AI based data science
.png)
Shall Chat GPT impact on data science job?
That's an interesting question. ChatGPT is an artificial intelligence chatbot developed by OpenAI that can generate natural language responses based on a given context. It can also perform various tasks such as code generation, data analysis, and content creation.
According to some experts, ChatGPT and similar AI tools could potentially disrupt some jobs, especially those that involve routine or repetitive tasks, such as data entry, customer service, or writing²⁴. However, this does not mean that data science jobs are obsolete or doomed. Data science is a complex and dynamic field that requires creativity, critical thinking, and problem-solving skills. Data scientists can use ChatGPT as a productivity-enhancing tool to assist them in their work, but they still need to apply human judgement and expertise to ensure the quality and accuracy of the outputs¹³.
Therefore, ChatGPT may not impact all data science jobs equally. Some data science jobs may be more at risk than others depending on the level of skill and creativity required. For example, data analysts who focus on simple or standardized tasks may be more vulnerable to automation than data scientists who work on more advanced or customized projects². Data scientists who use ChatGPT to generate code or insights may also need to verify the results and explain the logic behind them.
In summary, ChatGPT is a powerful AI tool that can help data scientists with various aspects of their work, but it cannot replace them entirely. Data science is still a valuable and in-demand skill that requires human intelligence and creativity. Data scientists should embrace ChatGPT as a potential ally rather than a threat.
Source: Conversation with Bing, 9/17/2023
(1) ChatGPT: the 10 Jobs Most at Risk of Being Replaced by AI..
(2) ChatGPT Guide for Data Scientists: Top 40 Most Important Prompts - Medium..
(3) ChatGPT - Are Data Science Jobs Now Obsolete?. .
(4) A Guide to Using ChatGPT For Data Science Projects | DataCamp..
(5) OpenAI: ChatGPT Could Disrupt 19% of US Jobs, Is Yours on the List .....
What is the future of data science jobs?
The future of data science jobs is bright and promising, according to various sources. Data science is a rapidly expanding and evolving field that is in high demand across various industries and sectors. Data scientists can leverage their skills in data collection, analysis, modeling, and reporting to solve complex problems and generate insights that can benefit businesses and society.
According to the U.S. Bureau of Labor Statistics, employment in data science will grow by "31 percent" in the coming decade, which means employers will create more than "10,000 new jobs" for data scientists³. They predict data science will see more growth than almost any other field between now and 2029². The demand for data science skills will also increase by "27.9 percent" by 2026⁴.
Some of the top data science jobs of the future include:
Data Scientist: A data scientist is someone who applies advanced analytical techniques to extract insights from large and complex datasets. They use machine learning, artificial intelligence, and other tools to create predictive models and solutions that can help businesses make better decisions.
Data Engineer: A data engineer is someone who builds and maintains the infrastructure that processes raw data into something usable by data scientists and analysts. They use programming languages, databases, cloud computing, and other technologies to design, develop, and manage data pipelines and systems.
Data Analyst: A data analyst is someone who collects, organizes, and interprets data to answer specific questions or provide recommendations. They use statistical methods, visualization tools, and business logic to communicate their findings and insights to stakeholders.
Data Visualization Specialist: A data visualization specialist is someone who creates graphical representations of data to make it easier to understand and communicate. They use tools such as charts, graphs, maps, dashboards, and reports to present data in an engaging and informative way.
Machine Learning Engineer: A machine learning engineer is someone who develops and deploys machine learning models that can learn from data and perform tasks such as classification, regression, clustering, recommendation, etc. They use frameworks such as TensorFlow, PyTorch, Scikit-learn, etc., as well as cloud platforms such as AWS or Azure.
These are just some examples of the many roles that exist in the field of data science. As the field continues to grow and innovate, new roles may emerge or existing ones may change over time. Therefore, it is important for anyone interested in pursuing a career in data science to keep learning new skills and stay updated on the latest trends and developments in the field.
Source: Conversation with Bing, 9/17/2023
(1) Job Forecast for Data Scientists Still Bright - School of Data Science..
(2) Predictions On The Future Of Data Science - Forbes./.
(3) Is Data Science a Good Career? (2023 Guide) | BrainStation®..
(4) The Top Data Science Jobs of the Future | DataCamp..
(5) Data Science Jobs that are set to be in demand in future..
What are some skills required for data science jobs?
