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DATA MINING AND MACHINE LEARNING

Exploring the available datasets to find patterns and anomalies is known as data mining. The technique of learning from heterogeneous data in a way that may predict or forecast unknown / future values is known as machine learning. Together, these two ideas make it possible to depict historical data and anticipate future data.

Data Mining

The purpose of data mining is to identify patterns in data.

Categorising data

We categorise data every day. For example, when we split groceries into oils, ingredients, spices, cleaning essentials etc. Sorting gets significantly more difficult when dealing with huge data. For instance, consumers are divided into “churners” and “not-churners” after integrating data on their demographics, past billings, payment histories and other details.

Detecting interdependency 

The different features are either independent or dependent in various extents among themselves. For example, consider a shopping mall where thousands of customers are shopping. Once data is collected, it would probably be revealed that people are buying certain set of commodities together. Sometimes associations are completely bizarre and beyond any anticipation.

Identifying outliers and anomalies

Finding odd or unexpected facts can be really helpful. A fraud detection system used by a credit card firm would serve as an illustration. if suddenly high-value items are purchased from a person’s account outside his or her regular buying range or country of residence, the system will isolate the incident and sound a virtual alarm to indicate something unusual is happening and alert the customer by way of phone calls or sms on immediate freezing the operation as a precautionary measure. 

Grouping data

Cluster analysis groups items together based on shared properties. The groups are heterogeneous, but members of a group are homogeneous. For example, an e-commerce company segment customers depending on their purchasing behaviours for promotional strategies. 

Measuring feature importance 

Various features are sorted according to their impact on the target variable. The effects may be negative or positive in case of value prediction. Whereas the magnitude is important in case of label prediction.  Finding patterns in data is the ultimate goal of all data mining techniques, regardless of the form. The process of generating tools that may be used to analyse new data using the findings of data mining is known as machine learning.

Machine Learning

The main purpose of machine learning is to develop algorithms that can “learn” from data. A machine learning algorithm’s performance will be impacted by each piece of data it processes. For instance, if the algorithm is performed on the information of one cancer patient, the machine will learn what traits a cancer patient has. The algorithm will have been exposed to several characteristics that cancer patients typically have if hundreds of cancer patient details are run through it. The objective of machine learning is to create an algorithm that can work independently and be used with fresh data. In this case, it would be an algorithm that could correctly identify if a patient had cancer or not. The same logic applies to value prediction as well. Accurately characterised data is split into “training” and “test” sets in supervised learning. Typically, training sets make up around 80% of the data, and test sets make up the remaining 20%. In our example, we have patients that are classified as “cancer” or “not cancer” by human experts. The training set, which consists of some of the patients who have previously been identified, is used to construct the machine learning algorithm. After the entire training set has processed and the optimised algorithm has been created, the algorithm is tested against the test set to ascertain its accuracy. The number of times the algorithm characterises the test set data correctly serves as a measure of accuracy. Generally speaking, a classification accuracy of over 90% is regarded as satisfactory. The classes in unsupervised learning are unknown. Based on input comparisons and the clustering of the data into several groups, the machine learning system would deduce patterns and properties.

Data Scientists are the decision makers

Data scientists are not only interested in categorising current data, even if that is a substantial aspect of their job. They are equally interested in precisely characterising unknown data as well as projecting future data. Data scientists must use both data mining and machine learning in order to conduct their tasks. To characterise the data, they must use data mining, and to generate predictions, they must incorporate machine learning techniques.  these two procedures involve a significant amount of programming. Thus, data scientists should be proficient in programming languages like R and Python in addition to having a solid understanding of statistics. 

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How to Organize Your Flask Apps Using Blueprints

Flask is a simple, easy-to-use micro web framework for Python that can help you build scalable and secure web applications.

There are occasions when programmers will place all of their logic in a single file named app.py. Many tutorials can be found online that use the same format. But for a big app, it's not a good approach.

