Occupational Certificate: Artificial Intelligence Software Developer

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About Course

 

Curriculum Document

 

Curriculum Code

Curriculum Title

 

251201002

Occupational Certificate: Artificial Intelligence Software Developer

 

 

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Email

Phone

 

Development Quality Partner

MICT SETA

Gugu.Sema@mict. org.za

011-2072600

 

MICTSETA

SECTION 1: CURRICULUM SUMMARY
Occupational Information

Curriculum Information
4 Part Qualification Curriculum Structure
None

SECTION 2: OCCUPATIONAL PROFILE
Occupational Purpose
Occupational Tasks
Occupational Task Details

SECTION 3: CURRICULUM COMPONENT SPECIFICATIONS
SECTION 3A: KNOWLEDGE MODULE SPECIFICATIONS
251201-002-00-KM-01, Overview of Artificial Intelligence, NQF Level 4, Credits 2
251201-002-00-KM-02, Introduction to Mathematics and Statistics, NQF Level 4, Credits 10
251201-002-00-KM-03, Analytical Thinking and Problem Solving, NQF Level 4, Credits 3
251201-002-00-KM-04, Data, Databases and Data Visualisation, NQF Level 4, Credits 8
251201-002-00-KM-05, Computing Theory, NQF Level 4, Credits 8
251201-002-00-KM-06, Introduction to Artificial Intelligence, Machine Learning, Deep Learning, NQF Level 4, Credits 5
251201-002-00-KM-07, Artificial Intelligence, NQF Level 5, Credits 12
251201-002-00-KM-08, Machine Learning, NQF Level 5, Credits 16
251201-002-00-KM-09, Deep Learning, NQF Level 5, Credits 16
251201-002-00-KM-10, Introduction to Governance, Legislation and Ethics, NQF Level 4, Credits 1

251201-002-00-KM-11, Fundamentals of Design Thinking and Innovation, NQF Level 4, Credits 1 12 251201-002-00-KM-12, 4IR and Future Skills, NQF Level 4, Credits 4
SECTION 3B: PRACTICAL SKILLS MODULE SPECIFICATIONS
251201-002-00-PM-01, Mathematics and Statistics for Programming, NQF Level 4, Credits 8

251201-002-00-PM-02, Problem Definition, Analytical Thinking and Decision Making, NQF Level 4, Credits 2

251201-002-00-PM-03, Access, Analyse and Visualise Structured Data Using Spreadsheets, NQF Level 4, Credits 4

251201-002-00-PM-04, Use SQL to Communicate with a Database, NQF Level 5, Credits 4

251201-002-00-PM-05, Build a simple AI solution using Python, NQF Level 5, Credits 8

251201-002-00-PM-06, Use Python Data Scraping to Populate Database Table in SQL, NQF Level 5, Credits 4

251201-002-00-PM-07, Use Machine Learning to Build an AI solution in Python, NQF Level 5, Credits 6 89

251201-002-00-PM-08, Use Deep Learning to Build an AI Neural Network Architecture in Python, NQF Level 5, Credits 10

251201-002-00-PM-09, Use Deep Learning to Build an AI Neural Network Architecture in TensorFlow, NQF Level 5, Credits 10

251201-002-00-PM-10, Function Ethically and Effectively as a Member of a Multidisciplinary Team, NQF Level 4, Credits 3

251201-002-00-PM-11, Participate in a Design Thinking for Innovation Workshop, NQF Level 4, Credits 4

SECTION 3C: WORK EXPERIENCE MODULE SPECIFICATIONS
251201-002-00-WM-01, AI Solution Design Interpretation and Development, NQF Level 5, Credits 20

251201-002-00-WM-02, AI Solution Performance Testing, NQF Level 5, Credits 20
251201-002-00-WM-03, AI Solution Deployment, Modification and Improvement, NQF Level 5, Credits 20

STATEMENT OF WORK EXPERIENCE

 

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Course Content

SECTION 1: KM-01-KT01: Introduction to AI
PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of artificial Intelligence, its definition and future, as well as the purpose and contribution of AI to society and business   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE: The learning will enable learners to demonstrate an understanding of: - KM-01-KT01: Introduction to AI - KM-01-KT02: Background to AI - KM-01-KT03: Strategic advantage of AI in business

  • KT0101 Evolution of Artificial Intelligence (AI)
  • KT0102 Defining AI
  • KT0103 Realistic and unrealistic AI
  • KT0104 Fields related to AI:
  • KT0105 Taxonomy of AI:
  • KT0106 Strong vs weak AI
  • KT0107 Why is AI important
  • KT0108 Limitation of AI

SECTION 2: KM-01-KT02: Background to AI
KT0201AI applications: Common application types

