Data Scientists design and execute analytic strategies to extract insights from complex datasets. They build predictive models, apply statistical and machine learning techniques, and communicate findings to inform decisions. Their work supports innovation, strategy, and efficiency across an organization.
Skill trends based on publicly-available nationwide job advertisement data.
Algorithms involve a set of rules or instructions designed to solve specific problems or perform tasks efficiently within a computational context. In data science, algorithms play a crucial role in processing, analyzing, and deriving insights from large datasets.
Data Scientists utilize algorithms to develop and implement statistical models, machine learning algorithms, and other computational techniques to extract meaningful patterns and insights from data. They apply algorithms to solve complex data problems, make predictions, optimize processes, and drive data-driven decision-making.
At Level 1 Proficiency, a worker can understand basic algorithm concepts and terminology, can implement simple algorithms using programming languages, and can apply predefined algorithms to solve straightforward problems, such as sorting or searching data sets.
At Level 2 Proficiency, a worker can analyze and select appropriate algorithms for specific tasks, can modify existing algorithms to improve performance, and can implement more complex algorithms, such as decision trees or clustering methods, to derive insights from data.
At Level 3 Proficiency, a worker can design and develop custom algorithms tailored to specific data challenges, can evaluate the efficiency and effectiveness of various algorithms, and can integrate multiple algorithms into a cohesive data analysis workflow to solve complex problems reliably.
Skill trends based on publicly-available nationwide job advertisement data.
Applied Mathematics involves the use of mathematical methods and techniques to solve practical problems in various fields, including data analysis, modeling, and statistical inference. It encompasses areas such as calculus, linear algebra, probability, and optimization, enabling professionals to create algorithms and interpret data effectively.
In the role of a Data Scientist, Applied Mathematics is utilized to develop models that analyze complex datasets, derive insights, and make predictions. Data Scientists apply mathematical concepts to formulate algorithms, optimize processes, and validate results, ensuring that their analyses are both accurate and actionable.
At Level 1 Proficiency, a worker can perform basic mathematical operations and apply fundamental concepts of statistics and probability to interpret simple datasets. They can assist in data cleaning and preparation, using basic formulas to summarize data and generate simple visualizations.
At Level 2 Proficiency, a worker can apply intermediate mathematical techniques, such as regression analysis and hypothesis testing, to analyze datasets more effectively. They can develop basic predictive models, interpret the results, and communicate findings to team members, demonstrating a greater understanding of the underlying mathematical principles.
At Level 3 Proficiency, a worker can independently design and implement complex mathematical models to solve specific data-related problems. They can utilize advanced techniques such as machine learning algorithms and optimization methods, ensuring robust analysis and interpretation of results. Additionally, they can mentor others in applying mathematical concepts to real-world data challenges.
Skill trends based on publicly-available nationwide job advertisement data.
Artificial Intelligence (AI) involves the development of algorithms and computational models that enable machines to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and understanding natural language.
Data Scientists leverage Artificial Intelligence to analyze complex datasets, extract valuable insights, build predictive models, automate repetitive tasks, optimize decision-making processes, and develop innovative solutions using machine learning, deep learning, natural language processing, and other AI techniques.
At Level 1 Proficiency, a worker can understand basic concepts of artificial intelligence, including definitions and common applications. They can assist in data collection and preparation for AI projects, follow simple instructions to run pre-built AI models, and interpret basic outputs with guidance.
At Level 2 Proficiency, a worker can implement standard AI algorithms and tools to solve specific problems. They can analyze datasets to identify patterns, perform feature selection, and fine-tune existing models. They are capable of collaborating with team members to develop AI solutions and can communicate findings effectively to stakeholders.
At Level 3 Proficiency, a worker can independently design and develop AI models tailored to specific business needs. They can evaluate model performance using appropriate metrics, optimize algorithms for better accuracy, and troubleshoot issues that arise during implementation. They are proficient in using programming languages and frameworks relevant to AI, and can mentor junior team members in best practices.
Skill trends based on publicly-available nationwide job advertisement data.
Neural networks are a subset of machine learning algorithms modeled after the human brain, designed to recognize patterns and make predictions based on input data through interconnected nodes (neurons) organized in layers.
In the role of a data scientist, neural networks are utilized to build predictive models, analyze complex datasets, and extract insights from unstructured data such as images, text, and audio, enabling advanced analytics and decision-making.
