DEV Community: SHEM MAINA

Smart Seas: How Tech is Transforming Aquaculture and Fisheries in 2025

SHEM MAINA — Fri, 06 Jun 2025 09:44:16 +0000

Introduction

With global seafood demand projected to hit 204 million tons by 2030, aquaculture and marine capture fisheries are at a pivotal moment, embracing cutting-edge technologies to ensure sustainability, efficiency, and transparency. From the shrimp farms of Vietnam to the salmon cages of Norway, and from the tuna fleets of Japan to the tilapia ponds of Kenya, innovations like artificial intelligence (AI), Internet of Things (IoT), and blockchain are transforming how we produce and harvest seafood. These advancements are not just optimizing fish farming but also revolutionizing wild-capture fisheries, creating a seamless bridge between aquaculture and marine ecosystems. For developers and tech enthusiasts, this convergence offers a thrilling frontier to innovate in a sector vital to global food security and the blue economy. Let’s dive into the technological waves reshaping aquaculture and capture fisheries across the globe in 2025.

AI-Powered Precision in Aquaculture and Fisheries

Artificial intelligence is redefining precision in aquaculture and marine capture fisheries by transforming raw data into actionable insights for sustainability and efficiency. In aquaculture, AI optimizes operations and predicts diseases, reducing waste and boosting yields. In Vietnam’s Mekong Delta, shrimp farmers use AI platforms to analyze environmental conditions and predict disease outbreaks, cutting losses by 15% and supporting sustainable exports. In Norway, AI-driven systems in salmon farms analyze fish behavior, reducing environmental impact and increasing output by 10%. In marine fisheries, Canada’s Atlantic cod industry employs AI to monitor catch data in real-time, ensuring compliance with quotas to protect overfished stocks. Similarly, in Japan’s Pacific tuna fisheries, https://www.youtube.com/watch?v=ycbIShvyOec
AI models forecast migration patterns, improving catch efficiency while adhering to sustainable quotas. In Kenya’s Lake Victoria, tilapia farmers leverage AI to enhance yields for local markets, boosting food security. These machine learning innovations enable smarter decision-making across diverse ecosystems. Areas like Nigeria and Ghana lack widespread AI adoption, where manual practices lead to inefficiencies and losses; AI could increase yields by 20%, but high costs and limited expertise are barriers. Tech firms like ReelData AI in Canada support Norway’s salmon farms with predictive analytics, while Observe Technologies in the UK aids Vietnam’s shrimp industry, cutting operational costs. For developers, this is an untapped opportunity to build AI tools tailored to local needs, from Vietnam’s shrimp ponds to Kenya’s fisheries, driving global seafood production toward a sustainable future.

IoT: Real-Time Monitoring for Smarter Oceans

The Internet of Things (IoT) is transforming aquaculture and wild-capture fisheries by using sensors and smart devices to collect live information, making seafood production more efficient, sustainable, and profitable. In fish farming, IoT tracks water conditions like oxygen, temperature, and pH to ensure fish thrive. In Chile’s salmon farms, IoT sensors have slashed feed waste by 20% and reduced disease-related losses by monitoring water quality around the clock, boosting yields for global markets. In Indonesia’s Java Sea, fishing boats use IoT to track ocean currents and vessel performance, cutting fuel use by 15% and supporting sustainable fishing quotas. In Thailand’s shrimp farms, IoT alerts farmers to water quality changes, increasing harvests by 10% and strengthening local economies. Tech firms are driving these gains: Aquabyte in Chile uses IoT cameras to optimize operations, saving costs and protecting marine ecosystems. eFishery in Indonesia deploys IoT-based smart systems, reducing energy costs by 20% for small-scale farmers. The Yield in Australia provides IoT sensors for prawn farms, improving water management and boosting output by 12%. In Africa, IoT remains untapped in many areas in Africa and Asia where manual monitoring leads to inconsistent yields and high losses. Adopting IoT could increase production by 25% in these regions, supporting food security and job creation, but high costs and limited internet access are barriers. https://www.youtube.com/watch?v=N84PUuxThP4 .

Oxygen Compressors in Iceland

Big Data Analytics: Guiding Smarter Decisions in Aquaculture and Fisheries

Big data analytics is reshaping aquaculture and wild-capture fisheries by turning vast amounts of information into clear insights, helping farmers and fishers make smarter choices. In aquaculture, analytics tools predict market prices and assess risks, ensuring better planning. In Ecuador’s shrimp farms, data dashboards forecast global demand, boosting export profits by 18% and supporting local jobs. In Portugal’s sardine fisheries, analytics tracks fish stocks and weather patterns, reducing overfishing by 22% and ensuring sustainable catches. These tools combine data from farms, markets, and oceans to guide decisions. Tech firms like AquaManager in Greece provide analytics platforms for Ecuador’s farms, optimizing pricing strategies. Pelagic Data in the USA supports Portugal’s fisheries with predictive tools, enhancing sustainability compliance. Lack of access to analytics, relying on guesswork, leads to market losses and overexploitation. Adopting big data could increase profits and protect resources, but limited internet and skills are barriers.

Blockchain for Transparency and Trust

Blockchain is revolutionizing aquaculture and wild-capture fisheries by creating secure, transparent records that build trust in seafood supply chains. In fish farming, blockchain tracks every step—from hatchery to market—ensuring fish are raised sustainably and ethically. In Bangladesh’s shrimp farms, blockchain systems log feed sources and water conditions, increasing export trust to Europe and boosting revenue by 12%. In Scotland’s mackerel fisheries, blockchain verifies catch origins and sustainable quotas, reducing illegal fishing and enhancing market credibility with a 15% rise in premium sales. These systems store unchangeable records of harvest dates, fishing locations, and processing details, meeting consumer demand for responsibly sourced seafood. Tech firms like OpenSC in Australia support Bangladesh’s shrimp industry with blockchain platforms that verify eco-friendly practices, cutting supply chain fraud by 20%. Stratis in the UK aids Scotland’s fisheries with blockchain tools that ensure transparent catch tracking, strengthening consumer trust. Blockchain remains untapped in small-scale aquaculture, where manual record-keeping leads to fraud and low market trust, limiting exports. Adopting blockchain could increase export value by 25% in these areas, supporting jobs and food security, but high costs and limited tech infrastructure are hurdles. There is a huge tech gap to create simple, affordable blockchain apps that connect local farms and fisheries to global markets.

