Forem: Mary-softeng

Infrastructure Design for Credit Risk Modeling

Mary-softeng — Fri, 10 Apr 2026 09:11:46 +0000

Common Problems Leading Institutions Face when Building Scorecards

One version of the truth. Two people asking the same question, or repeating the same exercise should get the same answer. One way to achieve this is by sharing and reusing data sources, data extraction logic, conditions such as filters and segmentation logic, models, parameters and variables, including logic for derived ones.

Transparency and audit. Anyone who needs to see details on each phase of the development process should be able to do so easily. For example, how data is transformed to create aggregated and derived variables, the parameters chosen for model fitting, how variables entered the model, validation details, and other parameters should preferably be stored in graphical user interface (GUI) format for review.

Retention of corporate intellectual property (IP)/knowledge. Practices such as writing unique code for each project and keeping it on individual PCs makes it harder to retain IP when key staff leave. Using programming-based modeling tools makes it more difficult to retain this IP as staff leaving take their coding skills with them. To counter this, many financial institutions have shifted to GUI software to reduce this loss and to introduce standardization.

Integration across the model development tasks. Integration across the continuum of activities from data set creation to validation, means that the output of each phase seamlessly gets used in the next.

Faster time to results. It sometimes takes months to build a model and implement it in many institutions, resulting in the use of inefficient or unstable models for longer than necessary.

The Human Blueprint of a Winning Scorecard

Mary-softeng — Fri, 27 Mar 2026 12:11:15 +0000

Overview

Have you ever encountered a situation where a very scorecard which is fully developed, is rejected by an organization due to one or two variables are not usable due to incompatibility with their current systems?

In order to ensure that there is smooth development and implementation of a scorecard, the collaboration between IT, risk management(strategy and policy), modeling, validation, and operational staff is crucial.

This collaboration not only creates better scorecards, it also ensures that the solutions are consistent with business direction, prevent surprises, and enable education and knowledge transfer during the development process.

Development of scorecards in isolation can lead to problems such as inclusion of characteristics that are no longer collected, legally suspect, or difficult to collect operational, exclusion of operationally critical variables, and devising of strategies that result that are unimplementable.

Different personas are involved in a scorecard development and implementation and their level of involvement varies and different staff members are required at various key stages of the process

Scorecard Development Roles

The following participants are required:

1. Scorecard Developer - This is a person who performs the statistical analysis needed to develop scorecards.

Requirements

Some business knowledge of the product/tasks for which models are being developed
An in-depth kwoledge of various databases in the company and the data sets being used.
An in-depth understanding of statistical principles, in particular those related to predictive modeling. E.g, knowledge of logistic regression, fit statistics, multicollinearity, decision trees and so on.
A good understanding of the legal and regulatory requirements of models and of the model development process. This includes documentation requirements, transparency, and any laws that control the usage of certain information.
Business experience in the implementation and usage of risk models. This is related to the business knowledge of the product.

2. Data Scientist - This is a person who helps source and extract the required records and field of information in order to populate the scorecard development database.

Requirements

An in-depth knowledge of the various databases in the company , and the data sets being used.
Proficiency in the tools and systems to determine and document data lineage, to perform field-specific code mappings to common values and definitions from a variety of internal legacy transaction systems and external data reporters.
Ability to merge information from disparate sources and perform necessary preprocessing to deal with data issues, such as undefined codes, missing information or extreme values.
Familiarity with file formats and fields of information available from the different credit bureaus, rating agencies, and other third-party data providers.

3. Product or Portfolio Risk Manager/Credit Scoring Manager - Risk managers are responsible for management of the company's portfolio and usage of scorecards. They are usually responsible for creation policies and strategies for approvals, credit limit setting, collections statement and pricing

Requirements

Subject matter expertise in the development and implementation of the risk strategies using scores.
An in-depth understanding of corporate risk policies and procedures.
An in-depth understanding of the risk profile of the company's customers and applicants for the products/services.
A good understanding of the various implementation platforms for risk scoring and strategy implementation in the company.
Knowledge of legal issues surrounding usage of particular characteristics/processes and adjudicate credit applications.
Knowledge of credit application processing and customer management processes in the company.
Knowledge of roll rate models; delinquency trends by product, region and channel; and reports and the average time to change-off.

