The Power of Data Science in Planting

By 2050, the current 7.3 billion people on the planet are expected to increase to 9.3 billion. According to the Food and Agriculture Organization (FAO), agriculture must expand by 70% to meet anticipated demand ("The Future of Agriculture," 2016). In order to meet the demands of this expanding population, there is a pressing need to increase crop production while using the few available resources, such as land, water, and fertilizers.

The way farmers and other agricultural professionals make decisions is changing due to data science (Matthews, 2019). With the help of modern technology, it is now possible to gather information about the soil, water, and minerals present in farms and store it in a centralized system known as the Internet of Things (IoT). IoT stands for the concept of interconnecting related devices to the Internet so they can independently share and exchange data (Clark, 2016). To create a larger volume, such data can be combined with data from outside sources like satellites, weather stations, and even data from nearby farms. In the aggregate, data analytics can be used to gather data that farmers can use to optimize their farming.

This article explores the expanding application of data science in contemporary agriculture. It first describes the need for data science in agriculture, then discusses the opportunities and potential problems that may arise during implementation. 

Agriculture-Related Innovation-Spurring Factors

Since the beginning of human civilization as a society, agriculture has dominated. Rearing crops and animals has always been a labor-intensive task. Farming has undergone significant changes in practices, equipment, and machinery. Agriculture is still in the stage of continuous improvement after years of research and development to get it to this point.

Farmers' reliance on their intuition alone to make agricultural decisions is one of the factors fostering innovation (The Future of Agriculture, 2016). There is a danger because the farmer's error could result in no harvest at all for that season. As a result, the farmer must reduce this risk and make decisions cost-effectively.

Potential Applications of Data-Based Solutions

Farmers constantly balance a variety of factors while making agricultural decisions. They must plan what they will cultivate, where they will cultivate it, and when to raise various crops. The use of irrigation, fertilizers, and pesticides should then be decided. The timing of the harvest, reaping, and sending the goods to market come next. This type of farming is an arbitrary science, so getting every variable right for the most significant profit is critical.

Thankfully, in this day and age, farmers can use data to help them make difficult decisions. Farmers can gather information from a variety of sources and use data analytics to learn more about their farms and crops. (For a detailed explanation of data analytics techniques, refer to the data analytics course in Mumbai. )Data from sensors used inside the farm, such as those measuring soil nutrients, water content, air permeability, etc. (also known as localized data), can be combined with data from external sources, such as temperature and rainfall, or used independently to obtain various types of information. These data can be combined to evaluate and implement changes as needed continuously.

Problems with the Application of Data-Based Solutions

The agriculture industry's resistance to change is one of the main challenges in implementing data science. Farmers are very hesitant to alter their farming practices because doing so could cost them money if something goes wrong. Only large-scale farmers can afford the significant investment needed to switch to digital farming methods. In comparison to smallholder farmers, big businesses can generate returns relatively faster ("The Future of Agriculture," 2016). Small-scale, uneducated farmers, might need help to implement digital farming and might not be able to make sense of the data that has been given to them. The possibility that data-driven solutions will only benefit knowledgeable large-scale farmers is another issue with their implementation.

Results and Implications of Data Science for Agriculture

In the modern world, digital transformation in agriculture has led to many innovations. One of these initiatives is MyCrop, a real-time, intelligent, self-learning system that considers each farmer's location, crop data, and weather. It provides information, knowledge, and resources to smallholder farmers through the use of big data, machine learning, and smartphone technology.

Data science is being used at the TH Milk facility in Vietnam to control the quality of milk production in cows, each of which is equipped with an RFID chip. The milking procedure is automated with sensors in the suckers that can recognize inflammation in the cow's mammary glands. The machine will stop milking if it notices inflammation, and the cow will be marked and examined. Similar chips that track movement are affixed to each goat's legs in AfiMilk. The goat will be examined for illness if it remains motionless for an extended time or exhibits hazy sleeping habits. 

For more information on modern big data tools, join the best data science course in Mumbai and prepare for a lucrative career in this fascinating field. 

