Lecture 29-Jan-2024, Session 2 Data Science and Python venv

[dudung]

• Write your student ID.
• Explain in brief about data science.
• What are the differences between data, data science, and data scientist?
• Explain about the four foundational aspects of data science?
• List link on PyPI for installing Jupyter Notebook, Matplotlib, NumPy.
• Create a virtual environment, install previous packages, and save information to requirements.txt, create other virtual environment and use requirement.txt. Show the screenshots for all processes.

[yunisangadji]

21181298

1. Data science is the study of extracting useful insights from data using scientific methods, statistical techniques, and computational algorithms. Data science is a field that combines mathematics, statistics, programming, and domain expertise to extract insights from data. It involves using machine learning, artificial intelligence, and data visualization to analyze data and make predictions.

2. Data science, on the other hand, is a more complex and process that involves working with larger, more complex datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with such as text or images and use algorithms to build predictive models and make data-driven decisions. In addition todata science often involves tasks such as and model selection. For instance, a data scientist might develop a recommendation system for an e-commerce platform by analyzing user behavior patterns and using to predict user preferences. 3.•Mathematics It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation. •Technology Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing •Visualization New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.The general principles that govern when and how to use visualizations will be studied •Communication How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.Clarity, brevity (concise), and knowing the target audience will be prioritized

3. 

4. 

[yunoooo111]

1. 21181296

2. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

3. Data is a series of facts and figures that can be used as components to organize information, and Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning. Data scientist is an analytics professional who is responsible for collecting, analyzing and interpreting data to help drive decision-making in an organization

4. •Mathematics •Technology •Visualization •Communication

5. 

[ayarayenima]

21181032

1. Data science is a multidisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It combines techniques from statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets. The goal of data science is to uncover patterns, trends, and actionable insights that can inform decision-making and drive innovation.

2. Data refers to raw facts, observations, or measurements that, when processed and analyzed, provide information. Data can be in various forms, such as text, numbers, images, or other formats. Data science is a multidisciplinary field that involves the use of scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines techniques from statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets. and A data scientist is a professional who possesses a combination of skills in statistics, mathematics, programming, and domain expertise. Data scientists leverage these skills to analyze

3. 4 Foundational aspects of data scientist A. Mathematics and statistics provide the theoretical foundation for many data science techniques. They help in understanding the underlying principles of algorithms, model building, and data analysis. B. Computer Science and Programming provides Proficiency in programming languages and computer science concepts is essential for handling and processing large volumes of data efficiently. It involves tasks such as data cleaning,
   transformation, and implementing algorithms. C. Domain knowledge refers to expertise in the specific industry or field where data science is being applied. It helps data scientists understand the context, variables, and nuances of the data they are working with. D. Communication and Visualization is crucial for making data-driven decisions. Data scientists need to convey complex results to both technical and non-technical stakeholders.

4. 

[irhwam]

Irham Prajawiguna

21181125

1. Data science is a field of applied mathematics and statistics that provides useful information based on large amounts of complex data or big data

2. Data can be defined as a systematic record of a particular quantity. It is the different values of that quantity represented together in a set. It is a collection of facts and figures to be used for a specific purpose such as a survey or analysis. Data science is considered a discipline, while data scientists are the practitioners within that field. A data scientist is an analytics professional who is responsible for collecting, analyzing and interpreting data to help drive decision-making in an organization.

3. domain knowledge, math & statistics skills, computer science, communication & visualization

[Antara000]

• Makmur Perkasa (21181161)
• Data Science is Scientific process of transforming data into insight for making better decisions, the goal is to turn data into actionable value.
• Here are the difference between Data, Data Science, and Data Scientist :

Data:

Data is like pieces of information, such as numbers or words, that we collect and use for different things. For example, numbers in a math problem or words in a story.

Data Science:

Data Science is like using special tools and techniques to understand and make sense of large amounts of data. It helps us find patterns, make predictions, and get valuable information from the data.

Data Scientist:

A Data Scientist is like a detective who uses their skills to solve problems and uncover important things from data. They work with computers and math to analyze data and help make smart decisions.

• This are 3 Foundational aspect of Data science :

Mathematics :

It will cover basic math ideas like functions, relations, assumptions, conclusions, and abstraction. This helps us use these concepts to define and understand many aspects of working with data.