4. Product Manager(s) - Is responsible for the management of the company's product(s) from marketing or customer retention perspective

Requirements

Subject matter expertise in the development and implementation of product-marketing strategies.
An in-depth knowledge of the company's typical client base and target markets, including its best/most valued customers.
Knowledge of future product development and marketing direction.

5. Operational Manager(s) - Is responsible for the management of departments such as Collection, Application Processing, Adjudication (when separate from Risk Management), and Claims. Any strategy developed using scorecards, such as changes to cutoff levels, will impact these departments.

Requirements

Subject matter expertise in the implementation and execution of corporate strategies and procedures.
An in-depth knowledge of customer-related issues.
Experience in lending money.

6. Model Validation/Vetting Staff

Requirements

A good understanding of the mathematical and statistical principles employed in scorecard development
In-depth knowledge of corporate model validation policies, all relevant regulations, and the expectations of banking regulation agencies.
Real-life experience in developing risk models and scorecards in financial institutions.
A good understanding of the banking business.
A good understanding of the data within the bank.

Dysfunctional Scorecard Development Process Usually have:

Model developers who work in isolation , employ black box processes, and don't share their knowledge with others.

Risk management business staff who refuse to participate or are not invited to participate in the scorecard development process, nor share knowledge of how they use the scorecard and downstream decisions.

Risk managements staff don't have even the most basic idea of how scorecards are developed.

People afraid to make decisions because of vague accountabilities.

Model validation staff who have never built a scorecard themselves. Model validation staff who ask the wrong questions and treat the development process as an academic exercise enable the production of statistically perfect but ultimately useless models.

Model validation staff with no banking experience

vague model validation processes and policies.

7. Project Manager - Is responsible for the overall management of the project, including creation of the project plan and timelines, integration of the development and implementation processes, and management of other project resources.

Requirements

Subject matter expertise in the management of projects.
An in-depth understanding of the relevant corporate areas involved in the project.

8. IT Managers - They are responsible for management of various software and hardware products used in the company.

Requirements

Subject matter expertise in the software and hardware products involved in risk management and risk scoring implementation.
In-depth knowledge of corporate data, data governance policies and internal procedures to introduce changes to data processing.
knowledge of processing data from external data providers.

9. Enterprise Risk/Corporate Risk Management Staff (Where Applicable) Enterprise risk departments are responsible for the management of both financial and operational risks at a corporate level (as opposed to the product level). They are also involved in capital allocation and oversight of the risk function

Requirements

Subject matter expertise on corporate policies on risk management and risk tolerance levels.
In-depth knowledge of impacts on capital allocation/hedging, and so forth, of introductions to changes in risk adjudication.
In-depth knowledge of actuarial practices.

10. Legal Staff/ Compliance Manager - Their work is to ensure that any proposed segmentation and scorecard characteristics is in contravention of existing laws and regulations.

Reference

https://github.com/thonyeh/Linear-Models/blob/master/Siddiqi%2C%20Naeem%20-%20Intelligent%20credit%20scoring%20_%20building%20and%20implementing%20better%20creditrisk%20scorecards%20(2017%2C%20Wiley).pdf

A Condensed Look Inside the Credit Scoring Industry

Mary-softeng — Thu, 26 Mar 2026 12:43:58 +0000

Key Terminologies

Credit risk Scoring is a tool used to evaluate the level of credit risk associated with applicants or customers providing statistical odds, or probability that an applicant with any given score will be good or bad.
Credit scoring offer a quick and proven way to use the data , not just for reducing losses but also lead to greater profitability.
Credit Score is a digit (commonly 3 digits ranging from 200-900 depending on institution) that summarizes a customer's credit risk based on their credit report and it predicts how you manage your credit(debt) and it helps the lenders to assess the rusks upon lending.
Scorecard is a management tool that generates credit score of an applicant based on their creditworthiness. It consists of a group of characteristics, statistically determined to be predictive in separating good and bad accounts. It can be developed In-house or outsourced.

scorecards can also be defined based on the type of data used to develop them.