Know The Three Key Elements of Data Science

I get this question a lot: What is data science? This question would probably elicit a wide range of responses from different data scientists. In addition to being so interdisciplinary, the data scientist is required to have a variety of skill sets depending on the job role. Depending on the type of work they do, data scientists may spend the majority of their time researching and developing new theories for existing tasks, or they may even develop an entirely new theory (in the case of convolutional/recurrent neural networks, I'm sure someone is working on developing X Neural Net that could one day completely replace these existing models).

On the other hand, you might come across data scientists who regularly work with CSV files, clean and visualize data, and produce insightful reports that could influence important decisions. The definitions of what is being done in the field are fairly clear in other sciences like biology, physics, and chemistry. What precisely is data science, then?

As elements are to chemistry, data are to data science. When dealing with chemistry, you must recognize the most fundamental components and their properties and construct more complex models out of them to comprehend and foresee what would happen in various scenarios. The model is legitimate if it is accurate and generalizable. If not, they create new models. Data science is the same. A data point is the most fundamental component in data science. 

Data scientists can create a model from data, validate it, and test it to explain what is happening in the scenario we are facing. But to accomplish all of this, we also need a little domain/business expertise, math, and statistics. But before we discuss them in detail, explore the data science course in Mumbai, which offers domain-specific training for working professionals wanting to advance their skills. 

• 

  Computer Science

As elements are to chemistry, data are to data science. When dealing with chemistry, you must recognize the most fundamental components and their properties and construct more complex models out of them to comprehend and foresee what would happen in various scenarios. The model is legitimate if it is accurate and generalizable. If not, they create new models. Data science is the same. A data point is the most fundamental component in data science. Data scientists can create a model from data, validate it, and test it to explain what is happening in the scenario we are facing. But to accomplish all of this, we also need a little domain/business expertise, math, and statistics.

Strong programming skills like data parallelism, distributed computing, and memory management are necessary for large-scale machine learning. You can fit the entire dataset into memory, for instance, when you look at a mock example of training an image classifier on MNIST data. Consider that your image data totals 1 TB. If you set all images to X in your Python code, your program will crash. Data vectorization is an additional illustration. Writing a nested collection for loops to update individual weight matrix elements would be a simple way to train a neural network. That would theoretically be sufficient in the Platonic world of mathematics to obtain a strong machine-learning classifier, but in practice, that could take months or years.

• Statistics and Mathematics

Machine learning is a concept where agents learn from their surroundings and data to perform tasks more effectively. How does the computer program learn? It is largely statistical. Some machine learning algorithms (like Linear/Quadratic Discriminant Analysis, for instance) are essentially Bayesian Models, where we assume that the data has some parametric distributional structure and update the parameters algorithmically. Other classifiers, like neural networks, map real-valued vectors into a probability space (a number between 0 and 1) using a series of additions, multiplications, and output activations. Gradient descent is used to calculate the weight updates, and the chain rule simulates the information flow from the network's output to its innermost nodes. We are modeling numbers, after all. Thus, the higher your. The more math and statistics you know, the better off you'll be.

• Domain/ Business Knowledge

A common misconception about AI is that it will eventually lead to autonomous robots that can establish their own goals, take care of themselves, and rule the world. That might be true in the future, but not right now (at the time of the writing). The era of vertical artificial intelligence is currently in effect. Robots we create are trained to perform a single task and excel only in that area. For instance, if you train an image classifier to distinguish between images of dogs and cats and then use it to predict images of cars, the result will almost certainly be either a dog or a cat. The model would identify certain characteristics in the image of the car, such as edges, colors, blurriness, etc., and compare them to those it had previously observed in trained images of dogs versus cats.

Summing Up

When you combine the three factors mentioned above, you get a person who is very adept at determining the problem, the stakes, the appropriate data to use, the models to use, how to train them, and finally, how to put them into use. It is beyond doubt that you are prepared to transform data into value with these skills. Are you Interested in pursuing a career in data science and AIML? Sign up for the popular data science certification course in Mumbai, and upgrade your skills with the latest technologies. 