At Level 1 Proficiency, a worker can implement basic neural network architectures using pre-built libraries, understand fundamental concepts such as layers and activation functions, and perform simple tasks like training a model on a small dataset to achieve basic predictions.
At Level 2 Proficiency, a worker can design and optimize neural network models for specific tasks, adjust hyperparameters to improve performance, and apply techniques such as regularization and dropout to prevent overfitting, demonstrating a deeper understanding of model evaluation and validation.
At Level 3 Proficiency, a worker can independently develop and deploy complex neural network architectures, such as convolutional and recurrent networks, effectively troubleshoot issues during training, and interpret model outputs to derive actionable insights, showcasing a high level of expertise in applying neural networks to solve real-world problems.
Skill trends based on publicly-available nationwide job advertisement data.
Big Data refers to the large volume of structured and unstructured data that is generated, collected, and processed at high speed. It involves using advanced analytics techniques to extract valuable insights and patterns from this vast amount of data.
Data Scientists use their expertise in Big Data to gather, organize, and analyze large datasets to uncover trends, patterns, and other insights that can help drive business decisions. They employ tools and technologies like Hadoop, Spark, and SQL to handle massive datasets efficiently.
At Level 1 Proficiency, a worker can assist in the collection and storage of large datasets, perform basic data cleaning tasks, and utilize simple tools to visualize data. They can follow established protocols to ensure data integrity and can work under supervision to support data-related projects.
At Level 2 Proficiency, a worker can independently manage and manipulate large datasets using appropriate big data technologies. They can perform exploratory data analysis, apply basic statistical methods, and create more complex visualizations. They are capable of identifying data quality issues and can suggest improvements to data collection processes.
At Level 3 Proficiency, a worker can effectively design and implement big data solutions tailored to specific business needs. They can analyze and interpret complex datasets, derive actionable insights, and communicate findings to stakeholders. They are proficient in using advanced big data tools and frameworks, ensuring data security and compliance throughout the data lifecycle.
Skill trends based on publicly-available nationwide job advertisement data.
Cloud computing refers to the delivery of computing services over the internet, including storage, processing power, and databases, allowing for scalable and flexible resource management without the need for physical hardware.
In the role of a Data Scientist, cloud computing is utilized to store large datasets, run complex algorithms, and deploy machine learning models efficiently, enabling collaboration and access to resources from anywhere.
At Level 1 Proficiency, a worker can access cloud computing platforms, upload and download data, and utilize basic cloud storage services to manage datasets for analysis.
At Level 2 Proficiency, a worker can configure cloud environments, utilize cloud-based tools for data processing, and perform basic data analysis using cloud resources, demonstrating a functional understanding of cloud services.
At Level 3 Proficiency, a worker can design and implement scalable data pipelines in the cloud, optimize resource usage for data processing tasks, and deploy machine learning models in a cloud environment, ensuring reliability and efficiency in their data science projects.
Skill trends based on publicly-available nationwide job advertisement data.
Communication is the ability to convey information effectively through both verbal and written means, as well as active listening and understanding of the message being communicated.
Data Scientists need strong communication skills to explain complex technical concepts to non-technical stakeholders, collaborate with team members, share findings, and create reports that are easily digestible by a wide audience.
At Level 1 Proficiency, a worker can convey basic ideas and findings related to data analysis in a clear and understandable manner, using simple language and visual aids like charts or graphs to support their points. They can participate in discussions and ask questions to clarify information, ensuring they understand the context of the data.
At Level 2 Proficiency, a worker can effectively communicate more complex data insights and methodologies to both technical and non-technical audiences. They can tailor their messaging based on the audience's level of understanding, using appropriate terminology and examples. They are also capable of leading small discussions or presentations, encouraging feedback and interaction.
At Level 3 Proficiency, a worker can confidently present comprehensive data analyses and strategic recommendations to diverse stakeholders, ensuring clarity and engagement throughout the presentation. They can facilitate discussions that drive decision-making, adeptly handling questions and objections while providing clear explanations of their data-driven conclusions.
Skill trends based on publicly-available nationwide job advertisement data.
Computer Vision is a field of artificial intelligence that enables computers to interpret and process visual information from the world, allowing them to identify objects, recognize patterns, and make decisions based on visual data.
