Track
Software Development
Duration
120 hours
Skill Level
Foundation
Language
English
About this Course
Learn Python basics and apply them to data science, machine learning, and AI projects.
Learning Mode: Learn at ALC or at Home
Detailed Course Curriculum
Hands-on module breakdown aligned with MKCL production standards and industry requirements.
- What is Python?
- History of Python
- Versions of Python
- Features of Python
- Limitations of Python
- Scripting Languages vs Programming Languages
- Applications of Python
- Python2 vs Python 3
- What is Python used for?
- Flavours of Python
- Python compared to other Languages
- Python vs Java
- How Python works?
- What is PVM?
- Compiler vs Interpreter
- Compile Time vs Run Time
- Future Scope of Python and Career Opportunities
- What is Memory Management?
- Memory Management in various Programming Languages
- Memory vs Storage
- Three Areas of Memory Management
- How important is Memory Management?
- Memory Management
- Memory management in Python
- Allocator Domain
- Allocation Domains in detail
- Python Memory Manager
- The Default Python Implementation C Python
- GIL
- Python Memory Allocation
- Garbage Collection
- Ways to make an object eligible for Garbage Collection
- Reference Counting in Python
- Cyclical Reference or Reference Cycle
- Generational Garbage Collection
- C Python Memory Management
- Common Ways to reduce the Space Complexity
- Python Installation on Windows
- Adding Python to Environmental Variable
- Checking Python Version on Windows
- Verifying Pip Installation
- What are IDE and IDLE Editors?
- How to run Python Program using IDLE?
- IDE’s Installation
- How to install Visual Studio?
- How to install Visual Studio?
- Thony installation
- Thony installation
- Executing Python Program
- Identifiers and rules to Write Identifiers
- Constants, Variables and Literals
- Keywords or Reserved Keywords
- Python Comments
- Python comments
- Python comments
- Benefits of using Python comments
- Benefits of using Python comments
- Python Syntax
- Lines and Indentation
- Python User Input
- Data Types in Python
- Text Data Type
- Text Data Type
- Numeric Types
- Numeric Types
- Sequence Type
- Sequence Type
- Mapping Types
- Mapping Types
- Set Types
- Set Types
- Boolean Types
- Boolean Types
- Binary Types
- Binary Types
- None Type
- None Type
- Type Casting
- Type Casting
- Operators in Python
- Arithmetic Operators
- Arithmetic Operators
- Assignment Operators
- Assignment Operators
- Comparison Operators
- Comparison Operators
- Logical Operators
- Logical Operators
- Identity Operators
- Identity Operators
- Membership Operators
- Membership Operators
- Bitwise Operators
- Bitwise Operators
- Precedence and Associativity of Operators
- Precedence and Associativity of Operators
- Ternary Operator
- Ternary Operator
- What are Control Flow Statements in Python?
- Decision Control Statements
- Simple if
- If else
- Nested If
- If elif else
- Elif ladder
- Short hand if ,if else
- Multiple Conditions using and or Operator
- Transfer Statements
- Break
- Break
- Continue
- Continue
- Pass
- Pass
- Iterative statements
- For
- For
- While
- While
- Nested For loop
- Nested For loop
- Pattern Programs
- Data Types in brief
- How to access String and Indexing?
- String Slicing
- Mutable and Immutable
- Mathematical Operators for String (+,*)
- Comparison of String
- String Membership
- Format String
- Escape Character
- Removing Spaces from String
- Finding Substring
- Counting Substring and Len()
- Replacing a String
- Splitting and Joining of String
- Changing Case of a String
- Checking tarting and ending part of the String
- Methods to check type of Characters present in String
- List and its Creation
- Accessing Elements of List
- List Mutability
- List Traversing
- Functions of List
- Manipulating List
- append()
- append()
- insert()
- insert()
- extend()
- extend()
- remove()
- remove()
- Ordering Elements of List
- Ordering Elements of List
- Alaising and Cloning of List Object
- Use of Mathematical Operators for List
- Comparision and Membership Operators
- Nested List
- List Comprehension
- What is Tuple
- Creating a Tuple
- Accessing through Tuple
- Tuple Methods
- Mathematical and Membership Operators
- Iterating through Tuple
- Updating Tuple
- Nesting of Tuple
- Tuple Comprehension
- Unpack Tuple
- Difference between Tuple and List
- Zipping of Tuples
- Creating Sets
- Modifying Sets
- Removing Elements from set
- Python set Operation
- Set Method
- Built in Functions
- Set Comprehension
- Frozen Sets
- How to create Dictionary
- Accessing Dictionary
- Update Dictionary
- Delete the elements from dictionary
- Python dictionary methods
- Membership and iterating through in dictionary
- Important functions in dictionary pt1
- Dictionary Comprehension
- Nested Dictionary
- Built in Functions
- User Defined Function
- Docstrings
- Calling a function in python
- Types of Arguements
- Variable Length Arugements
- Scope of variables
- Types of Variables
- Recursive Functions
- Namespaces
- Nested Functions
- Benifits of functions
- Anonymous function
- Lambda Function in detail
- filter,map and reduce function
- map()
- reduce()
- properties of function
- Decorators
- Chaining Decorators
- Magic Method
- Iterables
- Iterator and Iterations
- Yield Keyword
- Generators
- Iterators vs Generators
- Python Generator expression
- What is module?
