{"id":14384,"date":"2026-02-28T17:29:36","date_gmt":"2026-02-28T15:29:36","guid":{"rendered":"https:\/\/sudacad.net\/?post_type=courses&#038;p=14384"},"modified":"2026-02-28T17:29:36","modified_gmt":"2026-02-28T15:29:36","slug":"ai-datatm","status":"publish","type":"courses","link":"https:\/\/sudacad.net\/ar\/courses\/ai-datatm\/","title":{"rendered":"AI+ Data\u2122"},"content":{"rendered":"<h3 data-start=\"193\" data-end=\"220\"><strong data-start=\"197\" data-end=\"218\">Executive Summary<\/strong><\/h3>\n<p data-start=\"221\" data-end=\"400\">The <strong data-start=\"225\" data-end=\"238\">AI+ Data\u2122<\/strong> program equips professionals with essential <strong data-start=\"283\" data-end=\"306\">data science skills<\/strong>, covering <strong data-start=\"317\" data-end=\"397\">statistics, programming, data wrangling, machine learning, and generative AI<\/strong>.<\/p>\n<ul data-start=\"401\" data-end=\"787\">\n<li data-start=\"401\" data-end=\"473\">\n<p data-start=\"403\" data-end=\"473\">Learn to analyze, model, and visualize data for actionable insights.<\/p>\n<\/li>\n<li data-start=\"474\" data-end=\"570\">\n<p data-start=\"476\" data-end=\"570\">Apply advanced techniques to solve real-world problems using <strong data-start=\"537\" data-end=\"567\">Python, R, and cloud tools<\/strong>.<\/p>\n<\/li>\n<li data-start=\"571\" data-end=\"644\">\n<p data-start=\"573\" data-end=\"644\">Complete a <strong data-start=\"584\" data-end=\"604\">capstone project<\/strong> on <strong data-start=\"608\" data-end=\"641\">Employee Attrition Prediction<\/strong>.<\/p>\n<\/li>\n<li data-start=\"645\" data-end=\"787\">\n<p data-start=\"647\" data-end=\"787\">Gain expertise in <strong data-start=\"665\" data-end=\"696\">Data-Driven Decision Making<\/strong> and <strong data-start=\"701\" data-end=\"722\">Data Storytelling<\/strong>, enabling effective communication of insights to stakeholders.<\/p>\n<\/li>\n<\/ul>\n<hr data-start=\"789\" data-end=\"792\" \/>\n<h3 data-start=\"794\" data-end=\"821\"><strong data-start=\"798\" data-end=\"819\">Learning Outcomes<\/strong><\/h3>\n<p data-start=\"822\" data-end=\"853\">Participants will be able to:<\/p>\n<ul data-start=\"854\" data-end=\"1530\">\n<li data-start=\"854\" data-end=\"925\">\n<p data-start=\"856\" data-end=\"925\">Understand the fundamentals and lifecycle of data science projects.<\/p>\n<\/li>\n<li data-start=\"926\" data-end=\"995\">\n<p data-start=\"928\" data-end=\"995\">Apply statistical concepts and probability for informed analysis.<\/p>\n<\/li>\n<li data-start=\"996\" data-end=\"1067\">\n<p data-start=\"998\" data-end=\"1067\">Manipulate, clean, and preprocess structured and unstructured data.<\/p>\n<\/li>\n<li data-start=\"1068\" data-end=\"1154\">\n<p data-start=\"1070\" data-end=\"1154\">Develop data visualization and storytelling skills to convey insights effectively.<\/p>\n<\/li>\n<li data-start=\"1155\" data-end=\"1230\">\n<p data-start=\"1157\" data-end=\"1230\">Build predictive models using machine learning and generative AI tools.<\/p>\n<\/li>\n<li data-start=\"1231\" data-end=\"1347\">\n<p data-start=\"1233\" data-end=\"1347\">Optimize model performance and apply advanced ML techniques like ensemble learning and dimensionality reduction.<\/p>\n<\/li>\n<li data-start=\"1348\" data-end=\"1448\">\n<p data-start=\"1350\" data-end=\"1448\">Make data-driven decisions using open-source tools (Power BI, Apache Superset, Pentaho, Redash).<\/p>\n<\/li>\n<li data-start=\"1449\" data-end=\"1530\">\n<p data-start=\"1451\" data-end=\"1530\">Communicate findings effectively through dashboards, reports, and narratives.