There are also other math and stats courses that connect to data science. For example, using graphs to analyze social networks, using matrices to find patterns in relationships, and using supervised machine learning.

Technology :

The understanding of Python will be expanded beyond the prerequisite, incorporating more advanced table manipulation functions. This will involve additional practice in handling data cleaning and manipulation tasks, utilizing computational notebooks like Jupyter, and utilizing GitHub for version control and project publishing.

Visualization : New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.

The general principles that govern when and how to use visualizations will be studied.

How to build and publish interactive online visualizations (dashboards) will also be learnt.

Communication :

How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.

Clarity, brevity (concise), and knowing the target audience will be prioritized.

• Here is the Screenshot :

  

• Here is the step

  

[CriticalOlymp]

1. 21181064

2. Ilmu data adalah bidang yang mempelajari bagaimana mengumpulkan, membersihkan, menganalisis, dan memvisualisasikan data untuk menemukan pola dan tren yang dapat digunakan untuk membuat keputusan yang lebih baik. Ilmu data adalah bidang yang luas dan mencakup berbagai disiplin ilmu, termasuk matematika, statistika, ilmu komputer, dan kecerdasan buatan. Ilmuwan data menggunakan berbagai teknik dan algoritma untuk menganalisis data, mulai dari teknik statistik dasar hingga teknik pembelajaran mesin yang canggih.

3. Data adalah kumpulan fakta dan angka. Ilmu data adalah cara untuk memahami data dan menggunakan data untuk membuat keputusan. Ilmuwan data adalah orang yang ahli dalam ilmu data.

4. lmu data itu bagaikan rumah kokoh: butuh empat tiang penyangga agar berdiri tegak dan bertahan lama. Keempat tiang penyangga itu adalah:

Statistika: Ibarat fondasi, statistika memberikan dasar pemahaman tentang data. Kita belajar menghitung, menganalisis, dan menginterpretasi pola-pola yang tersembunyi dalam data. Misalnya, menghitung rata-rata, standar deviasi, dan korelasi untuk memahami hubungan antar variabel. Pemrograman: Seperti bahan bangunan utama, pemrograman digunakan untuk membangun alat dan model untuk menganalisis data. Bahasa seperti Python, R, dan SQL memungkinkan kita membersihkan, memanipulasi, dan memvisualisasikan data agar lebih mudah dipahami. Domain Knowledge: Ibarat keahlian arsitek, domain knowledge adalah pemahaman mendalam tentang bidang di mana data digunakan. Misalnya, untuk menganalisis data rumah sakit, kita perlu tahu istilah medis dan proses perawatan pasien. Ini membantu kita menginterpretasi data dengan akurat dan relevan. Komunikasi: Seperti jendela dan pintu, komunikasi memungkinkan kita membagikan hasil analisis data kepada orang lain. Kita perlu belajar menyajikan temuan-temuan dalam bentuk grafik, tabel, dan cerita yang jelas dan mudah dimengerti, bahkan bagi yang tidak paham teknis.

5. 

[birongzah]

21181241

1.Data science is a multidisciplinary field that involves the use of scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines expertise from various domains such as statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets. The primary goal of data science is to uncover patterns, trends, correlations, and valuable insights from data to support decision-making, predictions, and strategy formulation.

2. Data refers to raw facts and information, encompassing the values or content that can be collected, stored, and processed. Data science, on the other hand, is a multidisciplinary field that involves the use of scientific methods, algorithms, and systems to analyze and extract insights from data. It combines expertise from statistics, computer science, and domain-specific knowledge to uncover patterns, correlations, and valuable information, ultimately aiding in decision-making. A data scientist is a professional within the field of data science, possessing skills in data analysis, statistics, machine learning, and programming. Data scientists apply their expertise to interpret complex data sets, develop models, and derive actionable insights that contribute to informed decision-making and problem-solving across various industries and domains. In summary, while data is the raw information, data science is the discipline that extracts knowledge from that data, and a data scientist is the individual skilled in utilizing data science techniques to derive meaningful insights.

