Large language models (LLMs) are a type of advanced AI technology that helps computers understand and generate human-like text. These models are built using a method called the transformer architecture, which is like a blueprint for creating them.

An example of an LLM is ChatGPT, created by OpenAI. LLMs have become crucial as AI technology is becoming more and more important in our lives. They work by learning from huge amounts of data, like a super-smart student studying from tons of books.

What exactly are Large Language Models (LLMs)?

LLMs are like brainy computers made of many parts (called parameters) that can take an input and give an output in the form of human-like text. They're basically machines that understand and create text like humans do.

These models are trained using massive amounts of information from things like books, articles, and websites. They're part of a special kind of AI that's made to create text. LLMs have improved a lot since the invention of transformers by Google, making language processing better and more effective.

Here are top LLMs right now:

• GPT 3.5 || GPT-4.0

GPT 4 is the latest version from OpenAI. It's really good at creating accurate content based on what users ask for. They've focused on making it safer and more ethical. The creators of GPT 4 listened to feedback from users of previous models to make this one even better.

• META (LLAMA)

META (LLAMA) is a big AI model from Meta. They made it to make AI more accessible for researchers who might not have had the chance to study these models before. META (LLAMA) aims to help researchers improve AI and reduce issues like harmful content and bias. Right now, researchers can get access to META (LLAMA) through Meta.

• Google's (Palm AI)

Google's Palm is a new type of AI called Pathways. It's meant to be like a super AI that can do lots of tasks at once, unlike older AIs that could only do one thing at a time. Palm is designed to learn new things quickly, just like humans. For example, once you learn how to ride a bike, you don't forget easily.

Where are Large Language Models (LLMs) used?

AI is becoming more common in our daily lives. Here are some places where LLMs are making a big impact:

• Retail: LLMs help make chatbots for stores, so customers can get help without needing a person.
• Language Translations: They're great for translating languages, which is useful for students and people living in new countries.
• Code Generation: LLMs trained in coding languages might make it easier for engineers to write code and do their jobs.

In this blog following topics will be covered 

1. - Why Data Science?
2. - What is Data Science?
3. - Who is a Data Scientist?
4. - Job Trends
5. - How to solve a problem in Data Science?
6. - Data Science Components
7. - Data Scientist Job Roles

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Why Data Science?

• Harvard Business Review hails data science as "the sexiest job of the 21st century". What makes data science so important? 
• Why are data scientists the highest-paid professionals? 
• Most importantly, why study data science? 
• In this article, we will examine some of the main reasons why data science has become one of the most attractive jobs in the market. 
• We understand the needs of companies and the need for data scientists to maximize their performance.

What is Data Science?

• Data science is the process of combining experts, programming skills, knowledge of mathematics, and statistics from the field to gain meaningful insights from data. 
• Data science practitioners use machine learning algorithms for numbers, text, images, video, audio, and more to develop artificial intelligence (AI) systems to perform tasks required by the humanities. 
• These systems generate statistics that can be translated into clear business value for the consequences and for business users.
• Data science professionals are emerging as one of the most promising and desirable career paths for skilled professionals. 
• Successful data professionals today realize that large-scale data overrides traditional skills for analyzing data processing and programming skills. 

• In order to find effective statistics for their organizations, data scientists must have mastered the entire spectrum of the Data science life cycle and have the flexibility and understanding to maximize revenue at each stage of the process.

Who is a data scientist?

Data scientists have several definitions. Simply put, a data scientist is someone who adheres to the art of data science. The most popular term for ‘data scientist’ is Created by Patil and Jeff Hamperpatcher. 

Data scientists are people who twist complex data problems with strong expertise in certain fields of science. They work with a number of components, including mathematics, statistics, and computer science (although not experts in these fields).

Job Trends in Data Science 

Data science is forecast to grow over the next decade. It is a shocking fact that 90% of the world's data was created in just 2 years. 

• It is unimaginable to recognize the amount of data that will be generated in the next decade. 
• The demand for data scientists will increase by 28% by 2020 alone. 
• More and more businesses are starving for data, and they need data to include specialized data scientists who can design products for users. The U.S. The Bureau of Labor Statistics estimates that by 2026, about 11.5 million jobs will be created.

How to solve a problem in Data Science

1. Discovery:

• The first step is discovery, which involves asking the right question. When you start any data science project, you need to determine what the basic requirements, priorities and project budget are. 
• At this point, we need to determine all the requirements such as project number, technology, time, data, and an end goal, and then the business problem can be designed at the first conceptual level.

2. Data preparation: 

• Data preparation is also known as data munching. At this point, we need to do the following:
• Data cleaning
• Data reduction
• Data integration
• Data transformation
• After completing all of the above tasks, we can easily apply this data to our additional processes.