You are obviously going against the Single Duty Principle by doing this, which states that each component of your application should only be handling one responsibility.

If you've used Django, your project may have been broken up into many modules. Additionally, you may organise your Flask applications using Blueprints, a Python module-like built-in idea.

How a Flask Application Appearance?

Your project structure will like this if you construct a simple application using the Flask documentation:

/myapp 

└── app.py

Isn't the folder wonderfully organised? The only thing you have is an app.py file that contains all of the application functionality.

Let's look at the app.py file:

from flask import Flask

app = Flask(__name__)

# Some Models Here

# Routes related to Products Page

# Routes related to Admin Page

if __name__ == '__main__':

    app.run(host=’0.0.0.0’,port=5000,debug=True)

When you imagine that you have created a large-scale programme, doesn't that look messy? You have scattered your models and different routes throughout the same file.

How Do Blueprints Address the Issue?

Flask Blueprints now come into play in this scenario. By grouping the logic into subdirectories, blueprints aid in the structure of your application. Additionally, you can put the logic and static files  all in the same subdirectory.

Consequently, your similar application will now look like this because to Blueprints:

/blueprint-tutorial

├── /myapp_with_blueprints

│   ├── __init__.py

│   ├── /admin

│   │   └── routes.py

│   ├── /products

│   │   └── routes.py          

├── app.py

You can see that your concerns are well separated at this point. The logic for admin is located in the admin folder, the logic for products is located in the products folder, and so on.

Use of Blueprints

Now that you understand what problems Blueprints solves, let's see how we can use Blueprints in our applications.

Defining a Blueprint

In the admin/routes.py file, let's define our very first blueprint for the admin functionality:

from flask import Blueprint

# Defining a blueprint

admin = Blueprint(

    'admin', __name__,

    template_folder='templates',

    static_folder='static'

)

You can import Blueprint from the Flask library because it is a built-in Flask concept. The first parameter when creating an object of the Blueprint class is the name you wish to give your Blueprint. Internal routing will later use this name, as we'll see below.

The second option is the Blueprint package's name, which is often __name__. This aids in locating the blueprint's root path.

Optional keyword arguments are supplied as the third and fourth parameters. You declare that you'll use blueprint-specific templates and static files by defining the template folder and static folder options.

How to Create Blueprint Routes

You may now use the blueprint you developed for the admin-related functionality when establishing routes for the admins.

from flask import Blueprint

# Defining a blueprint

admin = Blueprint(

    'admin', __name__,

)

@admin.route('/admin')   # Focus here

def admin_home():

    return "Hello Admin!"

Concentrate on the line where the route is specified in the given snippet. We have used @admin bp.route('...') rather than the typical @app.route('...'). This is how a route is connected to a certain blueprint.

Ways for Registering Your Blueprints

You currently have a route registered to a blueprint. But will your app be aware of this blueprint automatically?

So let's get going. We will construct a Flask app and register our blueprints there in the __init__.py file:

from flask import Flask

app = Flask(__name__)

from .admin.routes import admin

# Registering blueprints

app.register_blueprint(admin)

Using the register_blueprint() method and the blueprint's name, we register the blueprint. For even more customisation, you can also supply the procedure other parameters. One of these is url prefix, which you could need.

app.register_blueprint(admin, url_prefix='/admin')

Similar to that, if you have other blueprints, you can register them.

[skillgain-academy]

How to Organize Your Flask Apps Using Blueprints

Flask is a simple, easy-to-use micro web framework for Python that can help you build scalable and secure web applications.

There are occasions when programmers will place all of their logic in a single file named app.py. Many tutorials can be found online that use the same format. But for a big app, it's not a good approach.

You are obviously going against the Single Duty Principle by doing this, which states that each component of your application should only be handling one responsibility.

If you've used Django, your project may have been broken up into many modules. Additionally, you may organise your Flask applications using Blueprints, a Python module-like built-in idea.

How a Flask Application Appearance?