SECTION 3: KM-01-KT03: Strategic advantage of AI in business
Learning Outcome - KT0301 Introduction to the 4th Industrial Revolution (4IR)

Introduction to Mathematics and Statistics Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-02 NQF Level 4 Notional hours 100 Credit(s) 10 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to revise and acquire mathematical and statistical theory to successfully understand and interpret actions of Artificial Intelligence, Machine Learning, Deep Learning and Data Analytics   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE: -KM-02-KT01: Basic Mathematics -KM-02-KT02: Linear Algebra -KM-02-KT03: Conversion between decimal and binary systems -KM-02-KT04: Expressing size and magnitude -KM-02-KT05: Error in calculations -KM-02-KT06: Cartesian coordinate system -KM-02-KT07: Pythagorean theorem -KM-02-KT08: Increments -KM-02-KT09: Calculus -KM-02-KT10: Probabilities -KM-02-KT11: Statistics - KM-02-KT12: Bayes’ Theorem

SECTION 2: KM-02-KT02: Linear Algebra
Learning Outcome  KT0201 Linear transformation  KT0202 Vectors: • Vectors • Customary behavioural vectors • Eigen vectors  KT0203 Matrices: • Matrices • Inverse and transports • Special matrices  KT0204 Matrix operations  KT0205 Special functions in linear algebra: • ReLU • Sigmoid • SoftMax • Popular loss functions • Cross-entropy • Quadratic loss functions

SECTION 3: KM-02-KT03 :Conversion between decimal and binary systems
SECTION 3: KM-02-KT03 :Conversion between decimal and binary systems

SECTION 4: KM-02-KT04: Expressing size and magnitude
Learning Outcome  KT0401 Use scientific notation for small and large numbers  KT0402 Prefixes: Giga to Pica (109 to 10 -12) Conversions  KT0403 SI to Imperial  KT0404 Degrees F to degrees C

SECTION 5: KM-02-KT05:Error in calculations
Learning Outcome  KT0501 Rational and irrational numbers  KT0502 Explore repeating decimals and convert them to fraction  KT0503 Symbols for irrational numbers  KT0504 Rounding prematurely in calculations  KT0505 Accuracy in calculations  KT0506 Final value of a calculation expressed in terms of the required unit  KT0507 When PEMDAS fail

SECTION 6: KM-02-KT06: Cartesian coordinate system
Learning Outcome  KT0601 Definition  KT0602 Terminology  KT0603 The coordinate plane: Intersecting x- and y-axes Four quadrants  KT0604 Naming using Roman numerals  KT0605 Use an application to create graphs and maps

SECTION 7: KM-02-KT07:Pythagorean theorem
SECTION 7: KM-02-KT07:Pythagorean theorem

SECTION 8: KM-02-KT08:Increments
Learning Outcome  KT0801 Definition and terminology  KT0802 Purpose and use of increments  KT0803 Increment a variable  KT0804 Compound assignment operator  KT0805 Increments in programming

SECTION 9: KM-02-KT09: Calculus
Learning Outcome  KT0901 Calculus essentials: Differential calculus Integral calculus  KT0902 Derivatives: Derivative and partial derivatives Chain rule Derivatives of special functions

SECTION 10: KM-02-KT10: Probabilities
Learning Outcome  KT1001 Definition and terminology  KT1002 Probability essentials  KT1003 Probability basics and notations  KT1004 Probabilities and odds  KT1005 Probability: Why probability matters Odds KT1006 The Bayes rule: Essential probability theorem How odds change Bayes rule in practice Naïve Bayes classification  KT1007 What are parameters  KT1008 Estimating parameters  KT1009 Conditional probability  KT1010 Essential probability theorems for ML

SECTION 11: KM-02-KT11: Statistics
Learning Outcome  KT1101 Why is statistics important in AI?  KT1102 Introduction to statistics: Descriptive statistics Inferential statistics  KT1103 Qualitative and quantitative research: Definitions Application  KT1104 Statistics and machine learning: Statistics in data preparation Outlier detection Missing value imputation Data sampling Data scaling Variable encoding Statistics in model evaluation Data sampling Data resampling Experimental design Statistics in model selection Checking for a significant difference between results Quantifying the size of the difference between results Statistics in model prediction Summarizing the expected skill of the model on average Quantifying the expected variability of the skill of the model in practice Statistics in model presentation o Quantifying the expected variability for the prediction  KT1105 Gaussian distribution and descriptive statistics: Mean Variance Standard deviation  KT1106 Correlation between variables: Positive correlation Neutral correlation Negative correlation  KT1107 Statistical Hypothesis Tests: Hypothesis 0 (H0) Hypothesis 1 (H1)  KT1108 Estimation statistics: Classes of methods Effect size Interval estimation Meta-analysis  KT1109 Types of intervals: Tolerance interval Confidence interval Prediction interval  KT1110 Nonparametric statistics