3. Foundational aspects of data science : Mathematics:

Mathematics forms the backbone of data science, providing the theoretical underpinnings for algorithms and statistical methods. Concepts such as linear algebra, calculus, probability, and statistics are crucial for understanding and developing data models. Mathematical principles enable data scientists to analyze patterns, make predictions, and validate hypotheses in their work. Technology:

Technology is a fundamental pillar of data science, encompassing programming languages, data storage and retrieval systems, and tools for data analysis and machine learning. Data scientists often use languages like Python or R for coding, and they work with databases, distributed computing frameworks, and big data technologies. Familiarity with these technological tools is essential for managing and processing large datasets efficiently. Visualization:

Visualization plays a critical role in data science by enabling the representation of complex data in a comprehensible manner. Data scientists use various visualization techniques and tools to create charts, graphs, and dashboards that facilitate the communication of insights. Visualization aids in uncovering patterns, trends, and outliers in the data, making it easier for stakeholders to grasp and interpret the findings. Communication:

Communication is a foundational skill for data scientists. They must be able to convey their findings, insights, and recommendations to both technical and non-technical audiences. Clear communication helps bridge the gap between data science and decision-makers, ensuring that the implications of the analysis are well-understood and can be acted upon. This involves creating reports, presenting results, and collaborating with cross-functional teams.

4.

5.

[c4t4r]

21181074

Scientific process that transforming data to an insight that can be used for making decision

data is raw ,data science is the process, data scientist is the people

Mathematics It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction Technology Data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing Visualization New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them Communication How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.Clarity, brevity (concise), and knowing the target audience will be prioritized

[afrozhie]

21181094

1. Data science is a field that combines mathematics, statistics, programming, and domain expertise to extract insights from data. Data science is the study of extracting useful insights from data using scientific methods, statistical techniques, and computational algorithms. It involves using machine learning, artificial intelligence, and data visualization to analyze data and make predictions.

2. It is a more complex and process that involves working with larger, more complex datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with such as text or images and use algorithms to build predictive models and make data-driven decisions. In addition to data science often involves tasks such as and model selection. For instance, a data scientist might develop a recommendation system for an e-commerce platform by analyzing user behavior patterns and using to predict user preferences.

3. • Mathematics It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation.
   • Technology Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing
   • Visualization New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.The general principles that govern when and how to use visualizations will be studied
   • Communication Clarity, brevity (concise), and knowing the target audience will be prioritized. How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.

4. 

[NerdyVandheka]

• 21181213

• Data science is the scientific process of transforming data into insight for making better decisions. The goal is to turn data into actionable value.

• Data is as the raw material like numbers, text, images, or any other information captured about something. It can be structured (like rows and columns in a spreadsheet) or unstructured (like social media posts). Data Science is as the toolbox: a field of study that develops techniques and algorithms to extract insights from data. It involves statistics, mathematics, computer science, and domain knowledge. Data science uses various methods like cleaning, analyzing, building models, and visualizing data to uncover patterns, trends, and relationships. Data Scientist is as the builder: a professional who applies data science tools and techniques to solve specific problems using data. They're skilled in programming, statistics, and communication. Data scientists work in various sectors like healthcare, finance, marketing, research, etc. They use their expertise to answer questions, make predictions, and optimize processes based on data analysis.

• a. Mathematics, It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation. b. Technology, Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing. c. Visualization, New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them. The general principles that govern when and how to use visualizations will be studied. d. Communication, How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.

• 

[NeganSmith92]

• 21181300

• Data Science is scientific process of transforming data into insight for making better decisions, that the goal is to turn data into actionable value

• 1. Data: Data refers to raw facts, figures, and statistics that are collected and stored for analysis. It can be in various forms, such as numbers, text, images, or any other format that represents information.

  2. Data Science: Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It involves a combination of skills from statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets. The goal of data science is to uncover patterns, trends, and valuable insights from data that can be used to inform business decisions, solve problems, or gain a competitive advantage.

  3. Data Scientist: A data scientist is a professional who possesses a combination of skills in statistics, mathematics, programming, and domain expertise. Data scientists use their skills to analyze large and complex data sets, develop algorithms, and create predictive models to extract meaningful insights. They work on identifying patterns, trends, and correlations in data to help organizations make data-driven decisions. Data scientists also play a crucial role in designing and implementing machine learning models, building data pipelines, and communicating findings to non-technical stakeholders.