In the role of a Data Scientist, Computer Vision is utilized to develop algorithms and models that analyze images and videos, extract meaningful insights, and automate tasks such as image classification, object detection, and facial recognition.
At Level 1 Proficiency, a worker can perform basic image processing tasks, such as loading and displaying images, applying simple filters, and using pre-built libraries to execute fundamental operations like edge detection and color manipulation.
At Level 2 Proficiency, a worker can implement more complex algorithms for object detection and image segmentation, utilize machine learning frameworks to train models on labeled datasets, and evaluate model performance using standard metrics.
At Level 3 Proficiency, a worker can design and optimize advanced computer vision models, integrate them into larger data pipelines, troubleshoot issues in model performance, and effectively communicate findings and insights derived from visual data analysis to stakeholders.
Skill trends based on publicly-available nationwide job advertisement data.
Data analysis involves collecting, processing, and analyzing data to extract insights, identify trends, and make informed decisions.
Data scientists heavily rely on data analysis to extract valuable insights from large and complex datasets. They use various statistical and machine learning techniques to uncover patterns, trends, and correlations within the data.
At Level 1 Proficiency, a worker can perform basic data analysis tasks such as collecting and organizing data from various sources, using simple tools to create basic visualizations, and identifying straightforward trends or patterns in the data. They can also assist in data cleaning by removing duplicates and correcting errors in datasets.
At Level 2 Proficiency, a worker can conduct more complex data analysis by utilizing statistical methods to interpret data sets, create detailed visualizations that effectively communicate findings, and perform exploratory data analysis to uncover insights. They can also begin to use programming languages like Python or R to automate data manipulation tasks and generate reports.
At Level 3 Proficiency, a worker can independently execute comprehensive data analysis projects, applying advanced statistical techniques and machine learning algorithms to derive actionable insights from large datasets. They can effectively communicate their findings to stakeholders through well-structured reports and presentations, and they are capable of designing and implementing data collection processes that ensure data quality and relevance.
Skill trends based on publicly-available nationwide job advertisement data.
Data Engineering involves designing, building, and maintaining systems for collecting, storing, and analyzing data. It focuses on the architecture and infrastructure required to handle big data and ensure data quality, reliability, and accessibility.
Data Scientists rely on Data Engineering skills to access, clean, and transform data for analysis. They utilize these skills to build data pipelines, optimize data workflows, and ensure data integrity to support their analytical work.
At Level 1 Proficiency, a worker can perform basic data extraction and transformation tasks using simple tools and scripts. They can connect to data sources, retrieve data, and conduct straightforward data cleaning processes, ensuring that the data is in a usable format for analysis.
At Level 2 Proficiency, a worker can design and implement more complex data pipelines that involve multiple data sources and formats. They can utilize intermediate data engineering tools and frameworks to automate data workflows, ensuring data quality and consistency while also being able to troubleshoot common issues that arise during data processing.
At Level 3 Proficiency, a worker can architect robust and scalable data systems that support advanced analytics and machine learning applications. They can optimize data storage and retrieval processes, implement data governance practices, and ensure compliance with data privacy regulations, all while collaborating effectively with data scientists and other stakeholders to meet project requirements.
Skill trends based on publicly-available nationwide job advertisement data.
Data Management involves the processes and practices of collecting, storing, organizing, and maintaining data in a way that ensures its accuracy, accessibility, and security throughout its lifecycle.
In the role of a Data Scientist, Data Management is utilized to ensure that data is properly structured and maintained, enabling effective analysis and interpretation. This skill is critical for preparing datasets for modeling, ensuring data quality, and facilitating collaboration with other team members.
At Level 1 Proficiency, a worker can perform basic data entry tasks, organize data files, and follow established protocols for data storage and retrieval, ensuring that data is easily accessible for analysis.
At Level 2 Proficiency, a worker can implement data cleaning techniques, manage data integrity checks, and utilize basic data management tools to organize datasets, allowing for more efficient data analysis and reporting.
At Level 3 Proficiency, a worker can design and maintain complex data management systems, ensure compliance with data governance policies, and optimize data workflows, enabling seamless integration and analysis of large datasets in various projects.
Skill trends based on publicly-available nationwide job advertisement data.
Data mining involves extracting and analyzing large datasets to discover patterns, trends, and insights that can be used to make informed business decisions.