SECTION 12: KM-02-KT12:Bayes’ Theorem
Bayes’ Theorem of conditional probability Naming the terms in the theorem Example for calculating Bayes’ Theorem Diagnostic test scenario Manual calculation Python code calculation Binary classifier terminology Bayes’ Theorem for modelling Hypotheses Bayes’ Theorem for classification Bayes’ Classifier Bayes’ Optimal Classifier More uses of Bayes’ Theorem in Machine Learning Bayesian optimisation Bayesian belief networks

Analytical Thinking and Problem Solving
Module # 251201-002-00-KM-03 NQF Level 4 Notional hours 30 Credit(s) 3 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to provide the learner with an opportunity to acquire theory for formulating a problem and applying knowledge to design and create a solution for such a problem PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. QCTO/ MICT SETA requirements Human Resource Requirements: Lecturer/learner ratio of 1:20 (Maximum) Qualification of lecturer (SME): NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry AI vendor certification (where applicable) Assessors and moderators: accredited by the MICT SETA Legal Requirements: Legal (product) licences to use the software for learning and training (where applicable) OHS compliance certificate Ethical clearance (where necessary) Exemptions No exemptions, but the module can be achieved in full through a normal RPL process TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-03-KT01 : Introduction to analytical thinking  KM-03-KT02 : Problem solving and critical thinking  KM-03-KT03 : AI problem solving

SECTION 2: KM-03-KT02: Problem solving and critical thinkin
Learning Outcome  KT0201Root cause analysis (RCA): What is RCA? RCA Steps Define the event Identify the problem – 5 Why’s Establish a probable cause/s Find the root cause Test to determine the cause Establish a plan to resolve the problem Implement a solution Verify the functionality Implement preventative measures Document results Advantages and disadvantages  KT0202Decision tree analysis What are decision trees? Terminology used Steps Advantages and disadvantages

SECTION 3: KM-03-KT03:AI problem solving
Learning Outcome  KT0301 Formulate a real-world problem  KT0302 Search data  KT0303 Solve problems with AI  KT0304 Formulate a simple game tree: What is a game tree Minimize and maximize Strategy The value of the root node Minimax principle

251201002 – Data, Databases and Data Visualisation
PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of data and databases and giving meaning to data through data processing, analysis and visualisation PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. QCTO/ MICT SETA requirements Human Resource Requirements: Lecturer/learner ratio of 1:20 (Maximum) Qualification of lecturer (SME): NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry AI vendor certification (where applicable) Assessors and moderators: accredited by the MICT SETA Legal Requirements: Legal (product) licences to use the software for learning and training (where applicable) OHS compliance certificate Ethical clearance (where necessary) Exemptions No exemptions, but the module can be achieved in full through a normal RPL process TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-04-KT01 : Introduction to data  KM-04-KT02 : Data in spreadsheets  KM-04-KT03 : Data analytics  KM-04-KT04 : Introduction to databases  KM-04-KT05 : Data mining  KM-04-KT06 : Structured query language (SQL)  KM-04-KT07 : Visualising data with AI tools  KM-04-KT08 : Data security

SECTION 2: KM-04-KT02:Data and spreadsheets
Learning Outcome  KT0201 Use spreadsheets to analyse and visualise data: • Reporting using spreadsheets Create a spreadsheet report Filter and format data Create charts • Spreadsheets tables • Create a spreadsheet table • Summarize data • Sort, filter, and validate data • Format summarized data • Pivot tables and pivot charts Use pivot tables and pivot charts Import data from a csv file Create a pivot table Edit pivot tables and pivot charts How to set up pivot tables How to interpret data obtained from a pivot table and communicate it • Dashboards Create spreadsheet dashboards Conduct data analysis in spreadsheet pivot tables Arrange tables and charts Slice data Filter data using a slicer Add calculated columns to a dashboard Find anomalies • Hierarchies and time data Create a hierarchy Configure time data Create an animated time chart • The spreadsheets data model Explore an spreadsheet data model Add multiple tables Create relationships Add external data Import external data and use it Link out to external data DAX View data within an spreadsheet table • Importing data from files Pre-formatting and importing csv files Import data into spreadsheet Shape and transform data Load data • Importing data from databases Import data into spreadsheets from a SQL server database Identify available data sources Preview, shape, and transform data Table relationships and hierarchies Loading data • Importing Data from Spreadsheet Reports Import data from Spreadsheet reports Transform Spreadsheet report data • Creating and Formatting Measures Create and format measures DAX Measures Advanced DAX Functions Use some of the advanced functions within DAX • Visualizing Data in Spreadsheets Pivot charts Cube functions Charts for cube functions Create and refine a pivot chart Describe cube functions and when to use them Describe a number of charts for use with cube functions.