Stay tuned for my upcoming article, where I’ll demystify blockchain’s full potential in revolutionizing aquaculture, diving deeper into its opportunities and practical applications!

Robotics and Digital Twins for Smart Aquaculture and Fisheries

Robotics and digital twins—virtual models that mirror real-world systems—are transforming aquaculture and wild-capture fisheries by automating complex tasks and predicting outcomes, enhancing everything from fish processing to human safety while optimizing feeding and monitoring. In aquaculture, robots streamline processing, packaging, and feeding, while digital twins improve supply chain planning. In Peru’s anchovy farms, robotic sorting systems process fish 30% faster, reducing waste and boosting export quality, while automated feeders save 15% on feed costs. In Ireland’s mackerel fisheries, digital twins simulate transport routes, cutting delivery times by 20% and ensuring fresher fish for markets. In Malaysia’s tilapia industry, robotic cold storage systems maintain ideal temperatures, extending shelf life by 25%, while digital twins monitor water conditions to prevent disease, boosting yields by 10%. For marketing, digital twins in South Africa’s abalone farms forecast consumer demand, increasing sales to high-value markets by 15%. For human safety, robots handle hazardous underwater tasks in Australia’s prawn farms, reducing diver injuries by 40%. Tech firms like Marel in Iceland provide robotic processing and feeding systems for Peru’s farms, enhancing efficiency and sustainability. Fishency in Israel develops digital twin platforms for Ireland’s fisheries, optimizing logistics and water management. More than half of the fisherfolk globally rely on manual processing and monitoring, leading to spoilage, safety risks, and low yields. Adopting robotics and digital twins could increase export value and improve worker safety, but high costs and limited tech skills are barriers. Developers can create affordable software for robotic control or digital twin simulations, helping farmers and fishers thrive in a sustainable future.

Sustainable Tech: Renewable Energy Integration

Renewable energy and Recirculating Aquaculture Systems (RAS) are making aquaculture and capture fisheries greener by powering operations with clean sources and farming fish sustainably on land, cutting costs and environmental impact. In aquaculture, wave energy and solar power reduce reliance on fossil fuels, while RAS reuses water in closed-loop systems to grow fish like salmon with minimal waste. In Malaysia’s offshore fish farms, wave energy runs pumps and lights, reducing carbon emissions by 30% and lowering energy costs. In New Zealand’s land-based salmon farms, RAS saves 99% of water compared to traditional methods, boosting yields by 20% and protecting coastal ecosystems. In Mexico’s shrimp farms, solar panels power processing facilities, cutting energy costs by 15% and boosting local economies. These innovations make seafood production eco-friendly and affordable. Tech firms like Ocean Sun in Malaysia provide floating solar systems for fish farms, enhancing energy efficiency. AKVA Group in Norway supplies RAS technology for New Zealand’s salmon farms, reducing environmental impact. Eco Wave Power in Israel supports Mexico’s farms with wave energy converters, cutting fossil fuel use. In regions like Ghana’s tilapia farms and Vanuatu’s coastal fisheries, renewables and RAS are underutilized, with diesel generators and open-water farming causing pollution and high costs; adopting these could cut energy costs by 25% and increase yields by 20%, but limited infrastructure and funding pose challenges.

RAS for Salmon _Credits: Alice A Auma _

Tech for Communities and Policies: Building a Sustainable Future

Technology is empowering fishing communities and ensuring aquaculture and wild-capture fisheries meet global sustainability rules, fostering cooperation and trust. In Mexico’s oyster farms, mobile apps connect farmers to cooperatives, improving market access and raising incomes by 15%. In Namibia’s hake fisheries, digital platforms track catches to comply with international quotas, reducing illegal fishing by 25% and boosting export credibility. These tools strengthen local communities and align with environmental policies. Tech firms like ThisFish in Canada provide tracking apps for Mexico’s farms, enhancing community collaboration. Global Fishing Watch in the USA supports Namibia’s fisheries with monitoring platforms, ensuring policy compliance. Inadequancy of digital tools, limit market access and policy adherence, which hampers growth. Adopting these technologies could increase incomes and support sustainable practices, but low tech literacy and costs are challenges.

The Blue Economy Vision: A Unified Future

The convergence of AI, IoT, blockchain, big data, robotics, digital twins, renewable energy, and community-focused tech is driving the FAO’s Blue Transformation, aiming for sustainable seafood by 2030. In Peru’s anchovy fisheries, IoT and blockchain ensure sustainable catches and transparent supply chains, boosting exports by 20%. In South Africa’s abalone farms, AI and robotics increase yields by 15% while protecting marine ecosystems. These integrated technologies create a resilient blue economy, unifying aquaculture and capture fisheries. Mozambique’s shrimp farms face barriers like high tech costs, limiting adoption and growth, but solutions could unlock 25% higher exports. Building affordable, open-source platforms that combine these technologies, create holistic solutions for global hubs ensuring seafood feeds the world sustainably.

Share your ideas on how to shape this future on my DEV profile and stay tuned for my upcoming article demystifying blockchain’s full potential in revolutionizing aquaculture!

eas on how to shape this future on my DEV profile and stay tuned for my upcoming article demystifying blockchain’s full potential in revolutionizing aquaculture!

Works Cited

ReelData AI
Observe Technologies
Aquabyte
eFishery
The Yield
OpenSC
Stratis
Marel
Fishency
Ocean Sun
Eco Wave Power
AquaManager
Pelagic Data
ThisFish
Global Fishing Watch

The Rise of Data-Driven Aquaculture: Revolutionizing Fish Farming in Kenya

SHEM MAINA — Mon, 08 Jul 2024 08:20:22 +0000

Introduction

Aquaculture, the farming of aquatic organisms such as fish, crustaceans, and plants, has become a vital component of global food production. As the demand for seafood continues to rise, traditional methods of fish farming are being augmented and, in many cases, replaced by precision aquaculture, which leverages data analytics to optimize production, improve fish welfare, and enhance sustainability. This article delves into the transformative impact of data-driven aquaculture, with a particular focus on its application in Kenya.

The Role of Data Analytics in Aquaculture

Real-Time Monitoring and Control

Data analytics is central to modern aquaculture, offering real-time insights into various aspects of fish farming. Sensors and cameras are used to monitor water quality parameters such as temperature, pH, dissolved oxygen, and ammonia levels. These parameters are critical for maintaining optimal conditions for fish growth and health. Continuous monitoring allows for immediate adjustments, ensuring that the aquatic environment remains conducive to fish welfare.