Data Scientists utilize data mining to explore and analyze complex datasets to uncover hidden patterns, correlations, and anomalies that can provide valuable insights for decision-making, predictive modeling, and optimizing business processes.
At Level 1 Proficiency, a worker can perform basic data mining tasks such as identifying and extracting relevant data from structured datasets. They can utilize simple tools to clean and preprocess data, ensuring it is ready for analysis. They are capable of applying basic techniques to discover patterns and trends in the data, but their analysis may be limited to straightforward queries and visualizations.
At Level 2 Proficiency, a worker can conduct more complex data mining activities, including the use of intermediate algorithms for classification and clustering. They can effectively manipulate and analyze larger datasets, employing statistical methods to derive insights. Additionally, they are able to interpret results and communicate findings to stakeholders, demonstrating a greater understanding of the data's implications and potential applications.
At Level 3 Proficiency, a worker can independently execute comprehensive data mining projects, utilizing advanced techniques such as predictive modeling and anomaly detection. They are proficient in selecting appropriate tools and methodologies based on the specific data characteristics and business objectives. A worker at this level can also mentor others in data mining practices, ensuring high-quality analysis and fostering a data-driven culture within the organization.
Skill trends based on publicly-available nationwide job advertisement data.
Data modeling involves creating visual representations of data structures to help understand, analyze, and manipulate data effectively. It includes designing databases, defining relationships between data elements, and ensuring data integrity.
Data Scientists use data modeling to organize and structure data for analysis. They leverage this skill to create data models that optimize data storage, retrieval, and analysis processes. Data modeling enables Data Scientists to work with complex datasets efficiently and derive valuable insights from them.
At Level 1 Proficiency, a worker can create basic data models using simple structures such as tables and charts. They can identify and organize relevant data sources, perform initial data cleaning, and apply fundamental modeling techniques to represent data relationships. They may use basic tools to visualize data and can communicate simple findings to team members.
At Level 2 Proficiency, a worker can develop more complex data models that incorporate multiple variables and relationships. They can utilize statistical methods to analyze data patterns and trends, apply intermediate data transformation techniques, and use software tools to create visualizations that effectively communicate insights. They are capable of collaborating with team members to refine models based on feedback and can begin to interpret the implications of their findings for decision-making.
At Level 3 Proficiency, a worker can design and implement robust data models that are reliable and scalable. They can apply advanced modeling techniques, such as regression analysis or machine learning algorithms, to derive actionable insights from large datasets. They are proficient in using specialized data modeling tools and can effectively communicate complex results to both technical and non-technical stakeholders. Additionally, they can troubleshoot and optimize existing models to improve performance and accuracy.
Skill trends based on publicly-available nationwide job advertisement data.
Data Visualization involves presenting data in a graphical or visual format to help users easily understand complex information and detect patterns, trends, and outliers.
Data Scientists use data visualization to communicate insights from large datasets to stakeholders, make data-driven decisions, identify relationships between variables, and create interactive visualizations for exploratory data analysis.
At Level 1 Proficiency, a worker can create basic visual representations of data using simple charts and graphs, such as bar charts, line graphs, and pie charts, utilizing tools like Excel or basic visualization software. They can interpret these visuals to convey straightforward insights and trends in the data.
At Level 2 Proficiency, a worker can produce more complex visualizations, including scatter plots and heat maps, and can use advanced features of visualization tools to enhance clarity and engagement. They can effectively choose the appropriate visualization type based on the data characteristics and audience needs, and can begin to incorporate interactivity into their visualizations.
At Level 3 Proficiency, a worker can design and implement comprehensive data visualizations that effectively communicate complex datasets and insights to diverse audiences. They can utilize advanced visualization tools and techniques, such as dashboards and storytelling with data, ensuring that their visualizations are not only aesthetically pleasing but also informative and actionable. They can also provide guidance on best practices for data visualization within their team or organization.
Skill trends based on publicly-available nationwide job advertisement data.
Decision Making involves the process of selecting the best course of action from multiple alternatives to achieve a specific goal or solve a problem. It requires critical thinking, evaluation of options, considering consequences, and making informed choices.
Data Scientists utilize Decision Making to analyze complex data sets, interpret findings, and make strategic decisions based on data-driven insights. They use this skill to identify patterns, trends, and correlations in data to influence business strategies and outcomes.