SECTION 3: KM-04-KT03:Data analytics
Learning Outcome  KT0301 Types of data: Relational databases Data warehouses Advanced DB and information repositories Object-oriented and object-relational databases Transactional and Spatial databases Heterogeneous and legacy databases Multimedia and streaming databases Text databases Text mining and Web mining  KT0302 Operators  KT0303 Conditional statements: Loops Script Functions Probability  KT0304 Inference and modelling

SECTION 4: KM-04-KT04:Introduction to databases
Learning Outcome  KT0401 What is a Database:  Definition of a database  Components of a database  Function of a database  Types of databases  Characteristics of a good database  Structure and challenges  Database design tools  KT0402 Data Storage: Characteristics of quality data Quality traits of data Data reliability Best practices Data collection and warehousing Sources and collection systems Data capturing systems and processes Parameters for data capturing systems and processes Maintenance of data capturing systems and processes Automated data collection Limits of data acquisition  KT0403 Relational database design: Design a rational database Create a rational database Modify a relational database  KT0404 Import and export data  KT0405 Design and create queries  KT0406 Data driven solutions

SECTION 5: KM-04-KT05 : Data mining
Learning Outcome KT0501 What is data mining? KT0502 Data mining implementation process: Understanding business Understanding data Data preparation Data transformation Modelling KT0503 Data mining techniques: Classification Clustering Regression Association rule Outer detection Sequential pattern Prediction KT0504 Challenges of data mine implementations KT0505 Data mining tools: R language Oracle data mining KT0505 Advantages and disadvantages of data mining KT0506 Application of data mining

SECTION 6: KM-04-KT06 : Structured query language (SQL)
Learning Outcome KT0601 SQL programming language KT0602 SQL code constructs to perform database transactions KT0603 Storing, retrieving, managing or manipulating the data inside a relational database management system (RDBMS) KT0604 The application of SQL is explained

SECTION 7: KM-04-KT07 : Visualising data with AI tools
KT0701 Introduction to data visualization: Data visualization using graphics ggplot2 in R language Data visualization using AI tools TensorFlow Graph Visualiser MS Azure ML Studio

SECTION 8: KM-04-KT08:Data security
Learning Outcome KT0801 Definition KT0802 Purpose of protecting data KT0803 Process for protecting data KT0804 Unauthorised access

Computing Theory Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-05 NQF Level 4 Notional hours 80 Credit(s) 8 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of programming as creating a set of instructions for a computer on how to perform a task, using coding and programming languages   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE: KM-05-KT01 : Introduction to programming languages KM-05-KT02 : Introduction to algorithms KM-05-KT03 : Programming basics KM-05-KT04 : Solution development KM-05-KT05 : Introduction to Python

SECTION 1: KM-05-KT01: Introduction to programming languages
Learning Outcome  KT0101 Concepts, principles and terminology  KT0102 Developing structured and creative thinking skills through programming  KT0103 The logic of programming  KT0104 Types of programming languages: • Procedural Programming • Functional Programming • Object-oriented Programming • Scripting Programming • Logic Programming  KT0105 Choosing a programming language  KT0106 Top 5 AI programming languages: • Python • C++ • Java • LISP • Prolog

SECTION 2: KM-05-KT02 : Introduction to algorithms
KT0201 Advantages and disadvantages of AI programming languages: Definition and concept The role and function of algorithms Different types of algorithms (theories) Algorithms and Data Structures Algorithms and Problem Solving Algorithm development Planning and branching Adversarial games Mathematical formulas in texts Tools for representing algorithms Tracing and interpreting algorithms Problem solving steps Use of local search and heuristics Machine learning Deep learning Neural Process Learning

SECTION 3: KM-05-KT03 : Programming basics
Learning Outcome KT0301 Programming environment KT0302 Algorithms KT0303 Data types KT0304 Variables KT0305 Keywords KT0306 Logical and arithmetical operators KT0307 Logical operations: if-statements, where-statements, If-else conditions KT0308 Loops KT0309 Numbers, characters and arrays KT0310 Functions KT0311 Input and output operations KT0301 Programming environment

SECTION 4: KM-05-KT04 : Solution development
Learning Outcome KT0401 Software development principles: Solution development Design tools and techniques Process flow and cycle Extracting information User interface concepts and design (usability, functionality) KT0402 Computational thinking: Sequencing Selection Looping Simple data structures Objects