Predictive Analytics

Predictive analytics, which involves analyzing historical data to forecast future events, is increasingly being used in aquaculture. By examining past trends, farmers can predict and prevent potential issues such as disease outbreaks or water quality deterioration. This proactive approach helps in reducing mortality rates and enhancing overall farm productivity.

Optimized Feeding Strategies

One of the significant benefits of data-driven aquaculture is the optimization of feeding strategies. By analyzing data on fish feeding behavior and growth rates, farmers can determine the most efficient feeding times and quantities. This not only reduces feed waste but also ensures that fish receive adequate nutrition, leading to better growth rates and improved feed conversion ratios.

Market Gaps and Challenges

High Initial Costs

The implementation of data analytics in aquaculture requires significant investment in sensors, cameras, and software platforms. For many small-scale farmers in Kenya, the high initial costs can be a substantial barrier to adoption.

Technical Expertise

The effective use of data analytics tools requires a certain level of technical expertise. Many farmers may lack the necessary skills to operate and maintain these advanced technologies, highlighting the need for training and capacity-building initiatives.

Infrastructure Challenges

In regions with inadequate infrastructure, such as unreliable power supply and limited internet connectivity, the effective use of data analytics can be challenging. Ensuring that all farmers have access to the necessary infrastructure is crucial for the widespread adoption of data-driven aquaculture.

Data Integration

Integrating data from various sources and ensuring its accuracy and reliability can be complex. There is a need for standardized protocols and platforms for data collection and analysis to streamline this process.

Real-Life Scenarios in Kenya

Lake Basin Development Authority (LBDA)

The Lake Basin Development Authority (LBDA) in Kisumu has been a pioneer in adopting precision aquaculture practices. By using water quality sensors and automated feeders, LBDA has significantly improved the growth rates and health of their tilapia stock. The data-driven approach helps maintain optimal water conditions and feeding schedules, leading to reduced feed costs and enhanced fish welfare.

Victory Farms

Victory Farms, located in Homa Bay, is one of Kenya's largest aquaculture companies. They utilize a combination of sensors, underwater cameras, and data analytics platforms to monitor water quality, fish health, and feeding behavior. This comprehensive data collection and analysis have enabled Victory Farms to achieve higher productivity and better fish health outcomes. For instance, real-time monitoring of dissolved oxygen levels and water temperature ensures that the fish are kept in the best possible conditions.

Kamuthanga Fish Farm

Kamuthanga Fish Farm in Machakos County has integrated data analytics into their aquaculture practices to optimize production. They use biometric sensors to track the growth rates and health of their fish. The data collected is analyzed to adjust feeding regimes and ensure optimal nutrition, resulting in improved feed conversion ratios and reduced feed costs. Additionally, the farm uses predictive analytics to forecast potential disease outbreaks, allowing for timely interventions and treatments.

Mikoko Pamoja

Mikoko Pamoja, located in Gazi Bay, is a community-led mangrove conservation and fish farming project that leverages data analytics to monitor the health of mangroves and the surrounding aquatic environment. By analyzing water quality data and other environmental parameters, Mikoko Pamoja ensures sustainable fish farming practices that benefit both the community and the environment.

Uvuvi Aquaculture Solutions

Uvuvi Aquaculture Solutions is a tech startup in Kenya providing data analytics services to fish farmers. They offer a platform that collects data from various sensors and provides insights on water quality, fish health, and feeding behavior. Farmers using Uvuvi’s solutions have reported increased efficiency and productivity due to better management practices informed by data analytics.

Challenges in Implementing Data Analytics in Aquaculture

Data Management:

Handling large volumes of data and extracting meaningful insights can be complex. Effective data management systems are required to process and analyze the data efficiently.

Cost of Technology:

The high cost of advanced sensors, cameras, and data analytics platforms can be a significant barrier to adoption, particularly for small and medium-sized enterprises (SMEs).

Training and Support:

Farmers need adequate training and ongoing support to effectively use data analytics tools. This includes understanding how to interpret data and make data-driven decisions.

Regulatory and Privacy Concerns:

There may be concerns around data privacy and security, as well as regulatory challenges related to the use of data analytics in aquaculture.

Potential for Growth in Kenya

Kenya's aquaculture sector holds significant potential for growth through the adoption of data-driven practices. The government's commitment to supporting the blue economy, coupled with increasing demand for fish and seafood, presents a favorable environment for the implementation of precision aquaculture. Initiatives to provide funding, training, and technical support can help bridge the market gaps and overcome the challenges faced by farmers.

Conclusion

Data-driven aquaculture represents a paradigm shift in fish farming, offering a data-centric approach to optimize production, improve fish welfare, and enhance sustainability. In Kenya, the adoption of precision aquaculture practices is already showing promising results, with significant improvements in productivity and fish health. By addressing the challenges and market gaps, and leveraging the potential of data analytics, the aquaculture sector in Kenya can achieve substantial growth, contributing to the country's food security and economic development.

Leveraging of AI in search engines

SHEM MAINA — Mon, 30 Jan 2023 08:44:38 +0000

INTRODUCTION

Artificial intelligence (AI) has played a significant role in the development of modern search engines. AI techniques such as machine learning and natural language processing have allowed search engines to improve their ability to understand and interpret user queries, and to provide more relevant and accurate search results.

METHODS AND DISCUSSION

One of the primary ways AI has been used in browsers and search engines is through the development of personalized search results. By analyzing a user's browsing history and search queries, AI algorithms can predict what a user is looking for and present the most relevant results at the top of the search results page. This has made it much easier for users to find the information they need quickly and efficiently.
AI has also been used to improve the speed and accuracy of search results. For example, AI algorithms can analyze the content of web pages and rank them based on their relevance to a particular search query. This helps to ensure that the most relevant and useful pages are shown to users, rather than less relevant or spammy pages.
Another way AI has been used in browsers and search engines is through the development of virtual assistants, such as Siri and Google Assistant. These assistants use AI to understand and interpret natural language queries, allowing users to ask questions and perform tasks using their voice rather than typing out their requests. This has made it much easier and more convenient for users to interact with their devices and the internet.
In addition to these applications, AI has also been used in browsers and search engines to improve security and protect users from online threats. For example, AI algorithms can analyze web traffic and identify potential fraudulent activity, helping to keep users safe from scams and other types of cyber attacks.

