Certificate in Data Science (CDS)

Course overview

Course outline

1. 1Day 1: Data Analysis and Visualization

   • Understanding data types and visualization techniques
   • Assessing the representativeness of data
   • Summarizing data using descriptive statistics
   • Profiling multiple groups with statistical tests
   • Creating advanced visualizations with smart charts
   • Simple Linear Regression and Logistic Regression
   • Identifying and addressing outliers
2. 2Day 2: Machine Learning – Supervised

   • Multiple Linear and Logistic Regression
   • Discriminant Analysis: Functions and probabilistic models
   • Decision Trees: CART, CHAID, and Random Forests
   • Support Vector Machines and K-Nearest Neighbors
   • Naïve Bayes
   • Neural Networks, Deep Learning, and AI applications
3. 3Day 3: Business Intelligence Forecasting – R vs. Python

   • Fundamentals of Business Intelligence
   • Data collection and database sources
   • ETL processes (Extract, Transform, Load)
   • Data storage: Warehouses, marts, and lakes
   • Analytics tools: BI platforms, OLAP, dashboards, etc.
   • Forecasting methods and trend analysis
   • Exponential smoothing (additive and multiplicative)
   • Time Series Analysis and ARIMA models
   • Comparison of R and Python in statistical tests and ML algorithms
4. 4Day 4: Machine Learning: Unsupervised & Project Management for Data Scientists (PMP)

   • Machine Learning: Unsupervised
   • Principal Component Analysis (PCA)
   • Clustering techniques: Hierarchical and K-Means
   • Simple Correspondence Analysis
   • Multidimensional Scaling
   • Quadrant Analysis
   • Project Management for Data Scientists (PMP)
   • Introduction to PMP for Data Science projects
   • Managing integration, scope, and cost
   • Handling time, quality, and communication
   • Risk management, procurement, and stakeholder engagement
5. 5Day 5: IoT and Big Data Ecosystem

   • Essentials of IoT, M2M, and embedded systems
   • Basic IoT communication protocols
   • Big Data fundamentals: “Where” and “When”
   • Distributed file systems with HDFS
   • Comparing MapReduce and Spark for data sharing
   • Overview of the Big Data ecosystem: Spark, MongoDB, Cassandra, Flume, Cloudera, Oozie, and Mahout