SECTION 5: KM-05-KT05 : Introduction to Python
Learning Outcome   KT0501 Python programming KT0502 Installing Python: Programming basics Native data types Python classes Inheritance concepts Magic functions Special functions in Python       KT0503 Python Programming: Array and array manipulation Selecting data Slicing Stacking Splitting arrays Key Functions

Module 6 Introduction to ArtificialIntelligence, Machine Learning, Deep Learning, Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-06 NQF Level 4 Notional hours 50 Credit(s) 5 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of the relationship between Artificial Intelligence, Machine Learning and Deep Learning, as well as the application of such systems to create a set of instructions to perform a programming task   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-06-KT01 : Artificial Intelligence (AI) vs Machine Learning (ML) vs Deep Learning (DL)

Module # 251201-002-00-KM-07 NQF Level 5 Notional hours 120 Credit(s) 12 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-07 NQF Level 5 Notional hours 120 Credit(s) 12 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of the relationship between Artificial Intelligence, Machine Learning and Deep Learning, as well as the application of AI to create a set of instructions to perform a programming task   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-07-KT01 : AI frameworks  KM-07-KT02 : Using AI for data scraping

SECTION 2: KM-07-KT02: Using AI for data scraping
• Learning Outcome • KT0201 Concept and definition • KT0202 Purpose of data scraping • KT0203 Data scraping tools • KT0204 Legal issues • KT0205 Web scraping procedure: Find the URL to scrape Inspect the page  Find the data you want to extract Write the code Run the code and extract the data Store the data in the required format • KT0206 Libraries used for web scraping

Module 8 – Machine Learning Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-08 NQF Level 5 Notional hours 160 Credit(s) 16 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of the relationship between Artificial Intelligence, Machine Learning and Deep Learning, as well as the application of ML to create a set of instructions to perform a programming task   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-08-KT01 : Types of ML models  KM-08-KT02 : ML algorithm classification  KM-08-KT03 : Common ML algorithms  KM-08-KT04 : ML Workflow Process (Framework)  KM-08-KT05 : Business benefits of ML

SECTION 2: KM-08-KT02:ML algorithm classification
Learning Outcome  KT0201 Supervised Learning  KT0202 Unsupervised Learning  KT0203 Reinforcement Learning

SECTION 3: KM-08-KT03: Common ML algorithms
1. Learning Outcome 2. KT0301 Supervised Learning: 3. Linear Regression 4. Naive Bayes 5. Decision Trees 6. Nearest neighbour 7. Super Vector Machines SVM) 8. Neural Networks 9. KT0302 Unsupervised Learning: 10. K-means clustering 11. Association rule 12. KT0303 Reinforcement Learning: 13. Labels 14. No labels 15. KT0304 Reinforcement Learning 16. Q-learning 17. Temporal difference (TD) 18. Deep adversarial networks

SECTION 4: KM-08-KT04:ML Workflow Process (Framework)
• Learning Outcome • KT0401 Data Collection • KT0402 Data Preparation • KT0403 Choose a Model • KT0404 Train the Model • KT0405 Evaluate the Model • KT0406 Parameter Tuning • KT0407 Make Predictions

SECTION 5: KM-08-KT05 : Business benefits of ML 20%
• Learning Outcome • KT0501 Real-time business decision making • KT0502 Eliminating manual tasks • KT0503 Enhancing security and network performance • KT0504 Improved business models and services • KT0505 Reducing operating expense • KT0506 Other

Module 09 – Deep Learning Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-09 NQF Level 5 Notional hours 160 Credit(s) 16 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of the relationship between Artificial Intelligence, Machine Learning and Deep Learning, as well as the application of Deep Learning to create a set of instructions to perform a programming task using a Deep Learning tool   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-09-KT01 : Deep Learning (DL)  KM-09-KT02 : Advance Python for Deep Learning  KM-09-KT03 : TensorFlow and Keras for Deep Learning

SECTION 1: KM-09-KT01:Deep Learning (DL)
• Learning Outcome • KT0101 DL neural network architecture: • What are neural networks and layers? • Neural network types • KT0102 DL fundamental network architectures: • Convolution neural networks • Recurrent neural networks • Recursive neural networks • KT0103 Input and output nodes • KT0104 Activation functions in DL: • Types of activation functions • Sigmoid function • Hyperbolic Tangent function (Tanh) • Softmax function • Softsign function • Rectified Linear Unit (ReLU) function • Exponential Linear Units (ELUs) function • KT0105 Activation function in TensorFlow • KT0106 Building a simple DL Network • KT0107 Tuning a DL Network

SECTION 2: KM-09-KT02: Advance Python for Deep Learning
Learning Outcome KT0201Python programming primer: • Decorators and special functions • Decorators’ implementation with class • Context manager ‘with’ in Python • Context manager application • Exception Handling • Try and Catch block • Python package management • Bundling and export python packages