Custom scorecards are those developed using data for customers of one organization exclusively e.g., if a bank uses the performance data of its own customers to build a scorecard to predict bankruptcy

Generic or pooled data scorecards are those built using data from multiple lenders. e.g., few banks which did not have enough data to build their own custom scorecards, they decide to pool their data for auto loans , build a scorecard with that data and share the scorecard or customize the scorecards based on unique characteristics of their portfolios.
Also scorecards build using industry bureau data and marketed by credit bureaus such as [(https://creditinfo.co.ke/)], are types of generic scorecards.

Factors driving the increased use of scorecards

Increased regulation.
Availability of greater educational material and training for developers
Ease of access to sizable and reliable data
Corporate knowledge management fostering retention and sharing of subject-matter expertise.
Efficiency and process improvement.
Creating value and boosting profitability.
Improved customer experience.

Examples of Scorecard Characteristics

Demographics (e.g. age, postal code)
Existing relationship (e.g. payment performance, product type)
Credit bureau (e.g. inquiries, delinquencies) and so on.

Management reports produced during scorecard development

Gains chart It tells us the expected performance of the scorecard. An example of gains chart

The above gains chart exhibits;

The score bands which are arranged so that there are approximately 10% of accounts in each bucket.
The marginal event rate (marginal bad rate) rank orders from a minimum of 0.2 percent to a maximum of about 15.7%. There is some variability between the bad rate based on counts and predicted bad rate from the model due to low counts.
For the score range 163-172, for example, the expected marginal bad rate is 5.31%. This means 5.31% of the accounts that score in that range are expected to be bad.
For all accounts above 163, the cumulative event rate is 2.45%. This would be the total expected bad rate of all applicants above 163.
If we use 163 as a cutoff for an application scorecard, the acceptance will be about 70%.

Based on the outlined factors, a company can then decide , for example, to decline all applicants who score below 163, or charge them higher pricing in view of greater risk they present.

Strategies for high-risk applicants

These strategies will maximize revenue and minimize bad debt

Decline credit if the risk level is too high
assign a lower starting credit limit on a credit card or line of credit
Ask the applicant to provide a higher down payment or deposits for high ticket size loans such as mortgages or asset finance loans
Charge a higher interest rate on a loan
Charge a higher premium on insurance policies
Ask high risk applicants for further documentation on employment, assets or income

Despite of Risk scoring being used to evaluate levels of risks, they have also been applied in other operational areas , such as:

Streamlining the decision making process
Reducing turnaround time for processing applications through automated decision making
Evaluating quality of portfolios intended for acquisition through bureau-based generic scores.
Setting economic and regulatory capital allocation.
Forecasting
Comparing the quality of business from different channels

The amount of confidence in any scorecard or model must be based on both the quality and quantity of the underlying data and decision making strategies adjusted accordingly. Models are very useful when used judiciously, along with policy rules and judgement, recognizing both their strengths and weaknesses. The most accurate model in the world will not help if the financial institution chooses not to confirm any information from credit applicants or to verify identities. One need to be very realistic when it comes to using scorecards and not have an unjustified level of trust in them.

Reference

Intelligent Credit Scoring

Main Reasons Why Python is Recommended for Data Science

Mary-softeng — Fri, 04 Mar 2022 10:51:05 +0000

Python is easy to learn and its results are being achieved via few lines of code
It is cross platform meaning that the same code can work in multiple environments without needing any change.
It excecutes faster than other similar languages used for data analysis like R and MATLAB.
It has excellent memory management capability, especially garbage collection which makes it versatile in gracefully managing very large volume of data.
Python has a very large collection of libraries which serve as a special purpose analysis tools.
Python has packages which can directly use the code from other languages like Java and this helps in optimizing code performance by using existing code of other languages, whenever it gives a better results.