Google is a pioneer in the use of artificial intelligence (AI) in search engines. The company has developed a number of AI-powered features and technologies that have helped to improve the quality and relevance of its search results.
One example of the use of AI in search engines is the development of algorithms that can understand the meaning and context of search queries, and return results that are more relevant to the user's intent. These algorithms may use techniques such as natural language processing (NLP) to understand the meaning of words and phrases, and to determine the relationship between them. This can help search engines to better understand the user's query and to provide more accurate and relevant results.

One example of the use of AI in Google's search engine is the development of algorithms that can understand the meaning and context of search queries. These algorithms use techniques such as natural language processing (NLP) to understand the meaning of words and phrases, and to determine the relationship between them. This helps Google's search engine to better understand the user's query and to provide more relevant and accurate results.
Another example is the use of machine learning algorithms to analyze patterns in search data. These algorithms can learn from past search queries and results, and use this information to improve the accuracy and relevance of future search results. For example, a machine learning algorithm might be able to identify common patterns in search queries related to a particular topic, and use this information to rank search results more effectively.
Google has also developed a number of AI-powered features that are specifically designed to enhance the user experience. For example, the company's "Google Assistant" is an AI-powered personal assistant that can understand and respond to voice commands and queries. The Google Assistant can be used to perform a wide range of tasks, including searching the web, setting reminders, and answering questions.
Artificial intelligence (AI) has played a crucial role in improving the security of search engines and protecting users from online threats. One of the primary ways AI has helped to improve security is through the development of fraud protection algorithms.
Fraud protection algorithms use AI to analyze web traffic and identify potential fraudulent activity. For example, AI algorithms can detect patterns of behavior that are commonly associated with scams and other types of cyber attacks, such as phishing attempts and malware infections. By alerting users to these threats and blocking them from accessing potentially dangerous websites, AI algorithms can help to keep users safe from online threats.
Another way that AI has helped to improve security is through the use of machine learning algorithms to analyze and classify web content. These algorithms can learn to identify malicious content by analyzing patterns of behavior and characteristics that are commonly associated with cyber threats. This can help search engines to quickly identify and remove malicious content from the internet, helping to keep users safe from online threats.
In addition to these applications, AI has also been used to improve the security of search engines by identifying and blocking spam and other low-quality content. This helps to ensure that users are only presented with high-quality, relevant results when they perform a search, rather than being bombarded with spam or irrelevant content.

Artificial intelligence (AI) has also played a significant role in improving the revenue of web browser companies by improving the user experience and increasing customer satisfaction. Here are a few examples of how AI has contributed to this process:
1. Personalized search results: AI algorithms can be used to personalize the search results that are shown to a user based on their browsing history and search queries. This can help users find the most relevant information more quickly and easily, increasing customer satisfaction and loyalty.
2. Intelligent search suggestions: AI algorithms can suggest search queries as a user types, based on their previous searches and the most popular queries on the web. This can help users find what they are looking for more quickly and easily, increasing customer satisfaction and loyalty.
3. Fraud protection: AI algorithms can be used to analyze web traffic and identify potential fraudulent activity, helping to protect users from scams and other online threats. This can increase customer satisfaction and loyalty, as users are more likely to continue using a web browser that keeps them safe from online threats.
4. Automatic translation: AI algorithms can be used to automatically translate web pages into the user's preferred language, making it easier for users to access information from around the world. This can increase customer satisfaction and loyalty, as users are able to access a wider range of information.
5. Data compression: AI algorithms can analyze and compress web content, making it faster and more efficient to load pages on slower or low-bandwidth connections. This can increase customer satisfaction and loyalty, as users are able to access information more quickly and easily.
6. E-commerce: Web browser companies can use AI algorithms to analyze user behavior and recommend products or services to users based on their interests. This can help web browser companies increase their revenue by generating more sales through e-commerce platforms.
7. Ad targeting: Google uses AI algorithms to track user activities and target ads to users based on their interests. By showing users more relevant and targeted ads, Google has been able to increase its revenue through higher ad click-through rates and conversions.

CONCLUSION

Overall, the use of AI in Google's search engine has helped to improve the quality and relevance of search results, and to make search a more useful and effective tool for users.
The use of AI in search engines has greatly improved the security of the internet and helped to protect users from online threats. As AI technology continues to advance, we can expect to see even more innovative and useful applications of AI in this and other areas.
The use of AI in web browsers has helped to improve the revenue of web browser companies by enabling them to show users more relevant ads, sponsored search results, and e-commerce recommendations.

The Ultimate Guide to Getting Started in Data Science

SHEM MAINA — Sun, 03 Apr 2022 20:11:45 +0000

Data science is a word I first heard in May 2019 when I first arrived on college, but I didn't pay much attention to it until the beginning of 2022. I was foolish at first, but things are improving with time, owing to excellent mentorship and connections with the right people. It's safe to say that if it hadn't been for this counsel, I wouldn't have lasted more than two weeks in my endeavor to break into this area.

As a novice, I'm sure you're wondering how to get started, what you'll need to do, how you'll accomplish it, and where you can find resources to help you. This blog will serve as a guide for you.

What is data science?

Data science is an interdisciplinary field that use scientific methods, procedures, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, as well as to apply that knowledge and actionable insights to a variety of application areas. Data mining, machine learning, and big data are all connected to data science.

Why Data Science

Data has the ability to produce magic. Data is required by industries in order for them to make informed judgments. Raw data is churned into valuable insights via data science. As a result, industries require data science. A Data Scientist is a magician who understands how to use data to produce magic.

A proficient Data Scientist will be able to extract useful information from whatever data he encounters. He assists the company in making the proper decisions. He is an adept at data-driven judgments, which the company requires.

Why do some find it difficult to start?

Impostor syndrome may make getting into any digital trend overwhelming, especially if you use online platforms like Twitter and Discord. You'll see people posting about stuff you've never heard of, and you'll probably feel frightened and want to give up.

## Roadmap
Language
First you have to choose a programming language that you will be using in your learning journey and also in your career. You can chose any object oriented programming language like Java, C++, Java script or Python. Personally, I prefer python due to its many use cases and easy uses. You can read more about getting started with Python in an article I published here (https://dev.to/mainashem/introduction-to-modern-python-42fo)
You also need an IDE depending on your language of choice and if you decide to use python then I would recommend Jupyter notebook.
A guide to getting started with Jupyter notebook is here(https://medium.com/codingthesmartway-com-blog/getting-started-with-jupyter-notebook-for-python-4e7082bd5d46)

Database
A database is a collection of digital material that has been indexed. It can be searched, referenced, compared, altered, or otherwise handled quickly and with low processing overhead.