SECTION 3: KM-09-KT03: TensorFlow and Keras for Deep Learning
Learning Outcome KT0301TensorFlow 2.0 Basics:  TensorFlow core concepts, Tensors, core APIs  Concrete Functions, Data Types, Control Statements  Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy,Pandas  Autograph eager execution function autograph implementation KT0302Keras (TensorFlow 2.0 Built-in API) Overview: • Sequential models • Configuring layers • Loading data • Train and test • Complex models • Callbacks • Save and restore • Neural network weights • Building neural networks in Keras • Building neural networks from scratch in Keras

Module 10 – , Introduction to Governance, Legislation and Ethics Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-10 NQF Level 4 Notional hours 10 Credit(s) 1 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of the various legislations governing the workplace and their implication for the employer and employees. The learning of this module will also enable the learner to acquire an understanding of the principles of areas of performance management, business planning concepts, costing of products and concepts of general ethical behaviour and its impact in the workplace   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-10-KT01 : Governance  KM-10-KT02 : Legislation governing workplaces  KM-10-KT03 : Introduction to ethics and security  KM-10-KT04 : Ethics at work  KM-10-KT05 : Security  KM-10-KT06 : Performance management  KM-10-KT07 : Business planning  KM-10-KT08 : Costing of products  KM-10-KT09 : Resources

SECTION 1: KM-10-KT01:Governance
Learning Outcome • KT0101 Definitions and the role of governance • KT0102 Rules, norms and actions that are structured, sustained, regulated and held accountable • KT0103 Structures and processes and industry codes of practice • KT0104 Transparent, participatory, inclusive and responsive • KT0105 Domains of IT governance • KT0106 Compliance vs non-compliance

SECTION 2: KM-10-KT02:Legislation governing workplaces
Learning Outcome • KT0201 LRA • KT0202 POPI • KT0203 B-BBEE • KT0204 BCEA • KT0205 SDA • KT0206 Current trends

SECTION 3: KM-10-KT03:Introduction to ethics and security
Learning Outcome • KT0301 Principles and practices • KT0302 Concepts, definitions and terminology

SECTION 4: KM-10-KT04: Ethics at work
Learning Outcome • KT0401 Code of conduct and moral compass • KT0402 Components of ethical behaviour, including integrity, honesty, fair dealing and respecting diversity • KT0403 Unwritten but expected behaviours, including reliability, accountability, time keeping and respect for others • KT0404 Lapses in ethical behaviour, including sexual harassment, racism, bullying, theft, abuse of company property, rules, time and sick leave • KT0405 Conflicts of interest, including primary and secondary interests, the impact on individuals and organisations and the link to corruption • KT0406 The need for ethical behaviour and the impact or consequences of lapses in ethical behaviour • KT0407 Copyright and plagiarism • KT0408 Intellectual property • KT0409 Spamming • KT0410 Contract management • KT0411 Software licensing • KT0412 Pricing

SECTION 5: KM-10-KT05 : Security
Learning Outcome • KT0501 Risks, threats and vulnerabilities • KT0502 Mitigation tools and strategies • KT0503 Digital forensics • KT0504 Cloud • KT0505 Commercial law • KT0506 Cyber security • KT0507 New trends

SECTION 6: KM-10-KT06 : Performance management
Learning Outcome • KT0601 Planning, organising and control • KT0602 Work flow • KT0603 Cost, waste • KT0604 Productivity, efficiency • KT0605 Housekeeping • KT0606 Risk, health, safety, environment and related systems • KT0607 Quality and quality systems • KT0608 Continual improvement

SECTION 7: KM-10-KT07 : Business planning
Learning Outcome • KT0701 Business sustainability • KT0702 Concept of supply and demand • KT0703 Concept of profit, loss and breakeven • KT0704 Accountability and responsibility • KT0705 Competition • KT0706 Customers • KT0707 Contracts • KT0708 Budgets

SECTION 8: KM-10-KT08 : Costing of products
Learning Outcome • KT0801 Input cost • KT0802 Overhead costs • KT0803 Direct labour cost • KT0804 Pricing a product (Under- or over-pricing)

SECTION 9: KM-10-KT09: Resources
Learning Outcome  KT0901 Human resources  KT0902 Financial resources  KT0903 Physical resources (infrastructure, machinery, equipment)  KT0904 Communication and information technology

Module 11 – , Fundamentals of Design Thinking and Innovation
Module # 251201-002-00-KM-11 NQF Level 4 Notional hours 10 Credit(s) 1 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of the design thinking principles and applications in the workplace   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-11-KT01 : Introduction to design thinking  KM-11-KT02 : The human element  KM-11-KT03 : Creativity  KM-11-KT04 : Innovation  KM-11-KT05 : Design  KM-11-KT06 : Design thinking methodology  KM-11-KT07 : Application of design thinking