3. Model planning:

• At this stage, we need to determine the different methods and techniques for establishing the relationship between the input variables. 
• We use research data analysis (EDA) using various statistical formulas and visualization tools to understand the relationships between variables and to see what data can tell us. 

4. Model Building: 

• At this stage, the model construction process begins. 
• We will create databases for training and testing purposes.

• We will use various techniques such as association, classification, and clustering to create the model.

5. Operationalize: 

• At this stage, we will provide the final reports, summaries, code, and technical documentation of the project. 

• This step gives you a brief overview of the full project performance and other factors prior to full deployment.

6. Communicate the results: 

• At this stage, we will check whether we are reaching the goal we set in the initial stage. 

• We will communicate the findings and final results with the business team.

Data Science Components

1- Statistics:

• Statistics are an important component of data science. 

• It is a method of collecting and analyzing large quantities of numerical data to obtain useful and meaningful statistics.

2-Visualization:

• Visualization is the representation of data in areas such as maps and maps so that people can easily understand it. 

• This makes it easier to access extensive data. 

• The main goal of data visualization is to help identify patterns, trends, and foreigners in large data sets.

3-Machine Learning:

• Machine learning acts as the backbone of data science. 

• This means training a machine to function as the human brain. 

• Different methods are used to solve problems. 

• With the help of machine learning, it is easy to make predictions about unexpected/future data.

4- Domain expertise:

• Domain expertise is the specific knowledge or skills of a particular area. There are different areas of data science, for which we need field experts. 

• You cannot open the entire feature of an algorithm without proper knowledge of where the data is coming from. 

• The more we know about the problem, the more difficult it will be to solve. Also, a high level of expertise in the area will greatly improve the accuracy of the model you want to create. 

• This is why data scientists are generally well aware of the various areas in which they work. 

• They may not be experts, but a good data scientist usually focuses on multiple skills.

5- Data Engineering:

• Includes data engineering, data recovery, storage, retrieval, and transfer. The key to understanding data engineering is in the field of engineering. 

• Engineers design and create things. Data engineers design and create tubes that transform and carry data, making it very useful for data scientists and other end users. 

• These pipelines take data from different sources and store it in a single warehouse.

6- Programming Languages:

Python: 

• Python is a high-level programming language provided by a wide-ranging library. This is a very popular language because most data scientists like it. 

• It offers expandable and generated data analysis libraries. 

• The best features of Python are dynamic type, functional, object-oriented, automated memory management, and practice.

R: 

• R is a popular programming language among data scientists that can be used on Windows, Unix, and Mac operating systems. 

• The best feature of the R language is data visualization, which is tough on Python, but it is more startup-friendly than Python. 

• R language is used to perform social analysis using subsequent data. Twitter language is used for data visualization and semantic clustering, and Google uses it to evaluate ad performance and make financial predictions.

Data Scientist job role?

The role of the data scientist is really a challenge! Although the skill packages and capabilities used by data scientists may vary as a skilled data scientist.

• Be very innovative and unique in his approach to extracting data, gaining useful insights into solving business problems and challenges, and using various technologies intelligently.

• Ability to find and create rich data sources.

• A handful of experience in data mining techniques such as graph analysis, method finding, result perspectives, clustering, or statistical analysis.

• Develop working models, systems, and tools using experimental and functional methods and techniques.

• Analyze data from different sources and perspectives and find hidden statistics.

In this blog following topics will be covered

What are the Best Books for Data Science?

1. Overview of Data Science
   
2. Books to begin with Data Science
   
3. Books to help you Master Data Science

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Overview of Data Science

• Data science is the process of combining experts, programming skills, knowledge of mathematics, and statistics from the field to gain meaningful insights from data. 
  
• Data science practitioners use machine learning algorithms for numbers, text, images, video, audio, and more to develop artificial intelligence (AI) systems to perform tasks required by the humanities. 
  
• These systems generate statistics that can be translated into clear business value for the consequences and for business users.

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• Aside from the fact that data science is one of the highest paid and most popular fields today, it should be noted that it will be very innovative and challenging for a decade or more. 
• There will be enough data science jobs to get beautiful salaries and opportunities to grow.
• Since data science includes not only computing, but also mathematics, probability, statistics, programming, machine learning and much more, studying data science through books can help you gain a holistic view of data science.
• Understand ML Concept as well. The book contains examples of Python, but you do not need any prior knowledge of mathematics or programming languages ​​to read this book.
• This book is for beginners and covers the basics in detail. However, just reading this book is not enough, because you will check the ML and coding.

1. Head First Statistics: A Brain-Friendly Guide

1. • Like other books by Headfast, this book's tone and friendliness are excellent for dialogue and data science. 
     
   • The book covers a wide range of statistics, including average, average, mode, and standard deviation - followed by Probability, Correlation, and Regression. 
     