Some of the skills required for data science jobs are:
- Programming: Data scientists need to be proficient in programming languages such as Python, R, SQL, SAS, etc., that can help them manipulate, analyze, and visualize data. Programming skills also enable data scientists to use various tools and frameworks for data science, such as TensorFlow, PyTorch, Scikit-learn, etc.¹²
Statistics and probability: Data scientists need to have a solid foundation in statistics and probability to understand the concepts and methods behind data analysis. Statistics and probability help data scientists perform tasks such as hypothesis testing, regression, clustering, classification, etc., as well as interpret the results and draw conclusions.¹²
Data wrangling and database management: Data scientists need to be able to clean, organize, and transform data from various sources into a suitable format for analysis. They also need to be familiar with database management systems such as MySQL, MongoDB, Oracle, etc., that can store and retrieve large amounts of data efficiently.¹²
Machine learning and deep learning: Data scientists need to have a good understanding of machine learning and deep learning techniques that can learn from data and perform tasks such as prediction, recommendation, anomaly detection, etc. They also need to be able to choose the appropriate algorithms and models for different problems and evaluate their performance.¹²
Data visualization: Data scientists need to be able to create graphical representations of data that can make it easier to understand and communicate. They use tools such as charts, graphs, maps, dashboards, reports, etc., to present data in an engaging and informative way.¹²
Communication skills: Data scientists need to be able to communicate their findings and insights effectively to various audiences such as business analysts, managers, clients, etc. They use clear language, visual aids, storytelling techniques, etc., to convey their message in a concise and compelling way.³⁴
Teamwork skills: Data scientists work collaboratively with other professionals such as business analysts, software engineers, domain experts, etc., who may have different perspectives or expertise. They need to be able to share their ideas and feedback constructively with others and respect different opinions.³⁴
Curiosity skills: Data scientists are always eager to learn new things and explore new possibilities with data. They need to have a curious mindset that drives them to ask questions, seek answers, experiment with different approaches, etc.³⁴
Persistence skills: Data science is a challenging field that requires patience and perseverance. Data scientists may face difficulties or failures along the way but they should not give up easily. They should learn from their mistakes and keep trying until they achieve their goals.³⁴
These are some of the most common skills required for data science jobs but there may be others depending on the specific role or industry. Therefore it is important for anyone interested in pursuing a career in data science to keep learning new skills and stay updated on the latest trends and developments in the field.
Source: Conversation with Bing, 9/17/2023
(1) 7 Skills Every Data Scientist Should Have | Coursera..
(2) 15 Top Skills for Data Scientists (With Job Duties) | Indeed.com..
(3) Your Guide to Data Science Careers (+ How to Get Started)..
(4) How to Become a Data Scientist | Coursera..
(5) 16 Must-Have Data Scientist Skills To Start (or Grow) Your Career. .
What are some common interview questions for data science jobs?
Some of the common interview questions for data science jobs are:
What is data science?: This question tests your basic understanding of what data science is and what it involves. A possible answer is: Data science is the interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data.¹²
What is the difference between data science and data analytics?:
This question tests your knowledge of the terminology and scope of data science and data analytics. A possible answer is: Data science is a broader term that encompasses the entire lifecycle of data analysis, from collecting and cleaning data to building and deploying models. Data analytics is a more narrow term that focuses on the analysis of specific problems or questions using statistical or machine learning techniques.¹²
Some of the essential skills and attributes that a data scientist needs: This question tests your self-awareness and self-evaluation as a potential data scientist. A possible answer is: Some of the essential skills and attributes that a data scientist needs are: programming, statistics, probability, machine learning, deep learning, data visualization, communication, teamwork, curiosity, persistence, etc.¹²³
Some of the tools and frameworks that you use for data science: This question tests your familiarity and proficiency with various tools and frameworks that are commonly used for data science. A possible answer is: Some of the tools and frameworks that I use for data science are: Python, R, SQL, SAS, TensorFlow, PyTorch, Scikit-learn, etc.¹²³
Approach to a new or complex problem: This question tests your problem-solving skills and analytical thinking as a data scientist. A possible answer is: I approach a new or complex problem by following these steps: 1) Define the problem clearly and identify the objectives and constraints. 2) Gather relevant information from various sources such as literature review, domain experts, stakeholders, etc. 3) Explore different approaches or methods to solve the problem using existing tools or frameworks. 4) Evaluate the performance and feasibility of each approach using appropriate metrics or criteria. 5) Choose the best approach or method based on the evaluation results. 6) Implement the chosen approach or method using code or scripts. 7) Test and debug the solution using various techniques such as unit testing, debugging tools, etc. 8) Document and communicate the solution using clear language, visual aids, etc.⁴
Handle missing or incomplete data in your analysis: This question tests your attention to detail and quality control as a data scientist. A possible answer is: I handle missing or incomplete data in my analysis by following these steps: 1) Identify the source and type of missing or incomplete data using descriptive statistics or visualizations. 2) Check if there are any patterns or trends in missingness such as clustering or outliers. 3) Apply appropriate techniques to deal with missingness such as imputation (using mean or median values), deletion (removing rows), interpolation (using linear regression), etc. 4) Evaluate the impact of missingness on the accuracy or validity of my analysis using metrics such as mean squared error (MSE), root mean squared error (RMSE), coefficient of determination (R-squared), etc.⁴
Explain your results to non-technical audiences: This question tests your communication skills and storytelling abilities as a data scientist. A possible answer is: I explain my results to non-technical audiences by following these steps: 1) Define the key concepts and terms related to my analysis such as variables, parameters, models, metrics, etc. 2) Use simple language and examples to illustrate how my results relate to my objectives or questions. 3) Use visual aids such as charts, graphs, maps
Source: Conversation with Bing, 9/17/2023
(1) 87 Commonly Asked Data Science Interview Questions - Springboard. .