• 1. Mathematics: Mathematics is the foundational element of data science, providing the theoretical basis for various algorithms and statistical methods. Statistics, linear algebra, calculus, and probability theory are essential mathematical concepts in data science. Statistical techniques help in making inferences, testing hypotheses, and understanding the uncertainty associated with data. Linear algebra is fundamental for tasks like matrix operations, which are prevalent in machine learning algorithms. Calculus is often used in optimization problems, such as adjusting model parameters to minimize errors.

  2. Technology: Technology encompasses the tools, programming languages, and platforms used to process, analyze, and manage data. Programming languages like Python and R are widely used for data manipulation, analysis, and building machine learning models. Big data technologies such as Apache Hadoop and Spark are employed to handle large-scale datasets. Database management systems (e.g., SQL and NoSQL databases) are used for data storage and retrieval. Cloud computing platforms provide scalable and cost-effective infrastructure for data storage and processing.

  3. Visualization: Visualization is the process of representing data graphically to aid in understanding patterns, trends, and insights. Data visualization tools like Matplotlib, Seaborn, and Tableau help create charts, graphs, and dashboards. Effective visualization allows data scientists to communicate complex findings in a clear and accessible manner to both technical and non-technical stakeholders. Visualization enhances exploratory data analysis and supports the interpretation of results, making it an integral part of the data science workflow.

  4. Communication: Communication is crucial for translating technical findings into actionable insights that can drive decision-making. Data scientists need to effectively communicate their results to stakeholders, including business leaders, policymakers, or other team members. The ability to convey complex concepts in a clear and concise manner is essential for ensuring that data-driven insights are understood and utilized. Communication skills also facilitate collaboration between data scientists and other professionals who may not have a technical background.

• 
• 

[tegarhermans]

TEGUH ANDHIVA P 21181284

1. concept to unify statistics, data analysis, informatics, and their related methods to understand and analyze actual phenomena with data

2. • Data is the raw information, facts, and figures collected from various sources
   • Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data
   • A data scientist is a professional who applies data science techniques and tools to solve problems and create solutions
3. • Mathematics : It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction
   • Technology : Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing
   • visualization : New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.
   • Communication : How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.

4. 

5. 

[CyberianRonin]

1. 21181229

2. Data science is a multidisciplinary field that involves the use of scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines elements from various domains such as statistics, mathematics, computer science, information theory, and domain-specific expertise to analyze and interpret complex data sets.

3. Data : Data refers to raw facts, figures, or information that can be in various forms such as numbers, text, images, or any other format. (A collection of temperature readings, a list of customer names, or a set of images.)

   Data Science : Data science is a multidisciplinary field that involves using scientific methods, processes, algorithms, and systems to extract insights and knowledge from data. It combines elements from statistics, mathematics, computer science, and domain-specific expertise. (Data science includes tasks such as data collection, preprocessing, exploratory data analysis, feature engineering, machine learning model development, and communication of results.)

   Data Scientist : A data scientist is a professional who uses a combination of statistical, mathematical, programming, and domain-specific knowledge to extract insights and knowledge from data.

4. Data Collection : Data collection involves gathering relevant and appropriate data from various sources. This may include databases, APIs, sensors, web scraping, or other methods.

   Data Cleaning and Preprocessing : Data cleaning and preprocessing involve transforming raw data into a format suitable for analysis. This includes handling missing values, removing outliers, standardizing formats, and addressing any other issues that may affect data quality.

   Exploratory Data Analysis (EDA) : Exploratory Data Analysis involves visually and statistically exploring the data to gain insights into its characteristics, patterns, and relationships. This phase helps data scientists form hypotheses and make informed decisions about subsequent

   Model Development and Evaluation : Model development involves selecting appropriate machine learning algorithms, training models on the data, and fine-tuning parameters. Model evaluation assesses the performance of these models using relevant metrics.

5. 

6. 

[narapatimulia]

21181195

1. Data science is a fascinating field that involves extracting knowledge and insights from data using a blend of various disciplines

2. The difference is : Data: The raw building blocks of information. Data Science: The field and techniques for extracting knowledge from data. Data Scientist: The professional who applies data science methods to specific problems.

3. Statistics and Probability, Programming, Machine Learning, Domain Expertise

4. 