The Importance Of Key Elements In Data Science Projects

As a promising career path for this year, data science is becoming increasingly popular. One of the most intriguing & appealing options is this one. According to recent reports, the demand for data scientists is growing and will continue to do so in the future. 

Data science is the study of patterns in structured and unstructured data using various scientific methods, processes, techniques, and information retrieval systems. At its core, data science is a field that involves discovering patterns in data. Insight can be gained from these patterns and used for business intelligence functions or as the foundation for new product features. For product teams looking to stand out from the competition and offer customers excellent value, all these outcomes of data science projects can be helpful. This brings up the subject of discussion about;

5 Essential Data Science Elements

• Data Science and Strategy

Simply deciding what data you're going to collect and why is the first step in developing a plan or data strategy. Here, we're not discussing how to choose the appropriate mathematical methods to use or the necessary tools. The emphasis is on the data we require and why to address the business opportunity or problem. Making a connection between the business objectives and the data you intend to collect is therefore necessary before choosing a strategy. Be aware that not all data is created equally. A reflection of achieving mission-critical data for business goals is the effort that goes into gathering and formatting the data as well as getting rid of the "garbage data" that doesn't serve the business goal.

• Data Mining

Fundamentally, data mining refers to the use of one or more pieces of software to examine data patterns in sizable data sets. It has uses in many different fields, including science and research. Businesses can use data mining to learn more about their customers, which will enable them to get to know them better, create more efficient business strategies, and make the best possible use of their resources.

• Data Engineering

Data engineering is primarily concerned with developing software solutions for data issues that call for constructing a data system with data pipelines and endpoints. Data engineering calls for in-depth knowledge of a variety of data technologies & frameworks as well as the development of data solutions to support business processes.

• Data Analysis & Models

Data analysis and mathematical models are regarded as the core of data science. They allow us to use data to extract insights, make business predictions, and develop tools that can replace or supplement human labor. Check out Learnbay’s data analytics course in Mumbai to better understand the data analytics techniques and tools.

• Data Visualization & Operationalization

Data visualization involves understanding the raw data and what needs to be visualized based on the needs and goals of users and the operations, not just presenting the analyzed data correctly. Real-time personal decisions or actions, a long-term response, or a suggestion for a particular task are all examples of data operationalization.

Undoubtedly, data science is a cunning game of numbers. To remain relevant, thrive, and ultimately achieve business success, you, as a business entity, must adhere to each of the data science components. When it comes to the big data game known as Data Science, statistics, machine learning, data engineering, visualization, domain knowledge, and programming all play a significant role.

Examining each of them in detail:

• Both descriptive and inferential statistics aid in organizing and generalizing large data sets and applying probability before drawing conclusions, focusing on the characteristics of the parameters that provide the data.

• Data visualization reduces the need for data scientists, speeds up decision-making, improves insights, and makes it simpler to share the information that has been gathered. It also increases the ability to hold the audience's attention.

• Machine learning is frequently used to detect fraud and keep customers happy. It also makes it simple to make predictions using unknowable or future data.

• Deep Learning - These algorithms, along with multi-layered ANN, are very effective at identifying patterns in input data and call for extremely powerful machines.

• Domain expertise - The accuracy of the model you want to build can be significantly increased by having a high level of expertise in the field.

• Data engineering is the process of gathering data into a single warehouse to represent data uniformity as a single source of truth. It also involves collecting, storing, retrieving, and transforming the data.

• A growing number of difficult science and engineering problems, the majority of which are data-intensive, are handled using the aforementioned capabilities of advanced computing.

• Mathematics and programming - The most widely used programming languages—Python, R, Java, and NoSQL—ensure improved performance when storing massive amounts of data.

These elements, when used correctly, can make the data science project a huge success and maximize the ROI of the intended business model, just as four wheels are necessary to balance a vehicle. If you want to develop some data science projects to improve your portfolio, sign up for India’s best data science course in Mumbai. Gain practical real-world experience and high chance of getting hired by MAANG firms. 