(2) 37 Data Science Interview Questions and Sample Answers..
(3) Top Data Scientist Interview Questions and Tips | Coursera..
(4) 20 Common Data Science Interview Questions - Forage..
(5) Top 100+ Data Science Interview Questions in 2023 - Intellipaat./.
Some of the common data science project
Building Chatbots: Chatbots are conversational agents that can interact with humans using natural language. They can be used for various purposes such as customer service, entertainment, education, etc. To build a chatbot, you need to use natural language processing techniques such as tokenization, stemming, lemmatization, sentiment analysis, etc., and machine learning techniques such as recurrent neural networks, decision trees, support vector machines, etc.¹
Credit Card Fraud Detection: Credit card fraud detection is the process of identifying and preventing fraudulent transactions using credit cards. It involves analyzing various features such as transaction amount, location, time, device, etc., and applying machine learning algorithms such as logistic regression, decision trees, random forests, etc., to classify transactions as fraudulent or not.¹
Fake News Detection: Fake news detection is the process of identifying and verifying the authenticity of news articles using various sources of information such as metadata, social media posts, fact-checking websites, etc. It involves using natural language processing techniques such as text preprocessing, topic modeling, named entity recognition, sentiment analysis, etc., and machine learning techniques such as support vector machines
Source: Conversation with Bing, 9/17/2023
(1) Top 15 Data Science Projects With Source Code - InterviewBit. .
(2) Data Science Projects for Beginners and Experts | Built In..
(3) Top 6 Data Science Projects for Beginners to Get Hired in 2023..
(4) 4 Data Science Portfolio Projects You Need to Create..
(5) 20 Data Science Projects with Source Code for Beginners - Dataquest./.
Some of the common data science tools are:
Python: Python is a popular programming language that is widely used for data science. It has a simple and elegant syntax, a large and active community, and a rich set of libraries and frameworks for various data science tasks. Some of the most popular Python libraries for data science are: pandas, numpy, matplotlib, scikit-learn, tensorflow, etc.¹²
R: R is another programming language that is widely used for data science. It has a powerful and flexible syntax, a large and active community, and a rich set of packages and tools for various data science tasks. Some of the most popular R packages for data science are: dplyr, ggplot2, caret, shiny, etc.¹²
Jupyter Notebook: Jupyter Notebook is an interactive web-based interface that allows users to create and share documents that contain code, text, images, and other elements. It supports multiple programming languages such as Python, R, Julia, etc., and allows users to run code cells in an online environment. Jupyter Notebook is useful for data science because it enables users to explore data interactively, visualize results easily, and document their work clearly.¹²
SQL: SQL stands for Structured Query Language and it is a standard language for accessing and manipulating relational databases. SQL allows users to perform various operations on tables such as creating, querying, updating, deleting, etc. SQL is useful for data science because it enables users to store and retrieve large amounts of structured data efficiently.¹²
NoSQL: NoSQL stands for Not Only SQL and it refers to various types of databases that do not use SQL as their query language. NoSQL databases are designed to handle large volumes of unstructured or semi-structured data such as JSON documents or graphs. NoSQL databases are useful for data science because they enable users to store and process complex or diverse types of data flexibly.¹²
The Command Line: The command line is a text-based interface that allows users to execute commands on their computer or terminal. The command line is useful for data science because it enables users to perform various tasks such as installing software packages
Source: Conversation with Bing, 9/17/2023
(1) 15 Must-Know Data Science Tools for Beginners (2023) - Dataquest./.