- How to create a module ?
- Possibilities of Import
- Built in Modulles
- Finding Members of module
- The Special Variable Name
- Packages
- Library
- Commonly used libraries
- Random Module
- Math Module
- PIL
- Movie Py Module
- PyScreenShot Module
- Date Class
- Time Class
- Date Time Class
- Time Delta class
- Open File
- Properties of Files
- Read & Write Operation
- Seek and tell method
- OS Module
- Working with Directories
- Handling binary data
- CSV Files
- Zip and Unzip
- Types of Errors
- Exception
- Exception Handling Hierarchy
- Customized Exception Handling
- Control Flow in Try and Except
- Multiple Exceptions
- Default Exception
- Finally Block
- Control Flow in try except and finally
- Else with try except finally
- Types of Exception
- Assert Keyword
- Python Loggers
- What is log and log file in programmin?
- What is log and log file in programmin?
- Levels of log messages
- Levels of log messages
- Using basicConfig
- Using basicConfig
- Formatting
- Formatting
- Classes and functions
- Classes and functions
- Logging Handlers
- Logging Handlers
- Stream Handlers
- Stream Handlers
- File Handlers
- File Handlers
- Working with Handlers
- Working with Handlers
- Exception Information
- Exception Information
- JSON
- Json
- Json
- JSON Syntax
- JSON Syntax
- Datatypes in JSON
- Datatypes in JSON
- Read ,Write and Parse JSON
- Read ,Write and Parse JSON
- Python Object Conversions
- Python Object Conversions
- Python to JSON
- Python to JSON
- Formatting the results
- Formatting the results
- Serializing
- Serializing
- Parse JSON
- Parse JSON
- Deserialize
- Deserialize
- Pickling & Unpickling
- Pickling & Unpickling
- What is Reg ex
- Character Classes
- Quantifiers
- Functions of Re-Module
- Find all methods _ Important functions of re module
- Symbols
- Web scrapping using reg exp
- Multitasking
- Difference between Multiprocessing and multi-threading
- Difference between Process and Thread
- Ways of creating thread in python
- Difference in program with and without Multithreading
- Thread Identification Number
- Functions/Methods in Multithreading
- Daemon Thread
- Synchornization
- Diffference between lock and semaphore
- Thread Communication
- Inter Thread Communicatio
- Concurrency and parallelism
- Race Condition and DeadLock
- Collection Modules
- Counters
- Ordered dict
- default dict
- chain map
- Named Map
- DeQue
- User Dict
- UserList
- User String
- Object Oriented vs Procedural Oriented
- What is Class?
- What is Object ?
- Constructor
- Self Keyword
- Functions vs Method
- Types of Variables
- Static variable
- Local Variable
- Instance Variable
- Class Method
- Static Method
- Inner Class
- Garbage Collection in OOP’s
- Destructor
- Inheritance
- Inheritance
- Inheritance
- Built in function in oops
- Built in function in oops
- Single Inheritance
- Single Inheritance
- Constructor super()
- Constructor super()
- Multiple inheritance
- Multiple inheritance
- Method Resolution Order (MRO)
- Method Resolution Order (MRO)
- Multilevel Inheritance
- Multilevel Inheritance
- Hirarchical Inheritance
- Hirarchical Inheritance
- Hybrid Inheritance
- Hybrid Inheritance
- Polymorphism
- Polymorphism
- Polymorphism
- Polymorphism with class methods
- Polymorphism with class methods
- Polymorphism with functions and objects
- Polymorphism with functions and objects
- Overloading
- Overloading
- Operator Overloading
- Operator Overloading
- Magic Method for operator overloading
- Magic Method for operator overloading
- Method Overloading
- Method Overloading
- Constructor Overloading
- Constructor Overloading
- Method Overriding
- Method Overriding
- Method overriding with multiple and multilevel inheritance
- Method overriding with multiple and multilevel inheritance
- Method overriding with multiple and multilevel inheritance
- Method overriding with multiple and multilevel inheritance
- Constructor Overriding
- Constructor Overriding
- Type System
- Type System
- Duck Typing
- Duck Typing
- Abstraction
- Types of Methods in Python
- How to declare an abstract method in Python
- Concrete Methods in Abstract Base Classes
- Missed Abstract methods in implementation
- Abstact classes contain more subclasses?