At Level 1 Proficiency, a worker can identify basic decision-making scenarios relevant to data science, such as choosing between different data sources or selecting simple analytical methods. They can follow established guidelines to make straightforward decisions based on data insights and can communicate their choices to team members.
At Level 2 Proficiency, a worker can analyze multiple data-driven options and weigh their pros and cons to make informed decisions. They can apply basic decision-making frameworks to assess risks and benefits, and they are able to justify their decisions with supporting data. Additionally, they can collaborate with colleagues to refine decision-making processes.
At Level 3 Proficiency, a worker can independently make complex decisions that significantly impact project outcomes, utilizing advanced decision-making techniques and tools. They can synthesize diverse data sets and insights to evaluate potential outcomes and make strategic recommendations. Furthermore, they can mentor others in effective decision-making practices and contribute to the development of decision-making protocols within the team.
Skill trends based on publicly-available nationwide job advertisement data.
Deep Learning is a subset of machine learning that utilizes neural networks with many layers (deep networks) to analyze various forms of data, enabling the model to learn complex patterns and representations from large datasets.
In the role of a Data Scientist, deep learning is utilized to build and train models that can perform tasks such as image recognition, natural language processing, and predictive analytics, allowing for the extraction of insights and automation of decision-making processes.
At Level 1 Proficiency, a worker can implement basic deep learning models using pre-built frameworks and libraries, such as TensorFlow or PyTorch, and can perform simple tasks like training a model on a small dataset and evaluating its performance.
At Level 2 Proficiency, a worker can design and optimize deep learning architectures for specific tasks, such as adjusting hyperparameters, selecting appropriate loss functions, and applying techniques like dropout or batch normalization to improve model performance.
At Level 3 Proficiency, a worker can independently develop and deploy complex deep learning models, troubleshoot issues during training, and effectively interpret model outputs, ensuring that the models are robust and generalize well to unseen data.
Skill trends based on publicly-available nationwide job advertisement data.
MLOps (Machine Learning Operations) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It encompasses the collaboration between data scientists and operations teams to automate the end-to-end machine learning lifecycle, including model development, deployment, monitoring, and governance.
In the role of a Data Scientist, MLOps is utilized to streamline the process of taking machine learning models from development to production. This involves implementing best practices for version control, continuous integration and delivery (CI/CD), and monitoring model performance to ensure that models remain effective and relevant over time.
At Level 1 Proficiency, a worker can assist in the basic setup of machine learning environments, understand the fundamental concepts of MLOps, and contribute to the documentation of processes. They can also help in running simple scripts to deploy models in a controlled environment under supervision.
At Level 2 Proficiency, a worker can independently deploy machine learning models using established MLOps frameworks, manage version control for models, and monitor model performance metrics. They can troubleshoot basic issues that arise during deployment and collaborate with team members to improve deployment processes.
At Level 3 Proficiency, a worker can design and implement robust MLOps pipelines that automate the deployment and monitoring of machine learning models. They can ensure compliance with best practices in model governance, optimize model performance in production, and mentor junior team members on MLOps principles and practices.
Skill trends based on publicly-available nationwide job advertisement data.
Machine Learning is a branch of artificial intelligence that focuses on developing algorithms and statistical models that allow computer systems to progressively improve their performance on a specific task without being explicitly programmed.
In the role of Data Scientists, Machine Learning is utilized to analyze large and complex datasets, extract meaningful insights, build predictive models, and make data-driven decisions. Data Scientists use Machine Learning algorithms to detect patterns, trends, and relationships in data that can be leveraged for various applications such as prediction, classification, clustering, anomaly detection, and recommendation systems.
At Level 1 Proficiency, a worker can perform basic tasks related to machine learning, such as understanding fundamental concepts and terminology, utilizing simple algorithms like linear regression, and applying pre-built models to small datasets. They can also assist in data preparation by cleaning and organizing data for analysis.
At Level 2 Proficiency, a worker can implement more complex machine learning algorithms, such as decision trees and clustering techniques, and can effectively tune model parameters to improve performance. They are capable of conducting exploratory data analysis to identify patterns and trends, as well as using libraries like Scikit-learn to build and evaluate models on moderately sized datasets.