<\/p>\n<\/li>\n<\/ul>\n<hr data-start=\"1532\" data-end=\"1535\" \/>\n<h3 data-start=\"1537\" data-end=\"1561\"><strong data-start=\"1541\" data-end=\"1559\">Course Modules<\/strong><\/h3>\n<p data-start=\"1563\" data-end=\"1607\"><strong data-start=\"1563\" data-end=\"1605\">Module 1 \u2013 Foundations of Data Science<\/strong><\/p>\n<ul data-start=\"1608\" data-end=\"1841\">\n<li data-start=\"1608\" data-end=\"1680\">\n<p data-start=\"1610\" data-end=\"1680\">Introduction to Data Science: concepts, importance, and applications<\/p>\n<\/li>\n<li data-start=\"1681\" data-end=\"1800\">\n<p data-start=\"1683\" data-end=\"1800\">Data Science Life Cycle: business problem, data preparation, exploratory analysis, modeling, deployment, evaluation<\/p>\n<\/li>\n<li data-start=\"1801\" data-end=\"1841\">\n<p data-start=\"1803\" data-end=\"1841\">Real-world data science applications<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"1843\" data-end=\"1885\"><strong data-start=\"1843\" data-end=\"1883\">Module 2 \u2013 Foundations of Statistics<\/strong><\/p>\n<ul data-start=\"1886\" data-end=\"2026\">\n<li data-start=\"1886\" data-end=\"1926\">\n<p data-start=\"1888\" data-end=\"1926\">Descriptive &amp; inferential statistics<\/p>\n<\/li>\n<li data-start=\"1927\" data-end=\"1980\">\n<p data-start=\"1929\" data-end=\"1980\">Probability distributions &amp; central limit theorem<\/p>\n<\/li>\n<li data-start=\"1981\" data-end=\"2026\">\n<p data-start=\"1983\" data-end=\"2026\">Hypothesis testing &amp; confidence intervals<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2028\" data-end=\"2067\"><strong data-start=\"2028\" data-end=\"2065\">Module 3 \u2013 Data Sources and Types<\/strong><\/p>\n<ul data-start=\"2068\" data-end=\"2265\">\n<li data-start=\"2068\" data-end=\"2118\">\n<p data-start=\"2070\" data-end=\"2118\">Structured, semi-structured, unstructured data<\/p>\n<\/li>\n<li data-start=\"2119\" data-end=\"2168\">\n<p data-start=\"2121\" data-end=\"2168\">Accessing data: databases, APIs, web scraping<\/p>\n<\/li>\n<li data-start=\"2169\" data-end=\"2208\">\n<p data-start=\"2171\" data-end=\"2208\">Data storage: SQL &amp; NoSQL databases<\/p>\n<\/li>\n<li data-start=\"2209\" data-end=\"2265\">\n<p data-start=\"2211\" data-end=\"2265\">Hands-on: querying and handling different data types<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2267\" data-end=\"2319\"><strong data-start=\"2267\" data-end=\"2317\">Module 4 \u2013 Programming Skills for Data Science<\/strong><\/p>\n<ul data-start=\"2320\" data-end=\"2463\">\n<li data-start=\"2320\" data-end=\"2343\">\n<p data-start=\"2322\" data-end=\"2343\">Python and R basics<\/p>\n<\/li>\n<li data-start=\"2344\" data-end=\"2413\">\n<p data-start=\"2346\" data-end=\"2413\">Key libraries: NumPy, Pandas, Matplotlib, Seaborn, ggplot2, dplyr<\/p>\n<\/li>\n<li data-start=\"2414\" data-end=\"2463\">\n<p data-start=\"2416\" data-end=\"2463\">Hands-on: data manipulation and visualization<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2465\" data-end=\"2512\"><strong data-start=\"2465\" data-end=\"2510\">Module 5 \u2013 Data Wrangling &amp; Preprocessing<\/strong><\/p>\n<ul data-start=\"2513\" data-end=\"2698\">\n<li data-start=\"2513\" data-end=\"2563\">\n<p data-start=\"2515\" data-end=\"2563\">Handling missing values: imputation techniques<\/p>\n<\/li>\n<li data-start=\"2564\" data-end=\"2640\">\n<p data-start=\"2566\" data-end=\"2640\">Outlier detection &amp; data transformation: normalization &amp; standardization<\/p>\n<\/li>\n<li data-start=\"2641\" data-end=\"2698\">\n<p data-start=\"2643\" data-end=\"2698\">Hands-on: cleaning, preprocessing, and