A database programming language is used to create and maintain databases. SQL is the most widely used database language. You need it to manipulate your Data. To learn more about SQL, you can view the official documentation here (https://docs.oracle.com/en-us/iaas/mysql-database/doc/getting-started.html)
There also other database infrastructures that you can use including: Postgres tutorial: https://www.postgresqltutorial.com/
MongoDB tutorial : https://www.mongodb.com/docs/manual/tutorial/getting-started/
DynamoDB tutorial: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html

Python libraries for Exploratory Data Analysis
Exploratory data analysis, or EDA, is the process of becoming acquainted with your data. This includes inspecting samples of your dataset, examining its datatypes, evaluating the relationship between different combinations of variables using various charting options, and evaluating distinct variables using summary statistics.

Some of the tools used in data exploration and visualization with resources to learn them include:
Pandas-https://www.geeksforgeeks.org/data-analysis-visualization-python/?ref=rp
Numpy-https://medium.com/nerd-for-tech/a-complete-guide-on-numpy-for-data-science-c54f47dfef8d
Matplotlib-https://medium.com/analytics-vidhya/a-beginners-guide-to-matplotlib-for-data-visualization-and-exploration-in-python-3fb32d03c3cd

These are just but a few of the resources you need to get started. There are many more available on the internet and also in university programmes.
Having covered that, It is now easier to cover the data analysis process and what it entails.

Data Analysis process

Data Collection
First and foremost, you must be able to have access to data. Whatever you want to do with it, having the abilities to obtain it is an important first step.
Get your feet wet with SQL if you haven't done it already. Structured query language (SQL) is an acronym for structured query language. It's all about getting information from a database. Because the main aim is to ask a database for data, the code is actually quite straightforward.
Data Cleansing
The goal of data cleaning is to get your data into a useful form for whatever analysis comes next.
There are several parts to data cleaning: how do we manage missing values, are data types correct, is there any form of re-encoding of variables that has to be done, and so on – all of which are important to evaluate in light of the analysis ahead.
Data Wrangling
Data wrangling is a procedure that comes after data cleaning. This also has to do with getting your data into the proper format so that it may be used.
You may need to integrate a number of datasets into a single one. As a result, you might use a join or a union to integrate the datasets.
Exploratory Data Analysis
Exploratory data analysis is a data exploration technique for gaining a better understanding of the data's many characteristics. It's a kind of data summary. Before executing any machine learning or deep learning activities, this is one of the most crucial procedures to take.

Data Scientists use information representation methodologies to perform exploratory data analysis procedures to investigate, deconstruct, and summarize the essential properties of datasets. Data Scientists can locate the proper responses they require by locating information designs, spotting inconsistencies, confirming suppositions, or testing conjecture using EDA processes that take into account compelling control of information sources.

Exploratory data analysis is used by data scientists to see what datasets can reveal beyond traditional data visualization or hypothesis testing assignments. As a result, they are able to gain top to bottom.

Statistical Analysis This is where you get to exercise your statistics muscles once you have a decent comprehension of your data. Probability density functions, t-tests, linear regression, logistic regression, hypothesis testing, and so on are examples of this.

With this roadmap, you'll be well on your way to becoming a data scientist, and you'll be able to apply for positions or take on projects to solve. You can learn how to develop machine learning models using Data Science as a prerequisite once you've mastered your way around.

Python for everyone: Mastering Python The Right Way

SHEM MAINA — Sun, 27 Feb 2022 09:01:08 +0000

We all know that food, shelter and clothing are the basic needs in live and are essential for survival. Similarly in being a developer especially now in web3, python is more like a basic need. With the ever evolving changing and new advancements in technology, python is the present and the future.

Many will argue that there are many languages in the market (which is a fact) and thus we can not isolate python as the god of all. This is a valid argument but what really makes python stand out is that it is a language for everyone and can be used almost everywhere in technology. Why am I saying python is a language for everyone?
First, python is very easy to learn even for people with very little experience in programming languages due to its easy syntax.
Second, python is a multipurpose programming language and is fields such as:

Game development
Machine learning and Artificial intelligence
Data science and data visualization
Web development
Web scrapping
Desktop GUI and many more. Third, most organizations and employers are switching to python thus making it a very marketable language and skill.

Mastering Python

There are people who prefer to learn with documentation while others understand better with video tutorials. Use whatever works for you since the end goal is to master the language and make a living out of it. There are a lot of resources on the internet for you to choose from. You can also attend one of the many boot camps which are mostly instructor-led.

How do you master python is not an easy question to answer since we all have different approaches when learning a new skill. How fast one masters a skill is also unique. The most helpful answer one can give is providing a roadmap for python that will guide a learner as they try to master the language like the one below;

Python basics
Data structures in python
Objected oriented vs functional programming
Modules and packages
File and exception handling
Important libraries

You need an IDE (Integrated development environment) where you will write and run your codes after having installed the python bundle in your pc.
You can now start working with python by writing short codes based on your road map and the kind of tutorials you are using. With time you can advance to frameworks which help you minimise the amount of code you write and also save you quite a lot of time.

One key thing that many beginners the for granted is practice. Practice makes perfect is not a myth since it has been proven by professionals from all walks of life to be true. The more you code, the more you master the skill, the more you are conversant with the language, the more you know how to avoid errors, the more you understand shortcuts and ways to make your coding easier and more enjoyable.
if I was to restart my programming journey, python would definitely be the first object oriented language I would learn.

DATA STRUCTURES AND ALGORIYHMS WITH PYTHON

SHEM MAINA — Sat, 19 Feb 2022 14:39:38 +0000

Python is a language that has been put to use in many industrial fields ranging from machine learning, artificial intelligence to data science and many more. To fully maximize the usage of python, there is data that is used to make cases and enhance usability. This data has to be stored well and can be easily retrieved. This process of storing and being able to manipulate it is what we call data structures. Algorithms are tools that help us to manipulate the data in various ways.

There are two types of data structures in python; Built-in data structures and user-defined data structures

## BUILT-IN DATA STRUCTURES
These are data structures that come pre-loaded in python (also known a implicit). They include: dictionaries, lists, sets and tuples.