Your project structure will like this if you construct a simple application using the Flask documentation:

/myapp 

└── app.py

Isn't the folder wonderfully organised? The only thing you have is an app.py file that contains all of the application functionality.

Let's look at the app.py file:

from flask import Flask

app = Flask(__name__)

# Some Models Here

# Routes related to Products Page

# Routes related to Admin Page

if __name__ == '__main__':

    app.run(host=’0.0.0.0’,port=5000,debug=True)

When you imagine that you have created a large-scale programme, doesn't that look messy? You have scattered your models and different routes throughout the same file.

How Do Blueprints Address the Issue?

Flask Blueprints now come into play in this scenario. By grouping the logic into subdirectories, blueprints aid in the structure of your application. Additionally, you can put the logic and static files  all in the same subdirectory.

Consequently, your similar application will now look like this because to Blueprints:

/blueprint-tutorial

├── /myapp_with_blueprints

│   ├── __init__.py

│   ├── /admin

│   │   └── routes.py

│   ├── /products

│   │   └── routes.py          

├── app.py

You can see that your concerns are well separated at this point. The logic for admin is located in the admin folder, the logic for products is located in the products folder, and so on.

Use of Blueprints

Now that you understand what problems Blueprints solves, let's see how we can use Blueprints in our applications.

Defining a Blueprint

In the admin/routes.py file, let's define our very first blueprint for the admin functionality:

from flask import Blueprint

# Defining a blueprint

admin = Blueprint(

    'admin', __name__,

    template_folder='templates',

    static_folder='static'

)

You can import Blueprint from the Flask library because it is a built-in Flask concept. The first parameter when creating an object of the Blueprint class is the name you wish to give your Blueprint. Internal routing will later use this name, as we'll see below.

The second option is the Blueprint package's name, which is often __name__. This aids in locating the blueprint's root path.

Optional keyword arguments are supplied as the third and fourth parameters. You declare that you'll use blueprint-specific templates and static files by defining the template folder and static folder options.

How to Create Blueprint Routes

You may now use the blueprint you developed for the admin-related functionality when establishing routes for the admins.

from flask import Blueprint

# Defining a blueprint

admin = Blueprint(

    'admin', __name__,

)

@admin.route('/admin')   # Focus here

def admin_home():

    return "Hello Admin!"

Concentrate on the line where the route is specified in the given snippet. We have used @admin bp.route('...') rather than the typical @app.route('...'). This is how a route is connected to a certain blueprint.

Ways for Registering Your Blueprints

You currently have a route registered to a blueprint. But will your app be aware of this blueprint automatically?

So let's get going. We will construct a Flask app and register our blueprints there in the __init__.py file:

from flask import Flask

app = Flask(__name__)

from .admin.routes import admin

# Registering blueprints

app.register_blueprint(admin)

Using the register_blueprint() method and the blueprint's name, we register the blueprint. For even more customisation, you can also supply the procedure other parameters. One of these is url prefix, which you could need.

app.register_blueprint(admin, url_prefix='/admin')

Similar to that, if you have other blueprints, you can register them.

[skillgain-academy]

Be Skilled Enough

Are you ready for today's Data-driven world?

For starters, there has been a significant change in how we view data and what we can do with it. Data processing and analysis were only a few of the many fictitious components of a corporation. Because of the expansion of the internet and online organisations, businesses now have access to information they were not even aware existed.

As processing power and capabilities have grown exponentially, we have figured out how to use this data to our advantage and use it to inform future decisions. For want of a better description, data analysis has revolutionised the way firms operate by allowing us to see a wide range of options.

All sizes of businesses are struggling to locate qualified personnel who can assist them in making better use of their data. Due to this, there is a particular market need for experts with analytics and data processing abilities. The massive shortage of skilled data analysts leads to a great deal of demand and many opportunities. 

Dealing with data is not just a technological or online issue. Data is used in every domain. The most common sectors at the moment are BFSI, retail, supply chain, sports, healthcare, manufacturing, research, media, agriculture etc. People who can use analytics to benefit from their subject expertise are also in demand, in addition to those with pure analytics skills.

