum?
  • Session 15 - Data Structures
  • Session 15
  • Data Structures Objectives
  • Lesson 1: Tuples and Lists
  • Lesson 2: Dictionaries
  • Lesson 3: Sets and Comprehensions
  • Exercises
  • Session 16 - Functions
  • Session 16
  • Agile Pre assessment
  • Lesson 1: Introduction to Functions
  • Lesson 2: Built-in Functions
  • Lesson 3: Defining Your Own Functions
  • Glossary
  • Exercises
  • Session 17 Agile (Scrum) Lecture 2
  • Session 17
  • Agile Lecture 2 .pptx
  • Agile Post assessment Day2
  • Activity Template: Add estimation
  • Scrum
  • scrum-guide
  • Session 18 - Modules & Packages
  • Session 18
  • Session Objectives
  • Lesson 1: Introduction to Modules & Packages
  • Lesson 2: Understanding Python Modules
  • Lesson 3: Creating & Using Your Own Module
  • Session Exercises
  • Session 19
  • Session 19
  • Session Objectives
  • Lesson 1: Introduction to File Handling
  • Lesson 2: File Modes and Writing to Files
  • Lesson 3: File Methods and Unicode Handling
  • Exercises
  • Session 20 Agile
  • Session 20
  • Agile Pre assessment Day3
  • Agile Lecture 3.pptx
  • Agile Post assessment Day3
  • Practical Activity: Applying Kanban Concepts to a Mini-Project
  • Agile PM with Kanban
  • what-is-kanban
  • Session 21
  • Session 21
  • Module Learning Objectives
  • Lesson 1: Introduction to Objects
  • Lesson 2: Classes and Objects
  • Lesson 3: Constructors and Destructors
  • Lesson 4: Inheritance in OOP
  • Glossary
  • Session 22
  • Session 22
  • Agile Pre assessment Day4
  • Agile Lecture 4.pptx
  • Agile Post assessment Day4
  • Assignment
  • Agile QA
  • Agile QA Process: Principles, Steps, and Best Practices
  • Session 23 - What’s Statistics & its importance + Types of statistics
  • Session 23
  • Objectives
  • Lesson 1: Introduction to Statistics
  • Lesson 2: Importance of Statistics
  • Lesson 3: Types of Statistics
  • Test - Statistics & Its Importance
  • Session 24 - Descriptive statistics + Inferential statistics
  • Session 24
  • Objectives
  • Lesson 1: Descriptive Statistics
  • Lesson 2: Inferential Statistics
  • Test - Descriptive Statistics + Inferential Statistics
  • Session 25
  • Session 25
  • Agile Pre assessment Day5
  • Agile Lecture 5.pptx
  • Agile Post assessment Day5
  • Applied Task
  • Lean
  • 10 lean agile principles that transform your business
  • Session 26
  • Session 26
  • Python Project One: Control Flow Assignment
  • Session 27
  • Session 27
  • Agile Pre assessment Day6
  • Agile Lecture 6.pptx
  • Agile Project Management
  • A beginner's guide to agile estimation and planning
  • Session 28
  • Session 28
  • Python Task 2: List Assignment
  • Session 29
  • Session 29
  • python Task 3: Tuple Assignment
  • Session 30
  • Agile Lecture 6.pptx
  • Agile Estimating and Planning
  • Agile Estimating and Planning.pdf
  • Session 30
  • Session 31
  • Session 31
  • python Task 4: Sets Assignment
  • Session 32
  • Session 32
  • Agile Lecture 6.pptx
  • Agile Post assessment Day6
  • Agile Test 2
  • Agile Estimating and Planning.pdf
  • Agile Estimating and Planning
  • Developing a New Feature for a Mobile Application
  • Session 33
  • Session 33
  • Python Task 5: Dictionaries Assignment
  • Session 34
  • Session 34
  • Python Projects 6-Packages Assignment
  • Session 35 Project Management Lecture 1
  • Session 35
  • Project Management – Pre Assessment
  • Project Management Lecture 1.pptx
  • Project Management – Post Assessment
  • Planning a University Graduation Ceremony
  • Project Management Fundamentals
  • Project Management
  • Section 2 - Probability and Statistics
  • Session 36 Basic of Probability and Statistics
  • Session 36
  • Python
  • Python Task 7: File Handling Assignment
  • Basic.pptx
  • Session 37 Histogram & Box Plot
  • Session 37
  • Histogram & Box Plot.pptx
  • Objectives
  • 1- What is Statistics?