6 Data Science Use Cases Changing the World

Today we will look at various data science use cases in the real world. We will use social media, e-commerce, transportation, and healthcare as examples to demonstrate some of the most critical data science use cases in today's industries.

Why Do We Require Data Science?

Data Science has ushered in a new industrial revolution. Every industry on the planet requires data. Companies can now analyze large amounts of data and gain insights from this massive trove of information thanks to advances in computational capabilities.

Case Studies in Data Science

Here is a list of the top six data science use cases you should be aware of. Large corporations are using data science for a variety of purposes. Let us begin with the most demanding one, Facebook. 

1. Facebook - Data-Driven Social Networking and Advertising

Today, Facebook dominates the social media landscape. Facebook, which has millions of users worldwide, carries out extensive quantitative research using data science to learn more about how people interact socially.

Facebook uses innovative data science methods to analyze user behavior and improve its offerings, turning it into a hub for innovation. Facebook makes use of deep learning, a trimming data science technology.

Facebook applies deep learning for facial recognition and text analysis. Facebook uses robust neural networks to classify faces in photography for facial recognition. To comprehend user sentences, it uses a unique text-understanding engine called "DeepText." For detailed information about these ML techniques, visit the data science course in Mumbai. 

2. Amazon - Revolutionizing E-Commerce with Data Science

Since its inception, Amazon has put a lot of effort into developing a customer-centric platform. Predictive analytics is a key component of Amazon's strategy to boost customer satisfaction. It does this by using a customized recommendation system.

The hybrid nature of this recommendation system also incorporates thorough collaborative filtering. Amazon looks at the user's prior purchases to make recommendations for more products.

This is also reflected in the recommendations made by other users who rate or use comparable products.

Using its anticipatory shipping model, Amazon uses big data to predict which products its users will most likely purchase. It looks at your purchasing habits and sends products to the closest warehouse that you might need in the future.

Additionally, Amazon adjusts prices on its websites by considering a range of variables, including user activity, order history, competitor prices, product availability, and more. Amazon employs this technique to discount popular goods while making money off less well-liked goods.

3. Uber – Going to improve Ride Comfort with Data

The following use case for data science is Uber. Uber is a well-known mobile application that allows you to order a taxi. Uber makes extensive use of Big Data. After all, Uber must keep a sizable database of drivers, clients, and other data.

As a side effect, it is built on Big Data and uses it to gain knowledge and give its users the best services. Uber and crowdsourcing both operate under the big data principle. In other words, anyone in the area which is required to get somewhere can get help from registered drivers.

4. Bank of America: Improving Customer Experience by Using Data

Ten years ago, Bank of America was one of the very first financial organizations to offer online banking to its clients. The first benefit from the services assistant from BoA, named Erica, was just introduced. It is thought to be the best financial innovation ever.

Currently, Erica provides customer support to more than 45 million daily users. Erica also accepts customer input through speech recognition, a development in data science technology.

Additionally, many other banks and BoA are using predictive analytics and data science. With the help of data science, the banking sector can identify payment and customer information fraud. Additionally, it stops accounting, credit card, and insurance fraud.

5. Airnab: Improving Guest Experience with Data

The international hospitality company Airbnb lets you host and find lodging through its app and website. Data drives this sector. It is jam-packed with big data, including guest and host information records, lodging and homestay bookings, and website traffic.

This company highly values data science. To give its customers better search results, it uses data. It uses demographic analytics to analyze website bounce rates.

In 2014, Airbnb noticed that some users from particular countries might well click the neighborhood link, browse its page and the photos, but not make a reservation.

6. Spotify: transforming music streaming 

The following Data Technology Use Case is Spotify. It is a massive music streaming company that uses data science to offer specialized music suggestions. With over 100 million registered users, Spotify deals with a tremendous quantity of big data.

The 600 Found in the tissues of daily data produced by users are used to build its algorithms, designed to enhance user experience. Data-driven company Spotify uses big data to offer users customized playlists.

Along with the introduction of the Playlists for Entertainers application, Spotify has added several partly the reason for its artists. This allows managers and artists to evaluate the streams, fan feedback, and hits produced by various Spotify playlists.