SECTION 1: 11-KT01: Introduction to design thinking
Learning Outcome • KT0101 Philosophy • KT0102 Approach and concepts • KT0103 Definitions and terminology • KT0104 History

SECTION 2: KM-11-KT02:The human element
Learning Outcome • KT0201 Human centeredness • KT0202 Human participation

SECTION 3: KM-11-KT03: Creativity
Learning Outcome • KT0301 Creativity: is unleashing the potential of the mind to conceive new ideas • KT0302 Perceiving the world in new ways • KT0303 Find hidden patterns • KT0304 Make connections between seemingly unrelated phenomena • KT0305 Generate solutions • KT0306 Application in the workplace

SECTION 4: KM-11-KT04: Innovation
Learning Outcome • KT0401 Innovation: is the action of putting things into practical reality, despite challenges and resistance • KT0402 Different innovations: • Incremental • Disruptive • Architectural and • Radical • KT0403 Main types of innovation: • Process innovation • Product innovation • Organisational innovation • Market innovation • KT0404 What innovation means to business

SECTION 5: KM-11-KT05 : Design
Learning Outcome • KT0501 Think outside the box • KT0502 Push beyond the obvious solutions • KT0503 Communication through shape and form

SECTION 6: KM-11-KT06 : Design thinking methodology
Learning Outcome • KT0601 Design thinking phases • KT0602 Design thinking tools and techniques

SECTION 7: KM-11-KT07 : Application of design thinking
Learning Outcome • KT0701 Application in software development • KT0702 Application in cyber security • KT0703 Business innovation • KT0704 Innovative problem solving

Module 12 – 4IR and Future Skills Occupational Certificate: Artificial Intelligence Software Developer
Module # 251201-002-00-KM-12 NQF Level 4 Notional hours 40 Credit(s) 4 Curriculum Code 251201002 Qualification Title Occupational Certificate: Artificial Intelligence Software Developer PURPOSE OF THE KNOWLEDGE MODULE The main focus of the learning in this knowledge module is to build an understanding of the impact of 4IR on communities, individuals and businesses as well as important skills for future needs   PROVIDER ACCREDITATION REQUIREMENTS FOR THE MODULE: Physical Requirements: • The provider must have lesson plans and structured learning material or provide learners with access to structured learning material that addresses all the topics in all the knowledge modules as well as the applied knowledge in the practical skills. • QCTO/ MICT SETA requirements Human Resource Requirements: • Lecturer/learner ratio of 1:20 (Maximum) • Qualification of lecturer (SME): • NQF 6 in industry recognised qualifications with 1 year’s experience in the IT industry • AI vendor certification (where applicable) • Assessors and moderators: accredited by the MICT SETA Legal Requirements: • Legal (product) licences to use the software for learning and training (where applicable) • OHS compliance certificate • Ethical clearance (where necessary) Exemptions • No exemptions, but the module can be achieved in full through a normal RPL process   TOPIC ELEMENTS TO BE COVERED INCLUDE:  KM-12-KT01 : 4 IR emerging trends  KM-12-KT02 : Computing Knowledge  KM-12-KT03 : Future skills and competencies (4IR)  KM-12-KT04 : 4 IR trends affecting businesses  KM-12-KT05 : Interpersonal skills  KM-12-KT06 : Intrapersonal skills  KM-12-KT07 : Communication principles and methods  KM-12-KT08 : Written business communication  KM-12-KT09 : Presentation skills  KM-12-KT10 : Teamwork in the workplace  KM-12-KT11 : Committees and meetings  KM-12-KT12 : Job descriptions and profiles  KM-12-KT13 : Customers and stakeholders  KM-12-KT14 : Customer service

SECTION 1: KM-12-KT01:4 IR emerging trends
Learning Outcome  KT0101 Artificial intelligence  KT0102 Cloud computing  KT0103 Cyber security  KT0104 Data science  KT0105 Internet of things  KT0106 Quality engineering automation  KT0107 Robotic processing automation  KT0108 Software programming  KT0109 Design thinking and innovation  KT0110 e-Waste

SECTION 2: KM-12-KT02 : Computing Knowledge
Learning Outcome  KT0201 Introduction to programming language  KT0202 Programming basics  KT0203 Basic programming knowledge on HTML, JavaScript (or any scripting language)  KT0204 Software development, e.g., C#, C++, Java, .NET  KT0205 Databases (SQL or NoSQL)  KT0206 Web development technologies