At Level 3 Proficiency, a worker can independently design and execute machine learning projects from start to finish, including data collection, preprocessing, model selection, and validation. They can apply advanced techniques such as ensemble methods and neural networks, interpret model results, and communicate findings to stakeholders, ensuring that the models are robust and applicable to real-world scenarios.
Skill trends based on publicly-available nationwide job advertisement data.
Natural Language Processing (NLP) involves the ability to extract, analyze, and understand patterns and structures in human language to enable computers to interact with and process text data.
Data Scientists leverage NLP to process and analyze large volumes of unstructured text data, such as customer reviews, social media posts, or articles, to derive valuable insights and make data-driven decisions.
At Level 1 Proficiency, a worker can perform basic text processing tasks such as tokenization, stemming, and lemmatization. They can utilize simple NLP libraries to extract keywords from text and understand basic concepts like stop words and part-of-speech tagging. They can also apply basic sentiment analysis techniques to determine the overall sentiment of short text samples.
At Level 2 Proficiency, a worker can implement more advanced NLP techniques such as named entity recognition and text classification. They can work with larger datasets and utilize machine learning models to improve the accuracy of their analyses. They are capable of preprocessing text data effectively, including handling different languages and formats, and can interpret the results of their NLP models to provide insights.
At Level 3 Proficiency, a worker can design and develop complex NLP systems that integrate multiple techniques, such as deep learning models for language understanding and generation. They can evaluate and optimize model performance using various metrics and can handle real-world challenges such as ambiguity and context in language. They are proficient in deploying NLP solutions in production environments and can collaborate with cross-functional teams to align NLP projects with business objectives.
Skill trends based on publicly-available nationwide job advertisement data.
Predictive modeling involves using statistical and machine learning techniques to develop models that predict future outcomes based on historical data patterns.
Data Scientists leverage predictive modeling to uncover insights, make data-driven decisions, forecast trends, identify patterns, and optimize processes within organizations.
At Level 1 Proficiency, a worker can understand the basic concepts of predictive modeling, including the identification of relevant data sources and the ability to prepare simple datasets for analysis. They can apply basic statistical techniques to create straightforward models and interpret the results at a fundamental level, often relying on existing templates or tools to assist in the modeling process.
At Level 2 Proficiency, a worker can independently build and validate predictive models using more complex algorithms and techniques. They can perform exploratory data analysis to identify patterns and relationships within the data, apply feature selection methods, and utilize software tools to enhance model accuracy. Additionally, they can communicate findings effectively to stakeholders, providing insights that inform decision-making.
At Level 3 Proficiency, a worker can design and implement sophisticated predictive modeling solutions tailored to specific business problems. They demonstrate a deep understanding of various modeling techniques, including regression, classification, and time series analysis, and can select the most appropriate method based on the context. They are capable of optimizing model performance through advanced techniques such as hyperparameter tuning and cross-validation, and they can mentor others in best practices for predictive modeling.
Skill trends based on publicly-available nationwide job advertisement data.
Problem Solving involves the ability to analyze complex situations, identify challenges, and develop effective solutions to overcome obstacles or achieve goals.
Data Scientists often need strong problem-solving skills to frame data-related challenges, devise analytical approaches, and interpret results to extract insights and make data-driven decisions.
At Level 1 Proficiency, a worker can identify basic problems within datasets and recognize patterns or anomalies. They can apply simple problem-solving techniques to address straightforward issues, such as missing values or outliers, and can follow established procedures to gather relevant data for analysis.
At Level 2 Proficiency, a worker can analyze more complex problems by employing a variety of problem-solving strategies. They can interpret data trends and make informed decisions based on their findings. Additionally, they can collaborate with team members to brainstorm solutions and implement basic algorithms to automate repetitive tasks.
At Level 3 Proficiency, a worker can independently tackle intricate problems by applying advanced problem-solving methodologies. They can design and execute experiments to test hypotheses, evaluate the effectiveness of different approaches, and refine their strategies based on results. Furthermore, they can effectively communicate their insights and recommendations to stakeholders, ensuring that solutions are actionable and aligned with business objectives.
Skill trends based on publicly-available nationwide job advertisement data.
Python is a versatile and high-level programming language known for its readability and ease of use. It supports multiple programming paradigms and is widely used for various applications, including web development, data analysis, artificial intelligence, and scientific computing.