In the same year, Spotify also acquired Niland, a Websockets commodity that uses machine learning to offer its users better searches and recommendations.

Interesting enough right? All these are the power of data science itself. If you’re pursuing a career in data science and AI, join India’s best data science certification course in Mumbai and take the next leap for a lucrative career. 

 

Data Scientist Career Path – All You Need to Know

Do you want to work in this fascinating field as a data scientist, or are you just interested in it? If your response was yes, you've come to the right place. In other words, developing a career in data science and learning about it can revolutionize your professional life. However, "How do I get a job in Data Science?" is the main question. What career path do you hope to follow? There's no need to worry. We're here to support you in coming to these conclusions.

What exactly is a Data Scientist?

A data scientist is a data analyst with technical skills and the ability to solve complex problems. A data scientist is a cross between a mathematician, a computer scientist, and a trend-spotter — someone who works in both the technology and business worlds.

Furthermore, there is no evidence that a data scientist is someone who collects, analyzes, and interprets data to help a business improve its operations and gain a competitive advantage.

Data Scientist Skills

Having the right set of skills is essential in the field of data science. However, it is important to note that data science is accessible to all, whether you are a novice or an expert. So, before diving into the world of data science, let's look at the skills required to become a data scientist.

• Business Intelligence (BI)

To begin, Data Science and Business Intelligence are very similar in that they both rely on data to provide outcomes and reliable decision-support systems. Data Science handles complex and multi-structured data from data sources, whereas BI works with static and structured data. Furthermore, BI aids in the illumination of historical data for reporting, whereas Data Science analyzes historical data to make future predictions in predictive analytics or prescriptive analytics.

• Statistics And Probability

Probability and statistics are fundamental concepts in data science. Statistics refers to the use of mathematics to perform technical data analysis and estimates for further analysis using statistical methods. Statistical methods rely heavily on probability theory to make predictions. As a beginner data scientist, you can begin with basic statistical concepts such as:

• Statistical Characteristics

• Distributions of Probability

• Dimensionality Reduction Due to Over and Under Sampling

• Statistics Using Bayesian Methods

For detailed information, refer to the IBM-accredited data science course in Mumbai, and gain an understanding of the data science concepts. 

• Machine Learning And AI

Because a large number of data scientists are not proficient in Machine Learning and AI techniques, this area is quite complex and requires high-level knowledge. However, in Data Science, you must work with large amounts of data, which necessitates knowledge of machine learning techniques.

Technical Skills become Data Scientist.

When it comes to data science, programming languages are just as important. In other words, to embark on the Data Science journey, you must be familiar with some of the technical programming languages.

• R

R is a statistical modeling language that data scientists widely use. It has a steep learning curve, which makes it difficult for newcomers. Regardless, R is the tool of choice for many hard-core statisticians. It also supports various data science operations through its extensive library.

• Python

Python is among the most widely used and straightforward programming languages. It is an advanced object-oriented programming language that is commonly used in the field of data science. Developing web applications and graphical user interfaces also depends on this language. Python supports many machine learning and deep learning libraries, including Tensorflow, Keras, sci-kit-learn, and others. Thus, Python is unquestionably the best programming language to use if you want to begin a career in data science.

• SQL

SQL is widely regarded as the "bread and butter" of data science. It occurs at the beginning stages of data science operations. SQL is primarily used for data extraction and retrieval. It is also in charge of managing data stored in a relational database. SQL is used mainly to handle structured data. Most importantly, as a data scientist, you must understand how to work with unstructured data using NoSQL.

• Big Data 

It is an important technology that is only a small part of Data Science. Due to their massive data storage and processing capabilities, big data technologies such as Hadoop, Spark, and Apache Flink have taken the world by storm. Because a data scientist must deal with large amounts of data, knowledge of Big Data is essential.

• Java

Java is a well-known programming language that is used in a variety of industries. In today's world, every other company, regardless of size, needs Java. Knowing Java allows you to tune and maintain big data platforms such as Hadoop written in the same language.

How to Get Started? 

Hope this guide on data scientist career helped you get an idea of how you can get started. 

