{"id":14384,"date":"2026-05-16T15:13:00","date_gmt":"2026-05-16T12:13:00","guid":{"rendered":"https:\/\/sudacad.net\/?post_type=courses&p=14384"},"modified":"2026-05-16T15:13:00","modified_gmt":"2026-05-16T12:13:00","slug":"ai-datatm","status":"publish","type":"courses","link":"https:\/\/sudacad.net\/ar\/courses\/ai-datatm\/","title":{"rendered":"AI+ Data\u2122"},"content":{"rendered":"

Executive Summary<\/strong><\/p>\n

The AI+ Data\u2122<\/strong> program equips professionals with essential data science skills, covering statistics, programming, data wrangling, machine learning, and generative AI.<\/p>\n

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 expertise in Data-Driven Decision Making and Data Storytelling, enabling effective communication of insights to stakeholders.<\/p>\n

Learning Outcomes<\/strong><\/p>\n

Participants will be able to:<\/p>\n

Understand the fundamentals and lifecycle of data science projects.
Apply statistical concepts and probability for informed analysis.
Manipulate, clean, and preprocess structured and unstructured data.
Develop data visualization and storytelling skills to convey insights effectively.
Build predictive models using machine learning and generative AI tools.
Optimize model performance and apply advanced ML techniques like ensemble learning and dimensionality reduction.
Make data-driven decisions using open-source tools (Power BI, Apache Superset, Pentaho, Redash).
Communicate findings effectively through dashboards, reports, and narratives.<\/p>\n

Course Modules<\/strong><\/p>\n

\nModule 1 \u2013 Foundations of Data Science<\/strong><\/summary>\n

Introduction to Data Science: concepts, importance, and applications
Data Science Life Cycle: business problem, data preparation, exploratory analysis, modeling, deployment, evaluation
Real-world data science applications<\/p>\n<\/details>\n

\nModule 2 \u2013 Foundations of Statistics<\/strong><\/summary>\n

Descriptive & inferential statistics
Probability distributions & central limit theorem
Hypothesis testing & confidence intervals<\/p>\n<\/details>\n

\nModule 3 \u2013 Data Sources and Types<\/strong><\/summary>\n

Structured, semi-structured, unstructured data
Accessing data: databases, APIs, web scraping
Data storage: SQL & NoSQL databases
Hands-on: querying and handling different data types<\/p>\n<\/details>\n

\nModule 4 \u2013 Programming Skills for Data Science<\/strong><\/summary>\n

Python and R basics
Key libraries: NumPy, Pandas, Matplotlib, Seaborn, ggplot2, dplyr
Hands-on: data manipulation and visualization<\/p>\n<\/details>\n

\nModule 5 \u2013 Data Wrangling & Preprocessing<\/strong><\/summary>\n

Handling missing values: imputation techniques
Outlier detection & data transformation: normalization & standardization
Hands-on: cleaning, preprocessing, and preparing data<\/p>\n<\/details>\n

\nModule 6 \u2013 Exploratory Data Analysis (EDA)<\/strong><\/summary>\n

Summary statistics and data visualization
Selecting the right visualization: histograms, scatter plots, box plots
Hands-on: visualizations with Python (Matplotlib, Seaborn) and R (ggplot2)<\/p>\n<\/details>\n

\nModule 7 \u2013 Generative AI Tools for Insights<\/strong><\/summary>\n

Introduction to generative AI: autoencoders, GANs, VAEs
Applications in data synthesis, augmentation, anomaly detection
Hands-on exercises with Gen AI tools<\/p>\n<\/details>\n

\nModule 8 \u2013 Machine Learning Refresher<\/strong><\/summary>\n

Supervised learning: regression, KNN, logistic regression
Unsupervised learning: clustering, decision trees, SVM, hierarchical clustering
Association rule learning
Hands-on exercises with ML tools<\/p>\n<\/details>\n

\nModule 9 \u2013 Advanced Machine Learning<\/strong><\/summary>\n

Ensemble learning: Random Forest, Bagging, Boosting, Stacking, XGBoost
Dimensionality reduction: PCA, t-SNE
Advanced optimization: SGD, Adam, RMSprop, LDA, momentum-based, learning rate schedulers
Practical tips for model training and optimization<\/p>\n<\/details>\n

\nModule 10 \u2013 Data-Driven Decision Making<\/strong><\/summary>\n

Importance of data-driven decision making
Tools: Apache Superset, Pentaho, Redash, Power BI
Case study: Adidas sales dataset for predictive modeling, segmentation, and insights<\/p>\n<\/details>\n

\nModule 11 \u2013 Data Storytelling<\/strong><\/summary>\n

Crafting compelling narratives with data
Identifying use cases, business relevance, and audience
Visualizing data for impact: charts, graphs, maps, dashboards
Interactive and engaging presentation techniques<\/p>\n<\/details>\n

\nModule 12 \u2013 Capstone Project: Employee Attrition Prediction<\/strong><\/summary>\n

Problem statement, data collection, and preparation
Exploratory data analysis and feature engineering
Predictive modeling: logistic regression, decision trees, random forests, gradient boosting
Model evaluation: accuracy, precision, recall, F1-score
Data storytelling: dashboards, visualizations, and actionable business insights<\/p>\n<\/details>\n

\n

Ready to get started?<\/h3>\n<\/div>\n
\ud83d\uded2<\/span><\/p>\n
\n
NOTE<\/div>\n

To purchase this course, please add it to your cart and complete the checkout process.<\/p>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

Executive Summary The AI+ Data\u2122 program equips professionals with essential data science skills, covering statistics, programming, data wrangling, machine learning, and generative AI. 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