[ayodyaadhi01]

21181017

Data science is a scientific discipline that uses various scientific methods, processes, algorithms and systems to extract insights and knowledge from data in various forms. The main goal of data science is to gain a deep understanding of the patterns, trends, and information contained in data to support decision making, future predictions, and innovation.

data is facts or information, data science is a field that includes methods for analyzing and understanding data, while data scientists are professionals who apply data science skills to extract insights from data.

1. Data collection: The first step in data science is to collect data from various sources, such as: -Internal database: Sales data, customer data, log data. -External sources: Sensor data, web data, social media data. -Surveys and experiments: Collect data directly from people.
2. Data Cleaning: Data cleansing is a process for: -Delete incomplete or inaccurate data. -Change the data format for analysis. -Addressing missing values ​​and outliers.
3. Data analysis: Various data analysis techniques can be used, such as: -Statistics: Analyze the mean, median, standard deviation, and correlation. -Machine learning: Building models to predict future value. -Data visualization: Presenting data in graphs and diagrams for easy understanding.
4. Data communication: Various data communication methods can be used, such as: -Written report: Presents the results of the analysis in a report format. -Presentation: Present the analysis results visually. -Interactive dashboard: Allows users to explore data independently.

[IZAN0R]

1. 21181085
2. Ilmu data adalah bidang ilmu yang mempelajari cara mengumpulkan, menganalisis, dan memahami data

3. Perbedaan data, data science, and data scientist -Definisi Data : Fakta dan angka mentah Data science : Bidang ilmu Data scientist : Ahli dalam ilmu data -Fungsi Utama Data : Menyimpan informasi Data science : Mengekstrak pengetahuan Data scientist : Memecahkan masalah -Sifat Informasi Data : Bisa tidak terstruktur, semi-terstruktur, terstruktur Data science : Bisa terstruktur, semi-terstruktur, tidak terstruktur Data scientist : Bisa terstruktur, semi-terstruktur, tidak terstruktur -Contoh Data : Angka penjualan, teks dokumen, gambar Data science : Memprediksi permintaan produk Data scientist : Mendeteksi penipuan

   4. Domain Knowledge, Programming and Data Wrangling, Statistics and Machine Learning, Communication and Storytelling

   5. 

   6. 

[MazamGanendra]

1). 21181192

2). Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. Data science combines math and statistics, specialized programming, advanced, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

3). Data is the raw material, the unprocessed information that exists in various forms such as numbers, text, images, or any other format. It serves as the foundation for the broader field of data science. Data science is a multidisciplinary domain that employs scientific methods, algorithms, and systems to extract insights and knowledge from both structured and unstructured data. It encompasses statistical analysis, programming, machine learning, data cleaning, preprocessing, and data visualization. A data scientist is a professional within the field of data science. They bring together skills in statistics, mathematics, programming, and domain knowledge to analyze and interpret complex datasets. In summary, data is the raw information, data science is the field that utilizes various methods to extract knowledge from data, and a data scientist is the skilled professional who applies these methods to analyze data and contribute to informed decision-making within organizations.

4). Foundational aspects of data science, is Mathematics, Technology, Visualization, and Communication A. Mathematics, It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation B. Technology, python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing. C. Visualization, new types of plots will be learnt for a wide variety of data types and what you intend to communicate about them. The general principles that govern when and how to use visualizations will be studied. How to build and publish interactive online visualizations (dashboards) will also be learnt. D. Communication, how to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses. Clarity, brevity (concise), and knowing the target audience will be prioritized.

5). 1. Install Jupyter notebook

2. Install Matplotlib

3. Install NumPy

Install Notebook

6).

[robinhuud15]

21181234

1. Ilmu data adalah bidang interdisipliner yang menggunakan metode ilmiah, proses, algoritme, dan sistem untuk mengekstrak pengetahuan dan wawasan dari data dalam berbagai bentuk, baik terstruktur maupun tidak terstruktur.

2. -Data science adalah bidang ilmu yang mempelajari cara mengekstrak pengetahuan dari data. -Data scientist adalah profesional yang menerapkan ilmu data untuk menyelesaikan masalah bisnis. -Data science adalah bidang yang lebih luas, sedangkan data scientist adalah profesi yang lebih spesifik. -Data science berfokus pada metodologi, teknik, dan alat, sedangkan data scientist berfokus pada penerapan ilmu data. -Data science adalah ilmu, sedangkan data scientist adalah seorang praktisi.