1.Lists

they are used to hold data of various types in a logical order. Every element of the list has an address, which is referred to as the Index. The index value starts at 0 and continues until the last element, which is referred to as the positive index. Negative indexing, which begins at -1, allows you to access elements from the last to the first. To create lists you use square brackets []. Let's use an example application to help us better comprehend lists.

my_list=[] #create an empty list
my_list=[1,3,4, 'example ',3.142]
print(my_list) #prints (1,3,4, 'example' , 3.142)

There are a lot of ways to manipulate list like adding elements, deleting elements and accessing elements.
adding elements

The append() function combines all of the elements passed to it into a single element.
The extend() function adds the elements to the list one by one.
The insert() function adds the element passed to the index value while also increasing the list's size.

my_list=[1,2,3]
print(my_list)#outputs
my_list.append([66, 67, 'another example'])
print(my_list)#outputs [1,2,3 [66,67, 'another example']]
my_list.insert(2, 'insert example')
print(my_list)#outputs [1,2, 'insert example',3 [66,67, 'another example']]

deleting elements
To delete elements, use the del keyword, which is built into Python, but it does not return anything.

my_list=[1,2,3, 'example',69,'example2', 3.142]
print(my_list)
del my_list[5]#removes element index 5 from list
print(my_list)#outputs[1,2,3, 'example',69, 3.142]
my_list.remove('example')#removes element with the value 'example' from the list
print(my_list)#outputs [1,2,3,69, 3.142]
my_list.pop(3)#pops element with index 3 from the list
print(my_list)#output  [1,2,3, 3.142]
my_list.clear()#clears the list
print(my_list)#outputs an empty list

accessing elements in lists
You pass the index values hence you access the element in your desired index.

#accessing elements
my_list=[1,2,3, 'example',69,'example2', 3.142, 678, 'last element']
for element in my_list: #access elements one by one
    print(element)
print(my_list)#access the whole list
print(my_list[2:5])#access element from index 2 to 5
print(my_list[-1])#access list in reverse

Besides these bsic functions, there are others like len() which shows the length of the list and sort() which sorts your list.

Tuples

A tuple is similar to a list except that it's a collection which is ordered and unchangeable. To create tuples, we use parenthesis().
example

#create a tuple
my_tuple=(1,44,3.142, 'tuple example')
print(my_tuple)

Most functions used to manipulate tuples are similar to those of lists which I have demonstrated above with exceptions such as;
to append tuples, we use + sign

my_tuple=(1,44,3.142, 'tuple example')
print(my_tuple)
my_tuple=my_tuple+(4,4,6,'another tuple')#append a tuple
print(my_tuple)#output (1, 44, 3.142, 'tuple example', 4, 4, 6, 'another tuple')

Dictionaries

Data values are stored in key:value pairs using dictionaries.
A dictionary is a collection that is ordered*, changeable, and does not tolerate duplicates.
Dictionaries can be created using the flower braces or using the dict() function. You need to add the key-value pairs whenever you work with dictionaries.

writing dictionaries

_dict={}
dict['one']='This is one'
dict[2]='This is two'
tinydict={'name':'John','code':'6000', 3:'zendaya'}
print (dict['one'])
print (dict[2])
print (tinydict)#outputs {'name': 'John', 'code': '6000', 3: 'zendaya'}

tinydict
print(tinydict.keys())# outputs dict_keys(['name', 'code', 3])
print(tinydict.values())#outputs dict_values(['John', '6000', 'zendaya'])

my_dict={1:'name', 2:'age'}
print(my_dict)#ouputs {1:'name', 2:'age'}

changing and adding values in a dictionary is done using the keys.

tinydict['code']=  7000#changes the element to 7000
print(tinydict)
tinydict[4]='casie'# adds a new pair entry
print(tinydict)

*deleting values
*

del tinydict['name']#deletes the element with that value
print(tinydict) #outputs {'code': 7000, 3: 'zendaya', 4: 'casie'
tinydict.pop(4)  #removes element with that value
print(tinydict)#outputs{'code': 7000, 3: 'zendaya'}

Accessing the elements is done using the keys

print(tinydict['code']) #outputs element with key ''code

Sets

A set is a collection which is unordered, unchangeable(but you can remove items and add new ones),unique and unindexed.They are created using the flower braces.

myset={3,3,3,4,4,5,5,5,23,34,23,65,56,77}
print(myset)#outputs{65, 34, 3, 4, 5, 77, 23, 56}

note that in sets, you will only output one of duplicate characters.

add elements
you can only add one element at a time.

my_set.add('zendaya')
print(my_set)#outputs {65, 34, 3, 4, 5, 77, 'zendaya', 23, 56}

manipulate sets

The union() function joins the data from both sets.
The intersection() function only returns data that is present in both sets.
The difference() function deletes data from both sets and outputs data from only the set passed.
The symmetric difference() function performs the same operation as the difference() function, but it outputs the data that is still present in both sets.

my_set={65, 34, 3, 4, 5, 77, 'zendaya', 23, 56}
print(my_set)
my_set2={3.142,33,44, 3,3,5,5, 4,56, 'new zendaya', 'zendaya'}
print(my_set2)
#manipulating sets
print(my_set.union(my_set2))#outputs {'zendaya', 65, 34, 3, 4, 5, 33, 3.142, 44, 77, 'new zendaya', 23, 56}
print(my_set.intersection(my_set2)) #outputs {'zendaya', 3, 4, 5, 56}
print(my_set.difference(my_set2))#outputs {65, 34, 77, 23}
print(my_set.symmetric_difference(my_set2))#outputs {33, 65, 3.142, 'new zendaya', 34, 44, 77, 23}

**

2.USER-DEFINED DATA STRUCTURES**

Python users can create their own Data Structures, giving them complete control over their functionality. The most common Data Structures are Stack, Queue, Tree, Linked List, and so on, all of which are also available in other programming languages.

Queues
A queue is a linear data structure based on the First-In-First-Out (FIFO) principle, which states that the data in first will be accessed first. It is constructed using an array structure and includes actions that can be performed from both the head and tail ends of the Queue, i.e., front-back and back-to-head. En-Queue and De-Queue operations include adding and deleting components, as well as accessing them. Queues are used as Network Buffers to handle traffic congestion, and as Job Scheduling in Operating Systems, among other things.

Stacks
Stacks are linear data structures that work on the Last-In-First-Out (LIFO) principle, which means that data inserted last is the first to be accessed. It is constructed using an array structure and includes activities such as pushing (adding) elements, popping (deleting) elements, and only accessing elements from the TOP of the stack. This TOP is a pointer to the stack's current location. Recursive programming, reversing words, undo systems in word editors, and other applications employ stacks extensively.