RESOLUTIONS FOR DATA SCIENTIST

Mary-softeng — Fri, 04 Mar 2022 10:31:05 +0000

1. Deepen your subject matter expertise
As a data scientist, you need to deepen your familiarity with the domain of a given problem and non concentrating on the "Data Science" components.

2. Align on security best practices
Security is a continuous practice that all data scientists should take seriously especially when it comes to the open-source tools. Since open source is such an integral part of the data scientist toolkit, it is crucial that practitioners keep up with patches, updates and vulnerabilities of their tools.

3. Contribute to open source
Open source is an ecosystem of collaboration that transcends geographical locations, time zones and organizations. Take time to engage with the community that has brought us so many of today's intergyral backend technologies, while also expanding your professional network in learning new skills.

4. Never Stop Learning
As a data scientist, you should keep on learning something new since the field keeps on changing and growing every time.

JAVA METHODS

Mary-softeng — Wed, 01 Dec 2021 10:33:54 +0000

Java methods are block of codes which runs only when they are called and they are also known as functions since they can perform certain actions.
How To Create Methods
A method must be declared within a class and it is defined with the word method, followed by parentheses ().

public class Main{
  static void myMethod(){
  }
}

myMethod() is the name of the method
static means that the method belongs to the Main class and not an object of the main class.
void means that this method does not have a return value.

Call A Method
you call a method by writting the name of the method followed by two parentheses and a semicolon;
A method can also be called multiple time.

pubblic class Main{
  static void myMethod(){
    System.out.println("I am Java Method");
   }
 public static void main(String[] args) {
   myMethod();
   }
}
Output
I am Java Method

Java Method Parameters
Parameters and Arguments
Parameters act as variables inside the method. They are specified after the method name , inside parentheses and they can be as many as possible separated with a comma.

public class Main {
  static void myMethod(String fname, int age){
    System.out.println(fname + "is" +age);
  }
  public static void main(String[] args) {
   myMethod("Mary", 23);

  }
}
output
Mary is 23

Return Value
For a method to return a value, use primitive data types such as(int,char,etec) instead of void, and use the return keyword inside the method:

public class Main{
  static int myMethod(int x) {
    return 7 + X;
   }
 public static void main(String[] args) {
   System.out.println(myMethod(5));
  }
}
outputs
12(7+5)

the sum of two parameters

public class Main{
  static int myMethod(int x, int y) {
    return x + y;
   }
 public static void main(String[] args) {
   System.out.println(myMethod(5,7));
  }
}
outputs
12(5+7)

It is highly recommended to store the resuly in a variable

public class Main{
  static int myMethod(int x, int y) {
    return x + y;
   }
 public static void main(String[] args) {
   int z =myMethod(5,3);
   System.out.println(z);
  }
}
outputs
12(5+7)

If...Else in Methods

public class Main{
//create a checkAge() method with an integer variable
  static void checkAge(int age){
    if (age< 18) {
         System.out.println("Access Denied");
   } else{
      System.out.println("Access granted");
    }
}
public static main(String[] args) {
  checkAge(20);
}
output
"Access granted"

Method Overloading
Method overloading means that multiple methods can have the same name with different parameters:

public class Main {
  static int plusMethod(int x, int y) {
    return x + y;
  }

  static double plusMethod(double x, double y) {
    return x + y;
  }

  public static void main(String[] args) {
    int myNum1 = plusMethod(8, 5);
    double myNum2 = plusMethod(4.3, 6.26);
    System.out.println("int: " + myNum1);
    System.out.println("double: " + myNum2);
  }
}

output
int: 13
double: 10.5599

Java Scope
the scope means that variables are only accessible inside the region they are created
Method Scope
Variables declared directly inside a method are available anywhere in the method following the line of code in which they are declared:

public class Main {
  public static void main(String[] args) {

    // Code here cannot use x

    int x = 100;

    // Code here can use x
    System.out.println(x);
  }
}

Block Scope
A block of code refers to all of the code between curly braces {}. Variables declared inside blocks of code are only accessible by the code between the curly braces, which follows the line in which the variable was declared:

public class Main {
  public static void main(String[] args) {

    // Code here CANNOT use x

    { // This is a block

      // Code here CANNOT use x

      int x = 100;