- Different cases for Abstract class object creation
- Built in Abstract Classes
- Interfaces
- Create a Python Interface
- Python Interfaces vs Abstract Class
- Encapsulation
- Python Access Modifiers
- Why we need Encapsulation
- Calculator
- Password Generator
- Tic Tac Toe
- Rock Paper Scissors
- Chat Bot
- BMI Calculator
- Story Generator
- Quiz Game
- Create Acronyms
- Intro
- Introduction to Tkinter
- Widgets in Tkinter
- Tkinter Geometry
- Python Tkinter Button
- Python Tkinter Canvas
- Python Tkinter CheckButton
- Python Tkinter Entry
- Python Tkinter Frame
- Python Tkinter Label
- Python Tkinter Listbox
- Python Tkinter MenuButton
- Python Tkinter Menu
- Tkinter Project Calendar
- Intro
- Python Tkinter Message
- Python Tkinter RadioButton
- Python Tkinter Scale
- Python Tkinter Scrollbar
- Python Tkinter Text
- Python Tkinter Toplevel
- Python Tkinter SpinBox
- Python Tkinter Paned Window
- Python Tkinter Label Frame
- Python Tkinter MessageBox
- Python GUI PyQt5- Part 1 intro
- PyQt5 Introduction
- Modules and tools
- PyQt5 First Program
- PyQt5 First Program
- PyQt5 Layouts
- QVBoxLayout and QHBoxLayout
- QVBoxLayout and QHBoxLayout
- QGridLayout
- QGridLayout
- QFormLayout
- QFormLayout
- QStackedLayout
- QStackedLayout
- Signals and slots
- PyQt5 Widgets
- QLabel
- QLabel
- QLineEdit
- QLineEdit
- QPushButton
- QPushButton
- QRadioButton
- QRadioButton
- QCheckBox
- QCheckBox
- QComboBox
- QComboBox
- QSpinBox
- QSpinBox
- QSlider
- QSlider
- QMenuBar, QMenu & QAction
- QMenuBar, QMenu & QAction
- QToolBar
- QToolBar
- QInputDialog
- QInputDialog
- QFontDialog
- QFontDialog
- QFileDialog
- QFileDialog
- QTab
- QTab
- QStacked
- QStacked
- QSplitter
- QSplitter
- QDock
- QDock
- QStatusBar
- QStatusBar
- QList
- QList
- QScrollBar
- QScrollBar
- QCalendar
- QCalendar
- Python GUI PyQt5- Part 2 intro
- Qmessagebox
- Multiple document interface
- Drag and Drop
- Drawing API
- Clipboard
- BrushStyle Constants- Part 1
- BrushStyle Constants- Part 2
- QPixmap Class
- Database handling
- Project 1- Text Editor
- Project 2- Calculator
- Python Turtle intro
- Introduction to Python Turtle
- Moving and Drawing with turtle I
- Moving and Drawing with turtle II
- First Turtle Program
- Turtle program on pen control I
- Turtle program on pen control II
- Program- Event handling
- Program on working state of the turtle module
- Working with turtle screen 1
- Working with turtle screen 2
- Program Colorfull Star Pattern
- Turtle Methods
- Program - Draw a hut using turtle module
- Pygame intro
- Pygame Introdution
- Basic structure of a Pygame program
- Basic Pygame concepts
- Pygame - Display Modes
- Pygame - Color Object
- Pygame - Event Objects
- Keyboard Events
- Mouse Events
- Pygame - Drawing Shapes
- Pygame - Using Image
- Pygame - Displaying Text
- Pygame - Moving an Image
- Pygame - Use Text as Buttons
- Pygame - Transforming Images
- Pygame - Sound Objects
- Playing Music
- Pygame - Load Cursor
- Pygame - The Sprite Module
- Snake Game
- Basic SQL intro
- Database and RDBMS
- Introduction to SQL
- SQL Subset
- RDBMS concepts
- Installing Mysql on windows
- Simple SQL queries
- SQL Expression
- SQL Operators
- DDL Operations
- DML Operations
- Functions in SQL
- Advanced SQL intro
- SQL Subqueris
- SQL Clause
- SQL Joins
- SQL Union
- SQL Group by
- SQL Views
- SQL Indexes
- SQL Transactions- Part1
- SQL Transactions- Part1
- SQL Transactions- Part2
- SQL Transactions- Part2
- SQL Transactions- Part3
- SQL Transactions- Part3
- SQL Transactions- Part4
- SQL Transactions- Part4
- SQL Transactions- Part5
- SQL Transactions- Part5
- Python Programming with MySQL intro
- MySQL Database
- Install MySQL Driver
- Check if Database Exists
- Python MySQL Create Table
- Check if Table Exists
- Primary Key
- Python MySQL Insert Into Table
- Insert Multiple Rows
- Python MySQL Select From
- Selecting Columns
- Python MySQL Where
- Python MySQL Order By
- Python MYSQL Delete From By
- Python MySQL Drop Table
- Python MySQL Update Table
- Python MySQL Limit
- Python MySQL Join
- What is Data Science
- Who is Data Scientist?