Graphs
Graphs are used to store data in the form of vertices (nodes) and edges (connections) (edges). The most accurate representation of a real-world map can be found in graphs. They are used to discover the least path by calculating the various cost-to-distance between the various data points known as nodes.

Nested lists
Connected lists are linear Data Structures that are linked together via pointers rather than being stored sequentially. A linked list node is made up of data and a pointer named next. These structures are most commonly employed in image viewer, music player, and other applications.

Arrays vs Lists
With one exception, arrays and lists are the same structure. Lists allow for the storage of heterogeneous data elements, whereas Arrays only allow for the storage of homogeneous components.

INTRODUCTION TO MODERN JAVASCRIPT

SHEM MAINA — Mon, 14 Feb 2022 10:56:49 +0000

WHAT IS MODERN JAVASCRIPT?

**
On top of the ECMAScript specification, JavaScript is commonly abbreviated as JS. It is a popular, interpreted programming language. It's a multi-paradigm language that's open source, cross-platform, high-level, and typically just-in-time compiled. It is easy to use because it is integrated with HTML. Curly-bracket syntax, dynamic typing, prototype-based object orientation, and first-class functions are all features of this language.

APPLICATIONS OF JAVASCRIPT

**
JavaScript is being utilized across a variety of platforms, despite the fact that it was meant to work in a browser environment. Listed below are a few examples:

Web server
Browser addons/extensions
Mobile applications
Databases consoles
OpenOffice scripts/macros
Java applications

**
**

GETTING STARTED

**
As a beginner, you should have a text editor which you will use to write your scripts. Java script scripts can be embedded inside the html page or written in an external sheet then linked to the html file.
For example in vs code, you have to install javascript extensions from the market place and it will be easy for you to run your code.
Here is a link to a video detailing on getting started on javascript and how to go about it in vs code.
https://www.youtube.com/watch?v=x_2sYpk75Ic&t=324s

General syntax

**
Case is important in JavaScript. The semicolon ; marks the end of each line. Curly brackets are used to separate blocks. For multiple lines, comments are placed between /* /, and for single lines, they are placed after /.
*

VARIABLES AND DATATYPES
**
The case of a word in JavaScript matters. The semicolon ; at the end of each line signifies the end of the line. Using curly brackets, block is delimited. For many lines, comments go between /* */, and for one line, they go after /.

Datatypes in javascript include;

numbers
string
boolean
null and undefined

ARRAYS**

**
An array is a special variable, which can hold more than one value:

const cars = ["Saab", "Volvo", "BMW"];

Arrays in javascript can be manipulated in the following ways;
Writing and reading
Insertion and deleting

OPERATORS

Operators are really powerful in JavaScript. Unlike in some other languages, operators are not only used to do maths, they are also very useful with strings.
Some of the type of operators in javascript include;

arithmetic operators
comparison
logical
bitwise
equality
assignment

STATEMENTS

Conditional statements in javascript are just like in many other languages. They execute after evaluating a given set of conditions and give a certain output.
They include;

for
if...else
switch

FUNCTIONS

A function is a predefined chunk of code that can be called and run multiple times to complete certain tasks.
JavaScript functions allow you to extract code, label it, and refer to it in other places.
The function keyword is used to define a JavaScript function, which is then followed by a name and parenthesis ().

Letters, numerals, underscores, and dollar signs can all be used in function names (same rules as variables).

Parameter names separated by commas may be included in the parentheses: (parameter1, parameter2, ...)

The function's code to be run is enclosed in curly brackets: {}

FRAMEWORKS

For creating a dynamic modern application, real-time chat, eCommerce, inventory, processing, and much more, JavaScript (JS) frameworks are the chosen platforms.
Some of the frameworks include;

AngularJS
Sencha Ext
React
Ember.js
Vue.js

To learn more about javascript, you can refer to javascript documentation on the internet and different tutorials.
Here is a link to some of the most comprehensive javascript tutorials
https://www.w3schools.com/js/default.asp --w3schools
https://devdocs.io/javascript/ -- javascript documentation

INTRODUCTION TO MODERN PYTHON

SHEM MAINA — Mon, 14 Feb 2022 09:19:23 +0000

what is python?

Python is a general purpose programming language language which was created by Guido van Rossum, and released in 1991. Python was created with the aim of making programmer's life easier thus it's simple syntax and readability compared to other languages.

Python has been adopted by many programmers in different fields and has many uses which include;

Data science
Artificial intelligence
Machine learning
Web development
Software development
Python can also be used to connect to databases

Getting started with python

Working with python is extremely easy even for people who don't have basic programming knowledge.
You require an IDE (Integrated development environment) is a software application that provides comprehensive facilities to computer programmers for software development. An IDE normally consists of at least a source code editor, build automation tools and a debugger. You can get the python ide from python.org where there are clear and simple instructions on how to install and run on your computer. There are different versions depending on your operating system and computer specifications. You may also need a text editor to write your code and there are several available in the internet.

After installation you are now set to write your first python code but before that there are things you need to know about python.

Identifiers & Keywords

Keywords are nothing more than special names that exist in Python. When developing a Python program, we can use these keywords to provide specialized functionality.

The following is a list of all of the keywords in Python:
False None True and as assert async await break class continue break del elif else except finally for from global if import in is lambda nonlocal not or pass raise return try while with yield

## Variables and data types
Data Types & Variables
Variables are similar to a memory area where a value can be stored. You may or may not modify this value in the future. They include;

Numbers -numerical data types(float, integers, complex, boolean)
String - represents alphabets

str= 'hello world'

print(str)
print (str[0])
print (str[2:6])
print (str[2:])

print (str *2)

List - List items are ordered, changeable, and allow duplicate values.

list= ['abcd ,1234 ,name:Joe']
tinylist =[1234, 'Joe']
print (list)
print (list[0])
print (list[2:6])
print (list+ tinylist)

Set - A set is a collection which is unordered, unchangeable*, and unindexed.

myset={3,3,3,4,4,5,5,5,23,34,23,65,56,77}
print(myset)

Dictionary - A dictionary is a collection which is ordered*, changeable and do not allow duplicates.

{}
dict={}
dict['one']='This is one'
dict[2]='This is two'
tinydict={'name':'John','code':'6000'}
print (dict['one'])
print (dict[2])
print (tinydict)

tinydict
print(tinydict.keys())
print(tinydict.values())

Tuple - A tuple is a collection which is ordered and unchangeable.