      // Code here CAN use x
      System.out.println(x);

   } // The block ends here

  // Code here CANNOT use x

  }
}

Java Recursion
Recursion is the technique of making a function call itself. This technique provides a way to break complicated problems down into simple problems which are easier to solve.

public class Main {
  public static void main(String[] args) {
    int result = sum(10);
    System.out.println(result);
  }
  public static int sum(int k) {
    if (k > 0) {
      return k + sum(k - 1);
    } else {
      return 0;
    }
  }
}
output
55

example explained
When the sum() function is called, it adds parameter k to the sum of all numbers smaller than k and returns the result. When k becomes 0, the function just returns 0. When running, the program follows these steps:

10 + sum(9)
10 + ( 9 + sum(8) )
10 + ( 9 + ( 8 + sum(7) ) )
...
10 + 9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1 + sum(0)
10 + 9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1 + 0

HOW TO RUN DATA SCIENCE PROJECT USING JUPYTER NOTEBOOK

Mary-softeng — Fri, 27 Aug 2021 15:50:55 +0000

Processes:
Download and install anaconda.
Launch Jupyter notebook (anaconda3)

This will direct you to a web browser
If you are creating a new project from the scratch, click New, then folder

Then open the new created folder and under new click python 3

Start by importing all the libraries the will be required while running the program and click Run to ensure that all the libraries are installed into your computer.

Then load the dataset

Train the dataset

Graphical representation of the output

FASTAPI

Mary-softeng — Wed, 11 Aug 2021 10:02:29 +0000

what is FastAPI?
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
The key features are:
•Fast: Very high performance, on par with NodeJS and Go.
•Fast to code: Increase the speed to develop features by about 200% to 300%.
•Fewer bugs: Reduce about 40% of human (developer) induced errors.
•Intuitive: Great editor support. Completion everywhere. Less time debugging.
•Easy: Designed to be easy to use and learn. Less time reading docs.
•Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.
•Robust: Get production-ready code. With automatic interactive documentation.
•Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema
How to install FastAPI
Pipenv is used while setting up the development environment for our APIs. Pipenv makes it easier to isolate your development environment irrespective of what things are installed on your machine. It also lets you pick a different Python version than whatever is installed on your machine. It uses Pipfile to manage all your project-related dependencies. Use pip install FastApi in a command prompt.

Once installed you can activate the virtual environment by running the command pipenv shell

FastAPI Hello World
Let’s get some practice with FastAPI! We’ll take a look at a simple Hello World! and break down the pieces.

To start the server, we need to run the following command:

Let’s break this down:
•main: refers to the file name
•app: refers to the object of FastAPI created inside the hello.py file
•--reload: parameter that makes the server restart after the code changes
Let’s break down our Hello World! code:
•Line 1: We import FastAPI, which is a Python class that provides all the functionality for the API.
•Line 3: We create an instance of the class FastAPI and name it app. This is the app referred to by uvicorn in the above command.
•Line 5: We create a GET path.
•Line 6: We define the function that will execute whenever someone visits the above path.
•Line 7: We return a response to the client whenever the route is accessed.
Basic FastAPI building blocks
Let’s explore some of the building blocks of FastAPI, including path parameters, query parameters, and request bodies.
Path parameters
Path parameters help scope the API call down to a single resource, which means you don’t have to build a body for something as simple as a resource finder.
These parameters are enclosed in curly brackets {}, and they offer a way for you to control the representation of specific resources. They’re placed before the query string and within the path of an endpoint.
Let’s take a look at how to use them:

The value of the path parameter course_name will be passed to the function read_couse() as the argument course_name.
Query parameters
Query parameters are optional. In FastAPI, function parameters that aren’t declared as part of the path parameters are automatically interpreted as query parameters.
Let’s look at some sample code:

The query is the set of key-value pairs that comes after the question mark ? in a URL, separated by an ampersand &.
Take a look at the following URL:
http://localhost:8000/courses/?start=0&end=10
Its query parameters are:
start with a value of 0 and end with a value of 10.
In line 8 of the code, we pass the two query parameters that our API would expect.
Request body
A request body is data sent by the client to your API. To declare one in FastAPI, we can use Pydantic models.
Let’s see an example of how we can do this:

Let’s break this down:
•Lines 1-3: We import the required packages.
•Line 5: We declare the request data model.
•Line 11: We create an instance of the FastAPI class.
•Line 13: We create a POST path.
•Line 14: We add the request data model to the path.

https://colab.research.google.com/drive/1_LLiBhSQ1RZuHnnL7cE6ML9mwVV1h_Nb?usp=sharing

Introduction to Python Functions

Mary-softeng — Fri, 30 Jul 2021 14:42:53 +0000

What are python functions?
Python Functions are a set of actions that we group together, and give a name to. The core functions in Python language includes string.title() and list.sort(). In python one can define his or her own functions, in order to "teach" Python new behavior.

Advantages of using Python functions
•We write a set of instructions once. We save some work in this simple example, and we save even more work in larger programs.
•When our function works, we don't have to worry about that code anymore. Every time you repeat code in your program. Writing a function means there is one place to fix mistakes, and when those bugs are fixed, we can be confident that this function will continue to work correctly.
•We can modify our function's behavior, and that change takes effect every time the function is called. This is much better than deciding we need some new behavior, and then having to change code in many different places in our program.

Basic python syntax
In Python a function is defined using the def keyword:

Calling a Function
To call a function, use the function name followed by parenthesis:

Arguments in Python
Information can be passed into functions as arguments.
Arguments are specified after the function name, inside the parentheses. You can add as many arguments as you want, just separate them with a comma.
The following example has a function with one argument (planguage). When the function is called, we pass along a programming language, which is used inside the function.

NOTE:
A parameter is the variable listed inside the parentheses in the function definition.
An argument is the value that is sent to the function when it is called.

Number of Arguments
By default, a function must be called with the correct number of arguments. Meaning that if your function expects 2 arguments, you have to call the function with 2 arguments, not more, and not less.

Arbitrary Arguments, *args
If you do not know how many arguments that will be passed into your function, add a * before the parameter name in the function definition.
This way the function will receive a tuple of arguments, and can access the items accordingly:

Keyword Arguments
You can also send arguments with the key = value syntax.
This way the order of the arguments does not matter.

Arbitrary Keyword Arguments, *kwargs
If you do not know how many keyword arguments that will be passed into your function, add two asterisk: * before the parameter name in the function definition.
This way the function will receive a dictionary of arguments, and can access the items accordingly:

Default Parameter Value
If we call the function without argument, it uses the default value:

Passing a List as an Argument
You can send any data types of argument to a function (string, number, list, dictionary etc.), and it will be treated as the same data type inside the function.
E.g. if you send a List as an argument, it will still be a List when it reaches the function:

Return Values
To let a function return a value, use the return statement:
The pass Statement
Function definitions cannot be empty, but if you for some reason have a function definition with no content, put in the pass statement to avoid getting an error.

Recursion
Python also accepts function recursion, which means a defined function can call itself.
Recursion is a common mathematical and programming concept. It means that a function calls itself. This has the benefit of meaning that you can loop through data to reach a result.
The developer should be very careful with recursion as it can be quite easy to slip into writing a function which never terminates, or one that uses excess amounts of memory or processor power. However, when written correctly recursion can be a very efficient and mathematically-elegant approach to programming.
In this example, tri_recursion() is a function that we have defined to call itself ("recurse"). We use the k variable as the data, which decrements (-1) every time we recurse. The recursion ends when the condition is not greater than 0 (i.e. when it is 0).

https://colab.research.google.com/drive/1SjCatUpRuRxsVJdsqKevanBmeS5OcWib?usp=sharing

Python Basics, Pythons 101!