  • 2-Importance of Statistics
  • 3-Descriptive Statistics
  • Session Test: Descriptive Statistics + Inferential Statistics
  • Session 38 Project Management Lecture 2
  • Session 38
  • Pre-Assessment – Project Management Day2
  • Project Management Lecture 2.pptx
  • Post-Assessment – Project Management Day2
  • Full Project Planning Simulation – Organize a University Career Fair
  • How to Make a Project Management Plan (Complete)
  • What is project planning? How to write a successful project plan
  • Session 39
  • Session 39
  • Objectives
  • Lesson 1: Variables
  • Introduction to Variables in Statistics
  • Lesson 2: Random Variables
  • Random Variables
  • Lesson 3: Percentiles and Quartiles
  • Box Plots
  • Box Plots and Quartiles
  • Lesson 4: Box Plot and Histogram
  • Histograms
  • Statistics in AI – Quiz
  • Session 40
  • Session 40
  • Pre-Assessment – Project Management Day3
  • Project Management Lecture 3.pptx
  • Post-Assessment – Project Management Day3
  • Plan and Schedule a Startup Product Launch
  • Resource Management
  • What is project planning? How to write a successful project plan
  • How to Write a Winning Project Plan with Templates & Examples
  • Session 41 -Probability and Statistics in AI
  • Session 41
  • Statistics
  • Objectives
  • Lesson 1: Five-Number Summary
  • Lesson 2: Correlation & Covariance
  • Correlation Explained Visually
  • Covariance Intuition
  • Lesson 3: Introduction to Probability
  • Probability Basics
  • Lesson 4: Probability Distributions
  • Normal Distribution Intro
  • Distributions Explained Simply
  • Bell Curve and Real-world Applications
  • Z-Score Tutorial
  • Exercise: Probability and Statistics in AI
  • Session 42
  • Session 42
  • Pre-Assessment – Project Management Day4
  • Project Management Lecture 4.pptx
  • Post-Assessment – Project Management Day4
  • Practical_Activity_Risk_Management
  • The Ultimate Guide to Project Risk Management
  • Risk_QA_Reading_Resources
  • Section 3 - Data Analysis with Python
  • Session 43
  • Session 43
  • Session Objectives
  • Lesson 1: Introduction to NumPy
  • Lesson 2: Understanding Data Structures and Creating Arrays
  • Lesson 3: Array Manipulation and Operations
  • Summary
  • NumPy Part 1 – MCQ Test
  • Session 44
  • Session 44
  • Pre-Assessment – Project Management Day5
  • Project Management Lecture 5.pptx
  • Post-Assessment – Project Management Day5
  • Project Management Test
  • assignment - Project Management
  • Project Management
  • Project Documents, Project Management, Reporting, Planning, Risk Management
  • Session 45
  • Session 45
  • NumPy
  • Session Objectives
  • Lesson 1: Array Broadcasting and Computation
  • Lesson 2: Boolean Arrays, Masks, and Logic
  • Lesson 3: Fancy Indexing and Advanced Selection
  • Lesson 4: Sorting and Structured Arrays
  • Lesson 5: Structured Arrays and Preparation for Pandas
  • Session 46
  • Session 46
  • Assignment_NumPy
  • Pandas
  • Learning Objectives
  • Lesson 1: Introducing Pandas Objects
  • Lesson 2: The Pandas DataFrame Object
  • Lesson 3: Indexing and Selection
  • Lesson 4: Operations in Pandas
  • Lesson 6: Hierarchical Indexing
  • Pandas part1
  • Session 47
  • Session 47
  • Pre-Assessment – Design Thinking Day1
  • Design Thinking Lecture 1.pptx
  • Post-Assessment – Design Thinking Day1
  • Designing a Better Learning Experience for a New Skill
  • Design Thinking video
  • Design Thinking (DT)
  • Session 48 - Pandas Part 2: Handling Missing Data & MultiIndexing in Data Analys
  • Session 48
  • Learning Objectives
  • Lesson 1: Handling Missing Data (Null Values)
  • Lesson 2: Hierarchical Indexing in Pandas
  • Lesson 3: Combining Data with Concatenation
  • Lesson 4: Merging and Joining DataFrames
  • Lesson 5: Working with Duplicate Indices
  • Session 49
  • Session 49
  • Pre-Assessment – Design Thinking Lecture 2
  • Design Thinking Lecture 2.pptx
  • Post-Assessment – Design Thinking Lecture 2
  • Practical Activity – Design Thinking Lecture 2
  • Design Thinking
  • What is Design Thinking and Why Is It So Popular?