preparing data<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2700\" data-end=\"2748\"><strong data-start=\"2700\" data-end=\"2746\">Module 6 \u2013 Exploratory Data Analysis (EDA)<\/strong><\/p>\n<ul data-start=\"2749\" data-end=\"2949\">\n<li data-start=\"2749\" data-end=\"2794\">\n<p data-start=\"2751\" data-end=\"2794\">Summary statistics and data visualization<\/p>\n<\/li>\n<li data-start=\"2795\" data-end=\"2870\">\n<p data-start=\"2797\" data-end=\"2870\">Selecting the right visualization: histograms, scatter plots, box plots<\/p>\n<\/li>\n<li data-start=\"2871\" data-end=\"2949\">\n<p data-start=\"2873\" data-end=\"2949\">Hands-on: visualizations with Python (Matplotlib, Seaborn) and R (ggplot2)<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2951\" data-end=\"3000\"><strong data-start=\"2951\" data-end=\"2998\">Module 7 \u2013 Generative AI Tools for Insights<\/strong><\/p>\n<ul data-start=\"3001\" data-end=\"3169\">\n<li data-start=\"3001\" data-end=\"3060\">\n<p data-start=\"3003\" data-end=\"3060\">Introduction to generative AI: autoencoders, GANs, VAEs<\/p>\n<\/li>\n<li data-start=\"3061\" data-end=\"3128\">\n<p data-start=\"3063\" data-end=\"3128\">Applications in data synthesis, augmentation, anomaly detection<\/p>\n<\/li>\n<li data-start=\"3129\" data-end=\"3169\">\n<p data-start=\"3131\" data-end=\"3169\">Hands-on exercises with Gen AI tools<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"3171\" data-end=\"3214\"><strong data-start=\"3171\" data-end=\"3212\">Module 8 \u2013 Machine Learning Refresher<\/strong><\/p>\n<ul data-start=\"3215\" data-end=\"3427\">\n<li data-start=\"3215\" data-end=\"3276\">\n<p data-start=\"3217\" data-end=\"3276\">Supervised learning: regression, KNN, logistic regression<\/p>\n<\/li>\n<li data-start=\"3277\" data-end=\"3360\">\n<p data-start=\"3279\" data-end=\"3360\">Unsupervised learning: clustering, decision trees, SVM, hierarchical clustering<\/p>\n<\/li>\n<li data-start=\"3361\" data-end=\"3390\">\n<p data-start=\"3363\" data-end=\"3390\">Association rule learning<\/p>\n<\/li>\n<li data-start=\"3391\" data-end=\"3427\">\n<p data-start=\"3393\" data-end=\"3427\">Hands-on exercises with ML tools<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"3429\" data-end=\"3471\"><strong data-start=\"3429\" data-end=\"3469\">Module 9 \u2013 Advanced Machine Learning<\/strong><\/p>\n<ul data-start=\"3472\" data-end=\"3735\">\n<li data-start=\"3472\" data-end=\"3546\">\n<p data-start=\"3474\" data-end=\"3546\">Ensemble learning: Random Forest, Bagging, Boosting, Stacking, XGBoost<\/p>\n<\/li>\n<li data-start=\"3547\" data-end=\"3587\">\n<p data-start=\"3549\" data-end=\"3587\">Dimensionality reduction: PCA, t-SNE<\/p>\n<\/li>\n<li data-start=\"3588\" data-end=\"3680\">\n<p data-start=\"3590\" data-end=\"3680\">Advanced optimization: SGD, Adam, RMSprop, LDA, momentum-based, learning rate schedulers<\/p>\n<\/li>\n<li data-start=\"3681\" data-end=\"3735\">\n<p data-start=\"3683\" data-end=\"3735\">Practical tips for model training and optimization<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"3737\" data-end=\"3782\"><strong data-start=\"3737\" data-end=\"3780\">Module 10 \u2013 Data-Driven Decision Making<\/strong><\/p>\n<ul data-start=\"3783\" data-end=\"3971\">\n<li data-start=\"3783\" data-end=\"3828\">\n<p data-start=\"3785\" data-end=\"3828\">Importance of data-driven decision making<\/p>\n<\/li>\n<li data-start=\"3829\" data-end=\"3882\">\n<p data-start=\"3831\" data-end=\"3882\">Tools: Apache Superset, Pentaho, Redash, Power BI<\/p>\n<\/li>\n<li data-start=\"3883\" data-end=\"3971\">\n<p