Mary-softeng — Fri, 23 Jul 2021 11:53:33 +0000

Definition to Python
Python is a general purpose language which has a wide range of applications from web development, scientific and mathematical computing (orange, SymPy, NumPy) to desktop graphical user interfaces such as pygame. Python programs have the extension .py and can be run from the command line by typing python file_name.py.

Python Features
1.A simple language which is easier to learn.
Python has a very simple and elegant syntax which is much easier to read and write python programs compared to other programming languages. Python makes programming fun and allows you to focus on the solution rather than the syntax.
2.Fee and open source-You can use freely use and distribute python, even for commercial use.
3.Extensible and Embeddable-When an application requires high performance, you can easily combine pieces of C\C++ or other languages with python code. This will give your application high performance as well as scripting capabilities which other languages may not provide out of the box.
4.A high-level, interpreted language-When you run python code, it automatically converts your code to the language your computer understands.
5.Large standard libraries to solve common tasks-Python has a number of standard libraries which makes life of a programmer much easier since you do not have to write all the
code yourself. Example, when you want to connect MySQL database on a web server, you can use MySQL library by using import MySQLdb.
6.Object oriented-Everything in python is an object and this helps you to solve a complex problem intuitively.

Applications of Python.

Web Application. You can create scalable Web Applications using frameworks and Content Management System (CMS) that are built on python. Some of these platforms are Django, flask, pyramid, plone.
Scientific and numeric Computing. There are numerous libraries available in python for scientific and numeric computing

Python Identifiers
A python identifier is a name used to identify a variable, function, class, module or other object. The following are the naming conversions for python identifiers:
• Class names starts with an uppercase letters and all other identifiers with lowercase letters.
• Starting an identifier with a single leading underscore indicates that the identifier is private.
• Starting an identifier with two leading underscores indicates a strongly private identifier.

Reserved Words
Reserved words cannot be used as constants or variable or any other identifier names. All the python keywords contain lowercase letter only eg:
and
exec
assert
finally
break
for
class
from
continue
global
def
if
del
import
elif
in
else
except
lambda.

Lines and indentation
Blocks of code are denoted by line indentation which is rigidly enforced. The number of spaces in the indentation is variable, but all statements within the block must be indented the same amount.

Multi-line Statements
Statements in Python typically ends with a new line. However, python allows the use of line continuation character () to dente that the line should continue.

Quotation in Python
Python accepts single (‘), double (“) and triple (‘”) quotes to denote string literals as long as the same type of quote starts and ends the string. The triple quotes are used to span the string across multiple lines.

Comments in Python.
A hash sign (#) that is not inside a string literal begins a comment. All characters after the # and up to the end of the physical line are part of the comment and python interpreter ignores them.Triple quotes (‘’’) are used when commenting a block of statement

Python Variables.
Variables are reserved memory locations to store values.
Assigning values to variables.
The equal sign (=) is used to assign values to variables. The operand to the left is the name of the variable and the operand to the right of the = operator is the value stored in the variable.

Multiple Assignment
Python allows you to assign value to several variables simultaneously.

Here, an integer object is created with the value 1 and all three variables are assigned to the same memory location.
You can also assign multiple objects to multiple variables eg

Here, two integer objects with values 1 and 2 are assigned to variables a and b respectively and one string object with value “John” is assigned to the variable c.
Python Data Types.
Python has five standard data types which includes:
• Numbers
• String
• List
• Tuple
• Dictionary

Python Numbers
Number data types store numeric values. Number objects are created when you assign a value to them.

Python supports four different numerical types.
• Int (signed integers)
• Long (long integers, they can also be represented in octal and hexadecimal)
• Float 9floating point real values)
• Complex (complex numbers)

Python strings
Strings in python are identified as contiguous set of characters represented in quotation marks.
The plus (+) sign is the string concatenation operator and the asterisk (*) is the repletion operator.

Python Operators
Arithmetic: +, -, *, /, and % (modulus)

Comparison: ==! =, <, >, <=, >=

Logical: and, or, not

Exponentiation: **
Execution: os.system ('ls -l')

https://colab.research.google.com/drive/1bXEWeKALGuY0h9kOd6PCFQ9THMNSP-FU?usp=sharing