3.-Mathematics -Technology -Visualization -Communication

4.

5. 

[bahrudinz]

21181177

(1) Data Science is scientific process of transforming data into insight for making better decisions, that the goal is to turn data into actionable value

(2) Data: General term: Refers to raw or processed information collected from various sources. Types: Structured (databases), unstructured (text, images), semi-structured (JSON) Focus: The information itself, without interpretation or analysis

Data Science: Interdisciplinary field: Combines statistics, computer science, mathematics, and domain knowledge Objective: Extract insights and knowledge from data to solve problems and make informed decisions Methods: Machine learning, statistics, data visualization, modeling, algorithms

Data Scientist: Professional role: Applies data science methods to analyze and interpret data Skills: Programming, statistics, machine learning, communication, problem-solving Tasks: Collect, clean, analyze, visualize data, build models, communicate findings, make recommendations

Key differences: Level of abstraction: Data is the raw material, data science is the process, and data scientist is the person applying the process. Focus: Data focuses on the information itself, data science focuses on extracting insights, and data scientist focuses on applying methods and solving problems. Skills: Data requires no specific skills, data science requires a mix of technical and analytical skills, and data scientist requires expertise in applying those skills.

(3) Four Foundational aspects of data scientist : A. Mathematics and statistics provide the theoretical foundation for many data science techniques. They help in understanding the underlying principles of algorithms, model building, and data analysis. B. Computer Science and Programming provides Proficiency in programming languages and computer science concepts is essential for handling and processing large volumes of data efficiently. It involves tasks such as data cleaning, transformation, and implementing algorithms. C. Domain knowledge refers to expertise in the specific industry or field where data science is being applied. It helps data scientists understand the context, variables, and nuances of the data they are working with. D. Communication and Visualization is crucial for making data-driven decisions. Data scientists need to convey complex results to both technical and non-technical stakeholders.

(4)

(5)

[charlesmana]

21181068

1. Data science is the study of extracting useful insights from data using scientific methods, statistical techniques, and computational algorithms. It involves using machine learning, artificial intelligence, and data visualization to analyze data and make predictions.
2. Difference data science and data scientists
   • Data science, on the other hand, is a more complex and process that involves working with larger, more complex datasets that often require advanced computational and statistical methods to analyze.
   • Data scientists often work with such as text or images and use algorithms to build predictive models and make data-driven decisions. Data scientist might develop a recommendation system for an e-commerce platform by analyzing user behavior patterns and using to predict user preferences.
3. four foundational aspects of data science •Mathematics It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation. •Technology Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing •Visualization New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.The general principles that govern when and how to use visualizations will be studied •Communication How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.Clarity, brevity (concise), and knowing the target audience will be prioritized
4. 

![Uploading Screenshot 2024-01-29 223921.png…]()

[heksamolako25]

21181117

1. Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines expertise from various domains, including statistics, mathematics, computer science, and domain-specific knowledge, to analyze and interpret complex data sets. The primary goal of data science is to uncover patterns, trends, and valuable information that can be used to make informed decisions and predictions.

2. Data:

Definition: Data refers to raw facts, figures, and observations that are collected or stored for reference, analysis, or future use. Types of Data: Data can be categorized as structured (organized in a tabular form, like a database), semi-structured (partially organized, like JSON or XML files), or unstructured (lacks a predefined data model, like text documents or images). Data Science:

Definition: Data science is an interdisciplinary field that involves the extraction of insights and knowledge from data using scientific methods, processes, algorithms, and systems. Activities: Data science encompasses various activities, including data collection, cleaning, exploration, feature engineering, modeling, evaluation, and interpretation of results. Goal: The primary goal of data science is to uncover patterns, trends, and meaningful information that can be used for decision-making and predictive analysis. Data Scientist:

Definition: A data scientist is a professional who applies their expertise in statistics, mathematics, programming, and domain-specific knowledge to analyze and interpret complex data sets. Skills: Data scientists typically possess a combination of skills in programming (e.g., Python, R), statistics, machine learning, data visualization, and domain knowledge relevant to the industry they work in. Responsibilities: Data scientists are responsible for designing and implementing models, extracting insights, and communicating findings to stakeholders. They may also be involved in data engineering tasks and ensuring the ethical use of data.