(2) The most common data science tools - leportella.com. .
(3) 19 Popular Data Science Tools Used by Professionals - Indeed..
(4) 9 best data science tools and software of 2023 - TechRepublic. .
(5) Top 25 Data Science Tools To Use in 2023 - KnowledgeHut..
How do I learn Python for data science?
Python is a popular and powerful programming language that is widely used for data science. To learn Python for data science, you can follow these steps:
Step 1: Learn Python fundamentals**: You should have a solid foundation in Python programming basics, such as variables, data types, operators, control structures, functions, etc. You can learn Python fundamentals from various online courses, such as [this one]() from Coursera or [this one]() from Dataquest.¹²
Step 2: Practice with hands-on learning**: You should practice your Python skills by working on various projects that involve data analysis and manipulation. You can use online platforms such as [Kaggle]() or [Jupyter Notebook]() to find datasets and notebooks that suit your interests and goals. You can also use online courses or bootcamps that provide hands-on learning opportunities, such as [this one]() from DataCamp or from Udemy.¹²
Step 3: Learn Python data science libraries**: You should learn how to use various Python libraries and frameworks that are commonly used for data science, such as pandas, numpy, matplotlib, scikit-learn, tensorflow, etc. These libraries provide various tools and functions that can help you perform tasks such as data cleaning, exploration, visualization, modeling, etc. You can learn Python data science libraries from online courses or tutorials, such as [this one]() from YouTube Dataquest.¹²
Step 4: Build a data science portfolio as you learn Python**: You should showcase your Python skills and projects by creating a portfolio that demonstrates your abilities and achievements. A portfolio can help you stand out from other candidates and impress potential employers or clients. You can create a portfolio by using online platforms such as GitHub or Medium to host your code and write about your projects. You can also use online courses or bootcamps that provide portfolio guidance or feedback, such as [this one]
Step 5: Apply advanced data science techniques**: You should challenge yourself by learning more advanced techniques and concepts that are relevant for data science, such as machine learning, deep learning, natural language processing
Source: Conversation with Bing, 9/17/2023
(1) Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)..
(2) Python Machine Learning Tutorial (Data Science)..
(4) Introduction to Data Science in Python | Coursera..
(5) How to Learn Python for Data Science in 5 Steps - Dataquest./.
(6) How to Learn Python for Data Science?.
What is the difference between supervised and unsupervised learning in machine learning?
The difference between supervised and unsupervised learning in machine learning is:
Supervised learning: uses labeled data as input and output. It learns from the feedback of the correct answers or labels. It can be used for classification or regression problems. Some examples of supervised learning algorithms are linear regression, logistic regression, decision trees, support vector machines, etc.¹²
Unsupervised learning: uses unlabeled data as input and does not have any feedback. It learns from the patterns or structures in the data without any guidance. It can be used for clustering, association, or dimensionality reduction problems. Some examples of unsupervised learning algorithms are k-means clustering, principal component analysis, association rules, etc.¹²
Some of the advantages and disadvantages of supervised and unsupervised learning are:
Supervised learning: has more advantages than disadvantages. It can provide more accurate and reliable results than unsupervised learning. It can also handle complex and nonlinear problems better than unsupervised learning. However, supervised learning requires a lot of labeled data and computation time. It also cannot handle missing or noisy data well.¹²
Unsupervised learning: has fewer advantages than disadvantages. It can provide more flexibility and creativity than supervised learning. It can also handle large and diverse data better than supervised learning. However, unsupervised learning cannot provide any feedback or guidance to the model. It also cannot handle ambiguous or imprecise problems well.¹²
Source: Conversation with Bing, 9/17/2023
(1) Supervised and Unsupervised learning - GeeksforGeeks./.
(2) Supervised vs. Unsupervised Learning: What’s the Difference?. .
(3) Supervised and Unsupervised Learning in Machine Learning - Simplilearn..
(4) Supervised vs. Unsupervised Learning in Machine Learning | Springboard./.
(5) What Is Unsupervised Learning? - Built In..
What are some common data science algorithms?