- Why Data Science
- Data Science Pipeline
- Data Science Tools
- Data Science Tools (Proprietary)
- Data Science Tools (Proprietary)
- Introduction to Python Tools for Data Science
- Introduction to Python Tools for Data Science
- Anaconda Installation and Setup
- Anaconda Installation and Setup
- Virtual Environment Setup with Anaconda
- Virtual Environment Setup with Anaconda
- What is PYPI?
- What is PYPI?
- Installing Packages via Pip
- Installing Packages via Pip
- Jupyter Notebook
- What is a Jupyter Notebook
- What is a Jupyter Notebook
- Getting familiar with Jupyter Notebook
- Getting familiar with Jupyter Notebook
- Jupyter Magic Commands
- Jupyter Magic Commands
- Case Studies
- Covid 19 Data Science Application
- Covid 19 Data Science Application
- JP Morgan
- JP Morgan
- Netflix User Case
- Netflix User Case
- UPS
- UPS
- Walmart
- Walmart
- Future of Data Scientist
- Future of Data Scientist
- Vector Introduction
- Vector Arithmetic
- Dot and cross product
- Applications of Vectors
- Probability Introduction
- Conditional probability
- Multiplication Rule of probability
- Baye’s Theorem
- Statistics Introduction
- Discrete and continuous mathematics
- Set Theory
- Applications of set theory
- Relations and Functions
- Introduction to numpys
- Creating numpy arrays and dimensions
- Indexing
- Numpy Slicing
- Numpy Arithmetic Operations
- Other Numpy Arithmetic Operations
- Broadcasting and comparison
- Solving equation with numpy
- Statistical Operation with numpy
- Numpy Exercises - Part 1
- Numpy Exercises - Part 2
- Create and manipulate arrays using numpy
- Create and manipulate arrays using numpy
- Combining 2 arrays
- Combining 2 arrays
- Compare the elements of the two arrays
- Compare the elements of the two arrays
- Program to print 2d diagonal array.
- Program to print 2d diagonal array.
- Flattening a 2d array
- Flattening a 2d array
- Python program explaining numpy.size () function
- Python program explaining numpy.size () function
- Non-Zero Functions with numpy
- Non-Zero Functions with numpy
- Changing Data Type
- Changing Data Type
- Trace of matrix
- Trace of matrix
- Addition of two matrix
- Addition of two matrix
- Subtraction of Two Matrix
- Subtraction of Two Matrix
- Intro video
- Introduction to pandas
- Data structures in pandas
- Reading files in Csv
- Retrieving data
- Analysing data
- Querying and sorting
- Working with dates
- Grouping and aggregation
- Merging data from multiple sources
- Writing data back to files
- Basic Plotting with Pandas
- Pandas Exercise
- How to create a DataFrame in Pandas from a dictionary of arrays/lists
- How to create a DataFrame in Pandas from a dictionary of arrays/lists
- Creating Dataframe from lists
- Creating Dataframe from lists
- Creating Dataframe from a list of tuples
- Creating Dataframe from a list of tuples
- Create a list of nested dictionaries
- Create a list of nested dictionaries
- Pandas to create a dataframe
- Pandas to create a dataframe
- Displays the values of each row and column using pandas
- Displays the values of each row and column using pandas
- How to read data from a string using the pandas read_csv() function
- How to read data from a string using the pandas read_csv() function
- How to reindex the rows of a Pandas DataFrame using the reindex() method
- How to reindex the rows of a Pandas DataFrame using the reindex() method
- Create two pandas Series using the NumPy linspace() function
- Create two pandas Series using the NumPy linspace() function
- Preprocess Data
- Intro video
- Intro video
- Why preprocess
- Why preprocess
- Preprocessing Technique
- Preprocessing Technique
- Null and NaN
- Null and NaN
- Forward Fill
- Forward Fill
- Selecting data with conditionals
- Selecting data with conditionals
- Dropping columns/rows
- Dropping columns/rows
- Subset and index data
- Subset and index data
- Reshaping
- Reshaping
- Pivoting
- Pivoting
- Rank and sort data
- Rank and sort data
- Matplotlib
- Intro video
- Intro video
- Introduction to Matplotlib
- Introduction to Matplotlib
- Linchart
- Linchart
- Improving style using seaborn
- Improving style using seaborn
- Scatter plot
- Scatter plot
- Histogram
- Histogram
- BarChart
- BarChart
- HeatMap
- HeatMap
- Intro video
- EDA Introduction
- Data Preparation and Cleaning
- Exploratory Analysis
- Asking and answering the questions Zale
- Intro video
- What is Artificial Intelligence?