Data Scientists are undoubtedly in high demand today. With Learnbay’s data science certification course in Mumbai you can surely become a certified data scientist in just 6 months of practical training. Enroll today and get a high-paying job in top MNCs.

The Importance of Data Science in the Sports Industry

Data science is a combination of various tools, machine learning principles, and algorithms to discover patterns or trends in raw data.

You're probably wondering how data science differs from data analysis. A data scientist will first perform an exploratory data analysis before enlisting the assistance of several machine learning principles and algorithms to assess the likelihood of a specific event occurring in the future. On the other hand, a data analyst only focuses on decoding patterns or trends from previous data repositories. 

Data science is the future and is present in almost every industry, including sports.

It is simply because our digital world generates approximately 2.7 zettabytes of data. To analyze it and formulate some practical competitive strategies, the need and importance of data science enter the picture.

The Value of a Sports Analytics Degree

Sports analytics is a relatively new and unpopular undergraduate degree.

Most people who work as sports analysts have a master's degree in math or statistics and have chosen sports analytics as a minor specialization.

However, situations and educational degrees are changing. Only a few institutes are providing specialized undergraduate degrees in data science. Learnbay’s data science course in Mumbai is here to help you develop exceptional skills such as data monitoring, management, representation, evaluation, and analysis.

What Is the Difference Between Data Science and Predictive Analysis?

Data Science is used in decision-making and prediction, as well as predictive causal analytics and machine learning. Sports analytics, on the other hand, is nothing more than using data from any game or sport to build predictive machine learning models.

Sports data includes individual player performance, weather conditions, and recent/records of the team's victory or defeats against all other groups. The primary goal of sports analysis is to improve the team's overall performance, thereby increasing the likelihood of winning.

The Predictive Model

Predictive analysis in the sports industry is primarily used to evaluate insights and provide an idea of all preparations the team needs to make on game day.

Data science is used by sports websites such as ESPN, Lines, and Cricbuzz to predict the performance of players and teams in various league games.

This is advantageous and pays off in the form of improved team performance and an increased likelihood of eventual victory.

By combining predictive analytics with machine learning models and algorithms, you will be able to identify and evaluate a player's performance at a specific position or gaming order on the day of the match.

The predictive analysis consists of three major components.

• Player Analysis

As the name implies, it evaluates individual player performance and can also assist players in maintaining their fitness level based on previous training sessions. It also has the advantage of allowing access to all information related to the individual player on the same platform.

• Team Analysis

It entails analyzing and evaluating the team's statistics, which is necessary to build great machine learning models such as SVMs, deep neural networks, and many others that can directly contribute to predicting several winning combinations.

• Fans Management Analysis

Data from fans can be collected from various social media handles, such as Twitter and Instagram, to form groups and find patterns using multiple clustering algorithms. The team's management must concentrate on the factors that attract the most fans, allowing them to expand their fan base.

Big Data Applications in Sports

Several applications of big data have resulted in revolutionary changes in the world of sports. Some of the most common applications of big data in sports are:

• It aids in personalizing the overall broadcasting of the game.

• Enhances training results through the use of big data analytics

• Assists in making data-driven player recruitment decisions.

• Smart and advanced athlete recovery tracking is available.

Dashboards are used to visualize data.

In today's data-driven world, data visualization is a powerful tool. You will need more than raw data to provide you with brief and hidden information about the performance of individual players or the team as a whole when considering the sports team.

The team management will be able to use even the most complex data sets with ease by seeking the assistance of data analytics and representing complex sets of data through graphs or pie charts. If done correctly, the administration can make wise decisions, increasing the overall chances of victory.

In the sports industry, data visualization is used to display essential data via the team manager dashboard and the fans dashboard.

Futures of sports Data Analysis

It is critical to understand the technicalities to maximize the potential of data analytics in the sports industry regarding player performance and increased chances of ultimate victory.

It is not rocket science but also not easy; therefore, an undergrad degree in data science or STEM is required to excel. You can also check out the popular data analytics course in Mumbai, designed to meet the industry demand.