Some of the common data science algorithms are:
Linear Regression: Linear regression is a supervised learning algorithm that is used for predicting the value of a continuous dependent variable by using the values of one or more independent variables. It assumes that the relationship between the variables is linear and can be represented by a straight line. The equation of linear regression is y = b0 + b1x, where y is the dependent variable, x is the independent variable, b0 is the intercept, and b1 is the slope.¹²
Logistic Regression: Logistic regression is a supervised learning algorithm that is used for predicting the probability of a binary outcome (such as yes/no, 0/1, etc.) by using the values of one or more independent variables. It assumes that the relationship between the variables is logistic and can be represented by a sigmoid function. The equation of logistic regression is P(x) = e^(b0 + b1x), where P(x) is the probability of x, b0 and b1 are the coefficients, and e is the base of natural logarithm.¹²
Decision Trees: Decision trees are unsupervised learning algorithms that are used for making predictions or classifications by splitting the data into smaller subsets based on certain criteria. They can handle both numerical and categorical data and can provide interpretable results. The structure of a decision tree consists of nodes and branches. A node represents a feature or an attribute, and a branch represents a decision or a split condition.¹²
Naive Bayes: Naive Bayes is an unsupervised learning algorithm that is used for making predictions or classifications by applying Bayes' theorem to calculate the probability of each class given some features. It assumes that the features are independent given each other and follow a multinomial distribution. The equation of naive Bayes is P(y|x) = (n_c * C(x) * P(x|y)) / (n_y * C(y)), where P(y|x) is the probability of class y given feature x, n_c and n_y are the number of classes and observations respectively, C(x) and C(y) are the conditional probabilities of class y given feature x.¹²
KNN: KNN stands for k-nearest neighbors and it is an unsupervised learning algorithm that is used for making predictions or classifications by finding k most similar observations to a given observation based on some distance measure. It assumes that each observation has k neighbors with similar features and can be predicted by voting on their labels. The equation of k-nearest neighbors is P(y|x) = k * max(P(y|k1), P(y|k2), ..., P(y|kn)), where P(y|x) is
Source: Conversation with Bing, 9/17/2023
(1) Top 10 Data Science Algorithms You Must Know About./.
(2) Breaking Down the Top Data Science Algorithms + Methods. .
(3) Machine Learning Algorithms for Data Science - Intellipaat.
(4) Top 10 Machine Learning Algorithms With Python & R Code - Analytics Vidhya./.
(5) Top Algorithms/Data Structures/Concepts every computer science ... - Medium..
How to learn data science by myself on the basis of online absolutely free ?
Learning data science by yourself online for free is possible, but it requires dedication, motivation, and curiosity. Here are some steps you can follow to learn data science online for free:
Step 1: Define your goals and interests**: Before you start learning data science, you should have a clear idea of what you want to achieve and why. Data science is a broad and diverse field that covers many topics and applications. You should choose a specific area or problem that interests you and aligns with your career goals. For example, you might want to learn data science for business, health care, education, or entertainment.¹²
Step 2: Choose your learning resources**: There are many online platforms and courses that offer free data science courses or certifications. You can choose from various topics such as Python, R, SQL, statistics, machine learning, data visualization, etc. You can also choose from various formats such as videos, articles, podcasts, blogs, etc. Some of the popular online platforms and courses are Coursera¹, Udacity³, Great Learning⁴, edX¹, DataCamp¹, etc.
Step 3: Follow a structured curriculum**: To learn data science effectively and efficiently, you should follow a structured curriculum that covers the essential concepts and skills. You can find many online courses or bootcamps that provide a comprehensive and rigorous curriculum for data science. You can also create your own curriculum based on your preferences and pace. Some of the common steps in a data science curriculum are:
- Learn the basics of programming languages such as Python or R
- Learn the basics of statistics and probability
- Learn the basics of data manipulation and analysis using tools such as pandas or numpy
- Learn the basics of machine learning and deep learning using frameworks such as TensorFlow or PyTorch
- Learn the basics of data visualization using tools such as matplotlib or seaborn
- Learn how to apply data science techniques to real-world problems using datasets from various domains
Step 4: Practice your skills**: The best way to learn data science is by doing it. You should practice your skills by working on various projects that involve data collection, processing, analysis, modeling, and presentation. You can use online platforms such as Kaggle¹ or Jupyter Notebook¹ to find datasets and notebooks that suit your interests and goals. You can also use online courses or bootcamps that provide hands-on learning opportunities
Source: Conversation with Bing, 9/17/2023
(1) Best Free Courses Data Science Courses & Certifications [2023 .....
(2) How to Learn Data Science from Scratch - Harvard Business School Online..
(3) Teach Yourself Data Science: the learning path I used to get an .... .