data-start=\"3885\" data-end=\"3971\">Case study: Adidas sales dataset for predictive modeling, segmentation, and insights<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"3973\" data-end=\"4008\"><strong data-start=\"3973\" data-end=\"4006\">Module 11 \u2013 Data Storytelling<\/strong><\/p>\n<ul data-start=\"4009\" data-end=\"4232\">\n<li data-start=\"4009\" data-end=\"4053\">\n<p data-start=\"4011\" data-end=\"4053\">Crafting compelling narratives with data<\/p>\n<\/li>\n<li data-start=\"4054\" data-end=\"4113\">\n<p data-start=\"4056\" data-end=\"4113\">Identifying use cases, business relevance, and audience<\/p>\n<\/li>\n<li data-start=\"4114\" data-end=\"4179\">\n<p data-start=\"4116\" data-end=\"4179\">Visualizing data for impact: charts, graphs, maps, dashboards<\/p>\n<\/li>\n<li data-start=\"4180\" data-end=\"4232\">\n<p data-start=\"4182\" data-end=\"4232\">Interactive and engaging presentation techniques<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"4234\" data-end=\"4299\"><strong data-start=\"4234\" data-end=\"4297\">Module 12 \u2013 Capstone Project: Employee Attrition Prediction<\/strong><\/p>\n<ul data-start=\"4300\" data-end=\"4649\">\n<li data-start=\"4300\" data-end=\"4355\">\n<p data-start=\"4302\" data-end=\"4355\">Problem statement, data collection, and preparation<\/p>\n<\/li>\n<li data-start=\"4356\" data-end=\"4409\">\n<p data-start=\"4358\" data-end=\"4409\">Exploratory data analysis and feature engineering<\/p>\n<\/li>\n<li data-start=\"4410\" data-end=\"4505\">\n<p data-start=\"4412\" data-end=\"4505\">Predictive modeling: logistic regression, decision trees, random forests, gradient boosting<\/p>\n<\/li>\n<li data-start=\"4506\" data-end=\"4565\">\n<p data-start=\"4508\" data-end=\"4565\">Model evaluation: accuracy, precision, recall, F1-score<\/p>\n<\/li>\n<li data-start=\"4566\" data-end=\"4649\">\n<p data-start=\"4568\" data-end=\"4649\">Data storytelling: dashboards, visualizations, and actionable business insights<\/p>\n<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>Executive Summary The AI+ Data\u2122 program equips professionals with essential data science skills, covering statistics, programming, data wrangling, machine learning, and generative AI. Learn to analyze, model, and visualize data for actionable insights. Apply advanced techniques to solve real-world problems using Python, R, and cloud tools. Complete a capstone project on Employee Attrition Prediction. Gain [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":14400,"template":"","course-category":[323],"course-tag":[],"class_list":["post-14384","courses","type-courses","status-publish","has-post-thumbnail","hentry","course-category-aicerts","pmpro-has-access","entry","has-media","owp-thumbs-layout-horizontal","owp-btn-very-big","owp-tabs-layout-horizontal","has-no-thumbnails","has-product-nav"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI+ Data\u2122<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sudacad.net\/ar\/courses\/ai-datatm\/\" \/>\n<meta property=\"og:locale\" content=\"ar_AR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI+ Data\u2122\" \/>\n<meta property=\"og:description\" content=\"Executive Summary The AI+ Data\u2122 program equips professionals with essential data science skills, covering statistics, programming, data wrangling, machine learning, and generative AI. Learn to analyze, model, and visualize data for actionable insights. Apply advanced techniques to solve real-world problems using Python, R, and cloud tools. Complete a capstone project on Employee Attrition Prediction. 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