SECTION 3: KM-12-KT03 : Future skills and competencies (4IR)
earning Outcome  KT0301 Disruptive thinking (encourage this) (application to their own environment)  KT0302 Continuously searching for ideas  KT0303 Thinking innovatively (analyse the current market and come up with solutions to the current problems)  KT0304 Soft skills  KT0305 Programming languages  KT0306 Operating systems  KT0307 Open source  KT0308 Tools for a cloud environment (for configuration and management), tools for debugging, login and monitoring and tools for image  KT0309 Familiarity with Office tools  KT0310 Leadership and people management skills

SECTION 4: KM-12-KT04 : 4 IR trends affecting businesses
Learning Outcome  KT0401 Afro-centric approach to African problems – taking the best from the existing products and coming up with own solutions - Continental challenges and opportunities  KT0402 Using Google, Amazon and MS forms and tools to reduce development time (e.g., embed AI APIs)  KT0403 Business intelligence applications and availability of Big Data (collecting data, converting data into information and turning information into knowledge, knowledge into intelligence and intelligence into wisdom)  KT0404 Collecting data on clients  KT0405 Insight into different markets  KT0406 Automated factories  KT0407 Exposure to the global world

SECTION 5: KM-12-KT05 : Interpersonal skills
Learning Outcome  KT0501 Concept, definition and terminology  KT0502 Principles  KT0503 Attributes: Social intelligence Confidentiality Conflict handling and resolution Decision making Defending vs attacking Problem solving, Troubleshooting Respect Roles, responsibilities Thinking about the end-user

SECTION 6: KM-12-KT06 : Intrapersonal skills
Learning Outcome  KT0601 Concept, definition and terminology  KT0602 Principles  KT0603 Attributes: Adaptability Agility Analytical thinking Cognitive thinking skills Emotional maturity Flexibility Planning Problem solving Reflection Research and investigate Self-management Strong attention to detail Time-management Resilience

SECTION 7: KM-12-KT07 : Communication principles and methods
Learning Outcome  KT0701 Concept, definition and terminology  KT0702 The different types and forms of communication and communication processes  KT0703 Communication methods  KT0704 Barriers to communication  KT0705 Communication network: Interdepartmental, Supply chain network, etc.  KT0706 Advantages of good communication  KT0707 Consequences of poor/no communication

SECTION 8: KM-12-KT08 : Written business communication
Learning Outcome  KT0801 Business requirement specifications  KT0802 Types  KT0803 Conventions  KT0804 Schedules  KT0805 Reports, reporting protocols and methods  KT0806 Manuals and guidelines  KT0807 Work instructions/briefs  KT0808 Technical report writing  KT0809 Extracting information from written texts  KT0810 Policies aligned to standard (IEEE 829-2008 standards)

SECTION 9: KM-12-KT09 : Presentation skills
Learning Outcome  KT0901 Concept, definition and terminology  KT0902 Types: visual, verbal, written  KT0903 Conventions  KT0904 Presenting options and solutions  KT0905 Presenting technical details  KT0906 Visualisation of business intelligence  KT0907 Suitable APIs and storytelling using the right tools to: present frame the story focus on certain aspects pitch clear terms pictorial

SECTION 10: KM-12-KT10 : Teamwork in the workplace
Learning Outcome  KT1001 Concept, definition and terminology  KT1002 Principles of teamwork  KT1003 Advantages of teamwork  KT1004 Team composition and members  KT1005 Roles, responsibilities and functions  KT1006 Team dynamics  KT1007 Common goals and collaboration  KT1008 Nature of multidisciplinary teams and teamwork  KT1009 Setting and achieving targets  KT1010 Collaboration tools (electronic)

SECTION 11: KM-12-KT11 : Committees and meetings
Learning Outcome  KT1101 Procedures  KT1102 Agendas and minutes  KT1103 Roles and responsibilities  KT1104 WSP committees  KT1105 EE committees  KT1106 Safety and health committees  KT1107 Wellness committees

SECTION 12: KM-12-KT12 : Job descriptions and profiles
Learning Outcome  KT1201 Purpose  KT1202 Job and person specification  KT1203 Content  KT1204 Alignment to performance standards

SECTION 13: KM-12-KT13 : Customers and stakeholders
Learning Outcome  KT1301 Concept, definition and terminology  KT1302 Types of customers  KT1303 Customer profile  KT1304 Typical customer behaviour: including habits and mannerisms  KT1305 Difficult customers

SECTION 14: KM-12-KT14 : Customer service
Learning Outcome  KT1401 Concept, definition and terminology  KT1402 Customer service principles  KT1403 Customer centeredness  KT1404 Handover and sign-off procedures and techniques  KT1405 Technical documentation  KT1406 Training in the use of the system

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