3. Four Foundational aspects of data scientist : A. Mathematics and statistics provide the theoretical foundation for many data science techniques. B. Computer Science and Programming provides Proficiency in programming languages and computer science concepts is essential for handling and processing large volumes of data efficiently. C. Domain knowledge refers to expertise in the specific industry or field where data science is being applied. D. Communication and Visualization is crucial for making data-driven decisions.

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[Ptolemu5]

Murrobi F 21181203

1. Data science is a multidisciplinary field that involves the extraction of knowledge and insights from structured and unstructured data. It combines expertise from various domains, including statistics, computer science, mathematics, and domain-specific knowledge, to analyze and interpret complex data sets. The primary goal of data science is to uncover valuable information, patterns, and trends that can inform decision-making and strategy in various industries.

2. Data: Definition: Data refers to raw facts, figures, and observations that are collected and stored. It can be in the form of numbers, text, images, audio, or any other format. Characteristics: Data can be categorized as structured (organized in a specific format, like a table in a database) or unstructured (lacking a predefined data model, like text documents or images). Data Science: Definition: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Scope: It involves various activities such as data cleaning, exploration, analysis, visualization, and the development of predictive models to make informed decisions and solve complex problems. Skills Needed: Data science requires expertise in statistics, mathematics, programming, and domain-specific knowledge. Data Scientist: Role: A data scientist is a professional who applies scientific methods, algorithms, and systems to extract knowledge and insights from data. They are responsible for analyzing and interpreting complex data sets to inform business decisions, identify trends, and develop predictive models. Skills: Data scientists need a combination of skills in programming (e.g., Python, R), statistical analysis, machine learning, data visualization, and domain-specific knowledge.

3. The four foundational aspects of data science encompass key elements that are crucial for effectively extracting insights and knowledge from data. These aspects provide a framework for the data science process. Here are the four foundational aspects: Data Collection and Storage: Definition: This aspect involves gathering relevant data from various sources and storing it in a structured manner. Data can be collected from databases, APIs, sensors, logs, and other sources. Importance: High-quality data is essential for accurate and meaningful analyses. Data scientists need to ensure that the collected data is relevant, comprehensive, and properly stored to facilitate efficient retrieval. Data Cleaning and Preprocessing: Definition: Data cleaning involves identifying and correcting errors, inconsistencies, and missing values in the dataset. Preprocessing includes transforming raw data into a format suitable for analysis, such as scaling, encoding categorical variables, and handling outliers. Importance: Clean and well-preprocessed data is essential for accurate and reliable analysis. Errors or inconsistencies in the data can lead to misleading results and conclusions. Exploratory Data Analysis (EDA): Definition: EDA involves examining and visualizing the data to understand its characteristics, uncover patterns, and identify potential relationships between variables. This step helps data scientists form hypotheses and guide further analysis. Importance: EDA is crucial for gaining insights into the structure of the data and identifying key patterns or trends. Visualization techniques, summary statistics, and exploratory techniques help in understanding the data before applying complex models. Model Building and Evaluation: Definition: Model building involves selecting and training appropriate algorithms to make predictions or identify patterns in the data. Evaluation assesses the model's performance using metrics like accuracy, precision, recall, or others, depending on the task. Importance: Building accurate models is the core of data science. Models should be selected based on the nature of the problem (classification, regression, clustering, etc.) and evaluated to ensure their effectiveness in making predictions or uncovering patterns in new data.

[AKS4R4]

21181236

1. Data science is a multidisciplinary field that involves the use of scientific methods, processes, algorithms, and systems to extract meaningful insights and knowledge from structured and unstructured data. It combines expertise from various domains such as statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets.

2. data is the raw material, data science is the process of extracting insights from that data, and a data scientist is the individual who performs the analysis and interprets the results.

3. Mathematics and Statistics: Purpose: Use math and stats to analyze and make sense of data. Example: Understanding probabilities, averages, and patterns in data.

Programming and Computer Science: Purpose: Use computer skills to work with big datasets. Example: Writing code in languages like Python to process and analyze data.

Domain Knowledge: Purpose: Know about the specific field you're working in. Example: If working in healthcare, understanding medical concepts and terminology.

Communication and Visualization: Purpose: Share findings in a way others can understand. Example: Creating charts or graphs to illustrate data trends, and explaining results to non-experts.

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