The last four are built-in data types in python. More information and resources on variables are data types can be found here https://www.w3schools.com/python/default.asp
I have also written an article on data structures and algorithms in python. https://dev.to/mainashem/data-structures-and-algoriyhms-with-python-7c0 .There is more detailed explanation.

OPERATORS

Python operators are used to perform operations on two values or variables. The following are the various types of operators available in Python;

Arithmetic Operators addition(+), subtraction(-), division(/), floor division(//), multiplication(), exponents(*), modulus(%).

# simple calculator to demonstrate arithmetic operators in python
num1 = float(input("Enter num1:"))
num2 = float(input("Enter num2:"))
add = num1 + num2  # Addition 
sub = num1 - num2  # Subtraction
mul = num1 * num2  # Multiplication
div = num1 / num2  # division
floor_div = num1 // num2  # Floor Division
power = num1 ** num2  # Power Operation
mod = num1 % num2  # Modulus
print("*****************")
print(f"Addition of {num1} and {num2}: {add}")
print(f"Subtraction of {num1} and {num2}: {sub}")
print(f"Multiplication of {num1} and {num2}: {mul}")
print(f"Division of {num1} and {num2}: {div}")
print(f"Floor Division of {num1} and {num2}: {floor_div}")
print(f"Power operation of {num1} and {num2}: {power}")
print(f"modulus of {num1} and {num2}: {mod}")

output

Enter num1:13
Enter num2:7
*****************
Addition of 13.0 and 7.0: 20.0
Subtraction of 13.0 and 7.0: 6.0
Multiplication of 13.0 and 7.0: 91.0
Division of 13.0 and 7.0: 1.8571428571428572
Floor Division of 13.0 and 7.0: 1.0
Power operation of 13.0 and 7.0: 62748517.0
modulus of 13.0 and 7.0: 6.0

Logical Operators;
and - If both the operands are true then the condition becomes True.
or - Returns True if one of the statements is true. Returns False if both the statements or conditions are false.
not - Used to reverse the logical state of its operand. Returns False if the result is true.
Assignment Operators - are used to assign values to variables .Remember that unlike other programming languages, Python does not offer increment (++) or decrement (- -) thus assignment operators are used together with arithmetic operators. Example (add and assign +=

>>> num = 10
>>> num += 50   # Same as num = num + 50 where initial value for num is 10
>>> print(num)
60

)

Comparison Operators - When comparing two values and determining their relationship, comparison operators, also known as relational operators, are utilized. It is mostly used to verify conditions during the decision-making process.

Equal to (==)
Not equal to (!=)
Less than (<)
Less than or equal to (<=)
Greater than (>)
Greater than or equal to (>=)

# Below follow the comparison operators that can be used in python
# == Equal to
42 == 42 # Output: True
# != Not equal to
'dog' != 'cat' # Output: True
# < Less than
45 < 42 # Output: False
# > Greater Than
45 > 42 # Output: True
# <= Less than or Equal to
40 <= 40 # Output: True
# >= Greater than or Equal to
39 >= 40 # Output: False

Membership Operators - Membership operators are used to test if a sequence is presented in an object. Uses in _and _not in keywords.
Identity Operators - Identity operators are used to compare the objects, not if they are equal, but if they are actually the same object, with the same memory location:
Bitwise Operators - Bitwise operators are used to compare (binary) numbers:

LOOPS

**
A loop allows us to execute a group of statements several times.
for loops

text = "Hello World"
for i in text:
  print(i)
#Output
#H, e, l, l, o, , W, o, r, l, d
for i in range(10):
  print(i)
#1, 2, 3, 4, 5, 6, 7, 8, 9, 10

while

while 10 > 8:
  print("Hello")
while not False:
  print("Hello")
while True:
    print("Hello")

nested loops
Loops that are nested together are known as nested loops. If we combine a while loop with a for loop, or vice versa, we get a nested loop

CONDITIONAL AND CONTROL STATEMENTS

**
Python's conditional statements support the standard reasoning in the logical assertions that we have.

The conditional statements that we have in Python are as follows:

if \elif \else
IF

1
2
3
x = 10
if x > 5:
   print('greater')

The if statement tests the condition, when the condition is true, it executes the statements in the if block.

ELIF

x = 10
if x > 5:
   print('greater')
elif x == 5:
     print('equal')
#else statement

x =10
if x > 5:
   print('greater')
elif x == 5:
     print('equal')
else:
     print('smaller')

When both if and elif statements are false, the execution will move to else statement.

_CONTROL STATEMENTS _
Control statements are used to manage the program's execution flow.

The control statements that we have in Python are as follows:

break
continue
pass

Here are is a link to some examples of control statements in python: https://www.softwaretestinghelp.com/python/python-control-statements/#:~:text=Control%20statements%20in%20python%20are%20used%20to%20control,the%20loop%20and%20proceed%20with%20the%20next%20iterations.

FUNCTIONS

**
In Python, a function is a block of code that runs anytime it is invoked. We can also pass parameters to the functions.
A function can return data as a result.

Let's look at an example to better comprehend the concept of functions.

#Function definition is Here
def changeme (mylist):
    "This change the value in a function"
    mylist.append([1,2,3,4])
    print("value inside function,",jkjgfjgm,cf mylist)
    return
#now you call the function
mylist=[10,20,30,40]
changeme(mylist);
print("value outside a function")

There are more detailed notes on functions in differentt python documentations on the internet.
Here is a to a sample : https://www.bhutanpythoncoders.com/functions-in-python-organize-code-into-blocks/

FRAMEWORKS

Python frameworks are a collection of modules or packages that assist in the development of web applications and enable the automation of the common implementation of certain required solutions, allowing users to focus more on the logic of the application rather than the basic processes involved in a routine, ultimately making things easier for web development enthusiasts by providing a proper structure for app development.

A web framework is a piece of software that allows you to create web applications. The client-side and server-side programming material is stored in the web framework.
The databases and their associated controls are loaded onto the server. The GUI elements are taken in by the client-side. The term "web framework" refers to a consistent approach for creating websites.
An API functions as a messenger, carrying the user's request to the database, where it is gathered and returned to the user by the receiving system.
Some of the frameworks in python include:
Django
Flask
CherryPy
Pyramid
Web2Py
Bottle
Grok
TurboGears
Tornado
Hug
Dash

Python can be regarded as the future of programming languages as it is evolving everyday and it's easy usability will ensure more people use it to make the world a better place.