Data Scientists often use Python for data cleaning, manipulation, analysis, visualization, and model building. Python's extensive libraries such as Pandas, NumPy, Matplotlib, and scikit-learn make it suitable for handling large datasets and implementing machine learning algorithms.
At Level 1 Proficiency, a worker can write basic Python scripts to perform simple tasks such as data manipulation and file handling. They can utilize fundamental data types, control structures, and functions to automate repetitive tasks and execute basic algorithms.
At Level 2 Proficiency, a worker can develop more complex Python programs that involve the use of libraries such as NumPy and Pandas for data analysis. They can effectively manipulate datasets, perform exploratory data analysis, and visualize data using libraries like Matplotlib or Seaborn, demonstrating a functional understanding of Python's capabilities in data science.
At Level 3 Proficiency, a worker can confidently implement advanced data analysis techniques using Python, including statistical modeling and machine learning algorithms with libraries such as Scikit-learn. They can write efficient, reusable code, optimize performance, and integrate Python with other tools and technologies to solve complex data problems, ensuring reliable and accurate results in their analyses.
Skill trends based on publicly-available nationwide job advertisement data.
Relational databases are structured collections of data that are organized into tables, allowing for efficient data storage, retrieval, and management through the use of structured query language (SQL). They enable data scientists to manipulate and analyze large datasets by establishing relationships between different data entities.
In the role of a data scientist, relational databases are utilized to store and manage data that is essential for analysis and modeling. Data scientists use SQL to query databases, extract relevant datasets, and perform data cleaning and transformation tasks, ensuring that the data is ready for analysis.
At Level 1 Proficiency, a worker can perform basic SQL queries to retrieve data from a relational database, such as selecting specific columns from a table and filtering rows based on simple conditions. They can also understand the structure of a database and navigate through tables to find the information they need.
At Level 2 Proficiency, a worker can write more complex SQL queries that involve joining multiple tables, aggregating data, and using functions to manipulate data. They can also perform data cleaning tasks, such as identifying and handling missing values, and can create basic reports based on the queried data.
At Level 3 Proficiency, a worker can efficiently design and optimize database queries for performance, ensuring that data retrieval is quick and effective. They can also implement advanced SQL techniques, such as subqueries and window functions, and are capable of managing database schemas, including creating and modifying tables and relationships to support data analysis needs.
Skill trends based on publicly-available nationwide job advertisement data.
Research involves systematically investigating a subject or phenomenon to discover new insights, validate existing knowledge, or solve specific problems. It includes gathering and analyzing information from various sources to draw meaningful conclusions.
Data Scientists utilize research skills to explore and understand complex datasets, identify trends, patterns, and relationships, and derive actionable insights to solve business problems or improve decision-making processes. Research is fundamental in the data science process, from formulating hypotheses to testing and refining models.
At Level 1 Proficiency, a worker can conduct basic literature reviews to gather existing information on a specific research topic, identify relevant sources, and summarize findings. They can formulate simple research questions and assist in data collection efforts, ensuring that data is organized and documented properly for further analysis.
At Level 2 Proficiency, a worker can design and implement straightforward research methodologies, including surveys or experiments, to collect data. They can analyze data using basic statistical techniques and interpret results to draw preliminary conclusions. Additionally, they can effectively communicate findings through reports or presentations, demonstrating a clear understanding of the research context.
At Level 3 Proficiency, a worker can independently lead research projects, developing comprehensive research designs that address complex questions. They can utilize advanced analytical methods to interpret large datasets and derive meaningful insights. Furthermore, they can critically evaluate research methodologies, contribute to peer-reviewed publications, and mentor junior researchers in best practices for conducting research.
Skill trends based on publicly-available nationwide job advertisement data.
Structured Query Language (SQL) is a standardized programming language used for managing and manipulating relational databases. It allows users to perform various operations such as querying data, updating records, inserting new data, and deleting existing data, as well as creating and modifying database structures.
In the role of a Data Scientist, SQL is utilized to extract, analyze, and manipulate data stored in relational databases. Data Scientists use SQL to retrieve relevant datasets for analysis, perform data cleaning and transformation, and generate insights that inform decision-making processes.
At Level 1 Proficiency, a worker can write basic SQL queries to select data from a single table, filter results using simple conditions, and understand the structure of a database. They can also perform basic data retrieval tasks and comprehend simple SQL syntax.