You can see that analytics is a skill set that is not only self-sufficient but also complements other talents well to develop a professional who is multi-skilled and can provide business context for the data.

We need to build new competencies if we want to remain relevant as automation replaces the current set of jobs. Data talents will always be prized, at least that's what the data tells us.

Strike while the iron is hot, as they say. Are you prepared enough for a data-driven future?

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Are you confused in choosing your career between Data Scientist and Business Analyst ????????????

Business Analytics” and “Data Science” — these two terms are used very frequently now-a-days. Though there is a thin line difference between two, one fact is common — both the professions are in high demand today with steep growing prospect. A lot of aspiring analytics professionals is confused in choosing between “Business Analytics” or “Data Science” as their career as they are not even sure about the distinction between these two roles. Let us discuss about the subtle disparity between these high level professions.

Role of Business Analysts and Data Scientists

Data Scientist — Data scientists work on intricate and unique issues to help the business operations towards research and developmental interfaces. For example, making a customer churn solution for the telecom industry.

Business Analyst — Business Analysts helps in managing business flow in right directions on a daily basis. He / she is the vital link between the IT team and the delivery channel.

In between of these two, Data Engineer plays an important role by utilizing industry best practices to put the data scientist’s conclusions into action. For example, deploying the machine learning model built for customer churn on telecom software.

For more understanding between them, let us consider a fascinating example. Suppose an agriculture entrepreneur resolve to implement two crucial projects. They have a team that includes a business analyst and a data scientist. How will they go about mapping the projects ? Two issues are listed below:

1.

Build a business plan to determine how many delivery outlets they need to open to increase their business by 25 % in next fiscal.

2.

Build a model to predict the crops are to be planted for optimum yields.

If you think a little deeper, Making many business assumptions and incorporating macro changes into the plan are required for the first problem statement. A business analyst will be suitable for making the necessary decisions because this will call for deeper business knowledge.

Whereas, processing enormous amounts of data regarding soil composition and weather conditions, as well as recognising hidden patterns, is required for the second problem statement. The expert should be well knowledgeable about issue formulation and algorithms for this. A data scientist would be the best candidate to take on this kind of particular issue.

Skills and Tools Required in Business Analytics and Data Science

Business Analytics

Company simulations and business planning must be presented by skilled business analytics professionals. They would be primarily responsible for analysing commercial trends. For example, profit-pricing analytics / social media analytics etc.

Excel, Tableau / Power BI, SQL, and Python are a few of the tools frequently used in business analytics. Statistical methods, forecasting, predictive modeling, dashboards, and storytelling are the tools that are most frequently employed.

Data Science

A data scientist needs to be proficient in linear algebra, programming, fundamentals of computer science, statistical concepts etc. For example, attrition analysis, sentiment analysis etc.

A data scientist typically uses R, Python, scikit-learn, Keras, KNIME, HADOOP PyTorch etc. as their tools and the most popular methods include statistics, machine learning, deep learning, natural language processing, CV etc.

However, for both roles, structural thinking and problem formulation are critical skills for success in their respective domains.

Career Paths for Data Scientists and Business Analysts

A data scientist’s strengths are in coding, statistics, mathematics and research abilities and they require continuous learning throughout their career. Data scientists tend to take on tech entrepreneur roles due to their strong technical background.

A business analyst must be more of a strategic thinker with strong project management skills. As they advance in their careers, business analysts tend to take on strategic and decision making roles.

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Data Analytics is the integral part of every domain. But WHY ?

Nowadays, there is a wealth of information available everywhere. Every moment, huge information is generated across every field of operation. However, incomplete and superfluous facts usually result in misunderstandings. You will be miles ahead of others due to improved skill to control over facts you acquire through this course. Practical and experiential learning coupled with necessary tools and techniques will give you the authority to unveil the key features and make real time critical decisions.

Know more about Data Analytics