   • Easy to remember The pages have lots of images, graphics and bits. You can find some good real life examples to include yourself in the book. 
     
   • Overall a great book to start your data science journey.

2. Practical Statistics for Data Scientists

1. • If you are a beginner, this book will give you a good overview of all the concepts you need to learn to master data science. 
     
   • The book is not very detailed, but gives good information about all the major concepts like randomization, modeling, distribution and model bias. 
     
   • The book surprises one with a survey of ML models.
     
   • This book covers all the topics required for Data Science. 
     
   • However, this is a quick and easy reference because the lack of explanations and examples is not enough to study the concepts in depth.

2. Introduction to Probability

1. • If you have a math background at school, you may remember calculating your chances of getting a spade or heart from a pack of cards.
     
   • This is the best book to know about probability. The descriptions are very clean and similar to real life problems. 
     
   • If you have studied probability in school, this book will further enhance your knowledge of the basics. 
     
   • If you are going to study probability for the first time - this book may help you build a strong foundation on important ideas, however you will need to work with the book for a while.
     
   • This book has been one of the most popular books for almost 5 decades and this is definitely another reason to have it on your bookshelf.

2. Introduction to Machine Learning with Python: A Guide for Data Scientists

1. • This is the book that will get you started on your ML journey with Python. 
     
   • Ideas are illustrated with enough examples for better understanding as a normal human being. The voice is friendly, heartfelt and easy to understand. 
     
   • ML is a very complex subject, however, after training with the book, you can create your own ML models. 
   • You can better understand ML's comments. The book contains examples of Python, but you do not need any prior knowledge of mathematics or programming languages ​​to read this book.
   • This book is for beginners and covers the basics in detail. However, reading this book alone is not enough, as you will examine ML and coding in more depth.

2. Python Machine Learning By Example

• • As the name suggests, this book is an easy way to get into machine learning. 
    
  • The book begins with Python and Machine Learning in a comprehensive and interesting way, with some great examples such as spy email detection using Python, regression and predictions using tree-based methods

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Books to help you Master Data Science

1. Pattern recognition and machine learning

1. • This book is for everyone of all ages, whether you are a bachelor, graduate or advanced level researcher. 
     If you have a Kindle subscription, this book will cost you nothing. 
     
   • Get the international edition with colorful pictures and illustrations that will make your reading experience absolutely worthwhile.
     
   • Coming to the content, this is a book that covers the outside of machine learning. This is done by simply explaining the ideas using examples. 
     
   • Some words are difficult for some readers to understand, but you can find other resources, such as web articles or videos, using generic sources. 
     
   • The book is a must if you are serious about engaging in machine learning, especially since the mathematics (data analysis) part is done in nature.
     
   • Although you can use the book for self-study, it is best to read it along with some machine learning courses.

2. Python for data analysis

1. • As the name implies, the book covers all methods of data analysis. 
     
   • This is a good start for a beginner before going into the role of Python in data startup and statistics, and includes the basics of Python. 
     
   • The book is fast and everything is explained in a very simple way. 
     
   • You can create some original apps within a week of reading the book. 

2. Data Science and Big Data Analysis

1. • This book slowly introduces big data, how important it is in today’s digital competitive world. 
     
   • You can see the practical operation of the whole system as it describes in detail the complete data analysis along with life cycle case study and attractive conditions. The structure and flow of the book is excellent and well organized. 
     
   • Each step is like a chapter in a book so you can easily understand the big picture of how the analysis goes. 
     In the book one can illustrate simple and everyday examples using clustering, regression, association rules and more. 
     
   • It also introduces the reader to advanced analytics using MapReduce, Hadoop and SQL.

2. R for Data Science

1. • Another book for beginners who wants to learn data science with R, which explains not only the concepts of statistics, but also the data you see in real life, how to change this with concepts like average, mean, standard Deviation. 
     
   • The book will help you understand how confusing real data can be and how real it is and how it is processed. 
     
   • Data conversion is one of the most time consuming tasks, and this book will help you gain a lot of knowledge about the different methods of converting data for processing so that you can gain meaningful insights. 
     
   • If you want to learn R before you start the book, you can do it with simple online courses, however, you can start now as the book has the necessary basics.

2. Storytelling with data

• • Telling anything as a story and showing it as graphics will easily fit in our minds and stay there forever. 
    
  • This book deals with the basic concepts of data visualization to better understand how to make the best use of the vast majority of data available in the real world. 
    
  • The author's method of explaining each idea is quite unique, because he tells it in the form of a remarkable story. 
    
  • You never realize how many ideas you can get in a day reading a book - getting to know the environment and the audience better, using the right map for the right situation, identifying and removing clutter to get only important information and giving away the most important parts of the data - and much more.

You can give a guide to this book or for topics you may be missing while searching for online courses.
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