At Level 2 Proficiency, a worker can construct more complex SQL queries that involve multiple tables using JOIN operations, aggregate data using functions like COUNT, SUM, and AVG, and apply more advanced filtering and sorting techniques. They can also create basic views and understand the implications of database normalization.
At Level 3 Proficiency, a worker can efficiently write optimized SQL queries for large datasets, implement subqueries and common table expressions (CTEs), and manage database transactions. They can also design and modify database schemas, ensuring data integrity and performance, while effectively collaborating with other team members to meet data requirements.
Skill trends based on publicly-available nationwide job advertisement data.
Statistics involves the collection, analysis, interpretation, presentation, and organization of data. It encompasses various methods for data analysis, including descriptive statistics, inferential statistics, and probability theory.
Data scientists utilize statistics to uncover meaningful insights, patterns, and trends within large datasets. They use statistical techniques to analyze data, test hypotheses, build predictive models, and make data-driven decisions.
At Level 1 Proficiency, a worker can understand basic statistical concepts such as mean, median, mode, and standard deviation. They can perform simple data collection and organization tasks, and use basic statistical tools to summarize data sets, providing foundational insights into data trends.
At Level 2 Proficiency, a worker can apply intermediate statistical techniques such as hypothesis testing, correlation, and regression analysis. They can interpret results from statistical software, draw conclusions from data analyses, and communicate findings effectively to stakeholders, demonstrating a functional understanding of statistical principles in practical scenarios.
At Level 3 Proficiency, a worker can independently design and conduct complex statistical analyses, including multivariate analysis and advanced modeling techniques. They can critically evaluate data quality, select appropriate statistical methods for specific research questions, and provide actionable insights based on their analyses, ensuring reliable and valid results in their data-driven decision-making processes.
Skill trends based on publicly-available nationwide job advertisement data.
Technical documentation refers to the process of creating clear, concise, and comprehensive written materials that explain the technical aspects of a project, system, or process. This includes user manuals, system specifications, API documentation, and other forms of documentation that facilitate understanding and usage of technical products or services.
In the role of a Data Scientist, technical documentation is utilized to communicate methodologies, data processing techniques, model development, and results to both technical and non-technical stakeholders. It ensures that the work is reproducible and that insights derived from data analyses are accessible and understandable.
At Level 1 Proficiency, a worker can create basic technical documentation that outlines the purpose and functionality of simple data analysis projects. They can document the steps taken in data cleaning and preprocessing, as well as provide brief descriptions of the models used and their outcomes.
At Level 2 Proficiency, a worker can produce more detailed technical documentation that includes comprehensive explanations of data sources, methodologies, and analytical techniques. They can also incorporate visual aids such as charts and diagrams to enhance understanding and ensure that the documentation is user-friendly for a broader audience.
At Level 3 Proficiency, a worker can develop extensive and highly detailed technical documentation that serves as a reference for complex data science projects. They can articulate the rationale behind methodological choices, provide in-depth explanations of algorithms, and ensure that the documentation is structured for easy navigation, making it a valuable resource for future projects and team members.
Skill trends based on publicly-available nationwide job advertisement data.
TensorFlow is an open-source machine learning framework developed by Google that is widely used for building deep learning models. It provides a comprehensive ecosystem of tools, libraries, and community resources to support the development and deployment of artificial intelligence applications.
Data Scientists use TensorFlow for various tasks such as developing and training machine learning models, implementing neural networks, conducting data analysis, and deploying models in production environments. TensorFlow's flexibility and scalability make it a popular choice for complex data science projects.
At Level 1 Proficiency, a worker can use TensorFlow to build and run simple machine learning models, such as linear regression or basic neural networks. They can follow tutorials to set up the environment, load datasets, and execute predefined code snippets to train models and make predictions.
At Level 2 Proficiency, a worker can implement more complex models using TensorFlow, such as convolutional neural networks (CNNs) for image classification or recurrent neural networks (RNNs) for sequence prediction. They can modify existing code, tune hyperparameters, and understand the basic principles of model evaluation and validation.
At Level 3 Proficiency, a worker can independently design, implement, and optimize sophisticated machine learning models using TensorFlow. They can effectively preprocess data, select appropriate architectures, and apply advanced techniques such as transfer learning or regularization. They are capable of troubleshooting issues and ensuring the models are robust and scalable for production use.