- The history of AI and its Development
- Narrow or Weak AI
- AI Techniques and Algorithms
- Natural Language Processing
- The Ethical and Societal Implications of AI
- Relationship between AI and other Technologies
- Robotics and its connection to AI
- Difference between AI and ML
- The Role of AI
- Applications of AI
- Use of AI in Social Media
- Intro video
- Numpy
- Pandas
- Matpotlib
- SciKit-Learn
- Tensorflow
- Keras
- PyTorche
- The Natural Langauge Toolkit
- XGBoost
- CatBoost
- OpenCV
- Introduction to Machine Learning
- Intro video
- Intro video
- Introduction of Machine Learning
- Introduction of Machine Learning
- Supervised Learning
- Supervised Learning
- Unsupervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Reinforcement Learning
- Data Preprocessing
- Data Preprocessing
- Feature Extraction
- Feature Extraction
- Training data
- Training data
- Which model to use?
- Which model to use?
- Overfitting / Underfit
- Overfitting / Underfit
- Mathematical/Statistical Concepts for AI/ML
- Intro video
- Intro video
- The necessity of Statistics for AI
- The necessity of Statistics for AI
- Vectors and Matrices
- Vectors and Matrices
- Graphs for AIML
- Graphs for AIML
- Sets for AIML
- Sets for AIML
- Probability distribution
- Probability distribution
- Hypothesis testing in AIML
- Hypothesis testing in AIML
- Markov model
- Markov model
- Clustering in AIML
- Clustering in AIML
- Kernal Functions in AIML
- Kernal Functions in AIML
- Intro video
- Decision Tree
- Decision Tree
- Introduction to Supervised Learning
- Classification
- Regression
- Naive Bayes
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVMs)
- K Nearest Neighbor
- Supervised Learning Applications
- Challenges in Supervised Learning
- Intro video
- Introduction to Unsupervised Learning
- Clustering in Unsupervised Learning
- Exclusive and Overlapping Clustering
- Hierarchical Clustering
- Probabilistic Clustering
- Association Rule
- Dimensionality Reduction in Unsupervised Learning
- Principal Component Analysis
- Applications of Unsupervised Learning
- Challenges in Unsupervised Learning
- Intro video
- What are Neural Networks
- History
- Types of Neural Network
- Weights and Biases
- How do Neural Networks Work? Part-1
- How do Neural Networks Work? Part-2
- Working of some common Neural Networks
- Neural Network vs Deep Learning
- Applications of Neural Network
- Intro video
- Define the problem and determine the goals of the model
- Data preparations
- Factors to consider while choosing model
- Why to use CSV file?
- Building the ML model (Logistic Regression)- P1
- Building the ML model (Logistic Regression)- P2
- Building the ML model (Logistic Regression)- P3
- Building the ML model (Logistic Regression)- P4
- Building the ML model (Logistic Regression)- P5
- Building the ML model (Logistic Regression)- P6
- Building the ML model (Logistic Regression)- P7
- Building the ML model (Logistic Regression)- P8
- Building the ML model (Logistic Regression)- P9
- Building the ML model (Logistic Regression)- P10
- Building the ML model (Logistic Regression)- P11
- Intro video
- Importance to evaluate the ML model
- Accuracy of ML model
- Precision measure of the ML model
- Recall
- F1 Score
- Confusion Matrix
- Techniques to improve accuracy
- Summary of the course
Eligibility Criteria
• Basic knowledge of computers and keen desire to build skills in this field.
• Open to students, job seekers, and working professionals.
• Open to students, job seekers, and working professionals.
Official Certification
• Official MKCL KLiC Certificate upon successful completion of the course and evaluations.
Work-Centric Learning Approach
• Step 1: Learners are given an overview of the course and its connection to life and work
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!
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