  • Stage 2 in the Design Thinking Process: Define the Problem
  • Session 50
  • Session 50
  • Pre-Assessment – Design Thinking Lecture 3
  • Design Thinking Lecture 3.pptx
  • Post-Assessment – Design Thinking Lecture 3
  • Practical Activity – Design Thinking Lecture 3
  • Design Thinking Define Stage
  • Stage 2 in the Design Thinking Define the Problem and Interpret theResults
  • How to Cluster Your Ideas and Reveal Insights
  • Session 51
  • Session 51
  • Ecommerce Purchases
  • Session 52
  • Session 52
  • Lesson 1: Aggregation and Basic Data Analysis
  • Lesson 2: Grouping Data with groupby()
  • Lesson 3: Working with Strings in Pandas
  • data analysis.pdf
  • Session 53
  • Session 53
  • Matplotlib
  • Lesson 1: Introduction, Basic Plotting & Figure Layouts
  • Lesson 2: Customization, Annotations, Legends, and Exporting
  • Lesson 3: Ticks, Layout Adjustments, Advanced Legends
  • Session 54
  • Session 54
  • Pre-Assessment – Design Thinking Lecture 4
  • Design Thinking Lecture 4.pptx
  • Post-Assessment – Design Thinking Lecture 4
  • Practical Activity – Design Thinking Lecture 4 (Ideation Stage)
  • Design Thinking Ideation
  • What Is Ideation in Design Thinking? 2025 Ideation Techniques Guide
  • Stage 3 in the Design Thinking Process: Ideate
  • Session 55
  • Session 55
  • Lesson 1: Seaborn Basics, Distributions, and Custom Styling
  • Lesson 2: Advanced Categorical, Correlation, and Grid Plots
  • Lesson 3: Seaborn Heatmaps, Jointplots, and Custom Facets
  • Session 56
  • Session 56
  • Seaborn
  • Lesson 1: Getting to Know the Data
  • Lesson 2: Univariate Analysis (Single Feature)
  • Lesson 3: Bivariate and Multivariate Analysis
  • Lesson 4: Feature Engineering & Encoding
  •  Lesson 5: Outliers, Duplicates, and Scaling
  • Lesson 6: Advanced Visualizations for EDA
  • Session 57
  • Session 57
  • Session 57 Support
  • Pre-Assessment – Design Thinking Lecture 5 (Prototyping)
  • Design Thinking Lecture 5
  • Post-Assessment – Design Thinking Lecture 5 (Prototyping)
  • Practical Activity – Design Thinking Lecture 5 (Prototyping Stage)
  • Prototyping Video unit
  • What Is Design Thinking? A Comprehensive Beginner's Guide
  • Design Thinking Phase 4 you Need to Know About Prototyping
  • Session 58
  • Session 58
  • Task_seaborn
  • EDA Session 2: Deep Feature Exploration & Automated Insights
  • Lesson 1: Feature Relationships & Transformations
  • Lesson 2: Visual Diagnostics & Correlation Analysis
  • Lesson 3: Automation & Feature Diagnostics
  • Assignment unit
  • Session 59
  • Session 59
  • Pre-Assessment – Design Thinking Lecture 6 (Testing Stage)
  • Design Thinking Lecture 6
  • Post-Assessment – Design Thinking Lecture 6 (Testing Stage)
  • Design Thinking Test
  • Practical Activity – Design Thinking Lecture 6 (Testing Stage)
  • Design Thinking - Stage 5: Test
  • Design Thinking – All 5 Stages Overview
  • Session 60
  • Session 60
  • Linear_Regression
  • Entry-Level AI Lab: Exploratory Data Analysis (EDA) on Titanic Dataset
  • Lesson 1 : Introduction to Machine Learning
  • Lesson 2: Build Your First Machine Learning Model
  • Lesson 3: Evaluating and Improving Machine Learning Models
  • Session 61
  • Session 61
  • Pre-Assessment Creative Thinking - Day 1
  • Creative Thinking Lecture 1
  • Post-Assessment Creative Thinking - Day 1
  • Challenge in the Tech Sector
  • Creative Thinking: How To Think Outside The Box
  • What is creative thinking and how can I improve?
Completion rules
  • All units must be completed
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