(4) Free Data Science Courses - Great Learning..
Required device configuration for data science both mobile and laptop
The device configuration for data science depends on your personal preferences, budget, and needs. However, some general guidelines are:
Processor (CPU): You should have a processor that can handle multiple tasks and parallelism efficiently. The recommended CPU platforms are Intel's Xeon W or AMD's Threadripper Pro, which offer high core counts, excellent memory performance and capacity, and large numbers of PCIe lanes. The recommended number of cores is 32 or more.¹
Video Card (GPU): You should have a video card that can accelerate the computation and visualization of data science tasks. The recommended GPU platforms are NVIDIA's GeForce RTX or AMD's Radeon RX, which offer high performance, memory bandwidth, and support for various frameworks such as TensorFlow or PyTorch. The recommended number of GPUs is 1 or more.¹
Memory (RAM): You should have enough memory to store and process large amounts of data without slowing down your system. The recommended amount of RAM is 16 GB or more.²³
Storage (Drives): You should have enough storage to store your data locally or remotely. The recommended amount of storage is 512 GB SSD (Solid State Drive) or more.²³
Operating System: You should have an operating system that is compatible with your hardware and software requirements. The recommended operating systems are Windows 10 or macOS, which offer stability, security, and support for various data science tools and frameworks.²³
Some examples of devices that meet these requirements are:
MacBook Pro: A laptop from Apple that offers a powerful processor (Intel Core i7 11th Gen), a high-performance GPU (NVIDIA GeForce RTX 3050 Ti), 16 GB RAM, 512 GB SSD storage, and macOS operating system.⁴
Dell XPS 17: A laptop from Dell that offers a powerful processor (Intel Core i7 11th Gen), a high-performance GPU (NVIDIA GeForce RTX 3050 Ti), 16 GB RAM, 512 GB SSD storage, and Windows 10 operating system.⁴
Lenovo ThinkPad X1 Carbon: A laptop from Lenovo that offers a powerful processor (Intel Core i7 11th Gen), a high-performance GPU (NVIDIA GeForce RTX 3050 Ti), 16 GB RAM, 512 GB SSD storage, and Windows 10 operating system.⁴
Source: Conversation with Bing, 9/17/2023
(1) Hardware Recommendations for Data Science | Puget Systems./.
(2) My Computer Setup for Data Science (Apps, Programs, Software). .
(3) 14 Best Laptops for Data Science (September 2023)./.
(4) How Much Ram Is Required for Data Science? (2022 Answer)./.
(5) How to Setup a Data Science Environment | DataCamp..
Some useful apps of a data scientist
There are many useful apps of a data scientist, depending on their needs, preferences, and goals. However, some of the common apps that can help a data scientist are:
Programming languages: A data scientist should be proficient in at least one programming language that can handle large and complex data sets, such as Python, R, SQL, etc. These languages can be used to manipulate, analyze, visualize, and model data using various tools and frameworks.¹²
Data analysis tools: A data scientist should be familiar with various tools that can help them perform data analysis tasks such as cleaning, exploring, transforming, and modeling data. Some of the popular tools are pandas, numpy, matplotlib, seaborn, scikit-learn, tensorflow, etc.¹²
Data visualization tools: A data scientist should be able to create graphical representations of data that can make it easier to understand and communicate. Some of the popular tools are Tableau, PowerBI, Bokeh, Plotly, Infogram, etc.¹³
Cloud platforms: A data scientist should be able to use cloud platforms that can provide scalable and reliable storage and processing of large amounts of data. Some of the popular cloud platforms are AWS⁴, Azure⁴, Google Cloud Platform⁴, etc.
Machine learning frameworks: A data scientist should be able to use machine learning frameworks that can help them build and deploy machine learning models using various algorithms and techniques. Some of the popular frameworks are TensorFlow¹², PyTorch¹², Keras¹², etc.
Deep learning frameworks: A data scientist should be able to use deep learning frameworks that can help them build and deploy deep learning models using various architectures and layers. Some of the popular frameworks are TensorFlow¹², PyTorch¹², Keras[^1
Source: Conversation with Bing, 9/17/2023
(1) 25 Top Data Science Applications & Examples to Know | Built In..
(2) The Top 3 Tools Every Data Scientist Needs | Built In..
(3) What Tools Do Data Scientists Use? - BrainStation®..
(4) 18 Must-Have Data Science Tools for Turning Data into ... - Geekflare. .
(5) 15 Must-Know Data Science Tools for Beginners (2023) - Dataquest.
No comments