AI+ Data™

التصنيفات : AiCerts Certifications
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عن الدورة

Executive Summary

The AI+ Data™ 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 expertise in Data-Driven Decision Making and Data Storytelling, enabling effective communication of insights to stakeholders.

Learning Outcomes

Participants will be able to:

  • 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.

Course Modules

Module 1 – Foundations of Data Science

  • 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

Module 2 – Foundations of Statistics

  • Descriptive & inferential statistics

  • Probability distributions & central limit theorem

  • Hypothesis testing & confidence intervals

Module 3 – Data Sources and Types

  • Structured, semi-structured, unstructured data

  • Accessing data: databases, APIs, web scraping

  • Data storage: SQL & NoSQL databases

  • Hands-on: querying and handling different data types

Module 4 – Programming Skills for Data Science

  • Python and R basics

  • Key libraries: NumPy, Pandas, Matplotlib, Seaborn, ggplot2, dplyr

  • Hands-on: data manipulation and visualization

Module 5 – Data Wrangling & Preprocessing

  • Handling missing values: imputation techniques

  • Outlier detection & data transformation: normalization & standardization

  • Hands-on: cleaning, preprocessing, and preparing data

Module 6 – Exploratory Data Analysis (EDA)

  • Summary statistics and data visualization

  • Selecting the right visualization: histograms, scatter plots, box plots

  • Hands-on: visualizations with Python (Matplotlib, Seaborn) and R (ggplot2)

Module 7 – Generative AI Tools for Insights

  • Introduction to generative AI: autoencoders, GANs, VAEs

  • Applications in data synthesis, augmentation, anomaly detection

  • Hands-on exercises with Gen AI tools

Module 8 – Machine Learning Refresher

  • Supervised learning: regression, KNN, logistic regression

  • Unsupervised learning: clustering, decision trees, SVM, hierarchical clustering

  • Association rule learning

  • Hands-on exercises with ML tools

Module 9 – Advanced Machine Learning

  • 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

Module 10 – Data-Driven Decision Making

  • Importance of data-driven decision making

  • Tools: Apache Superset, Pentaho, Redash, Power BI

  • Case study: Adidas sales dataset for predictive modeling, segmentation, and insights

Module 11 – Data Storytelling

  • 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

Module 12 – Capstone Project: Employee Attrition Prediction

  • 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

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