Course Content
Data Science Fundamentals
Introduction to Data Science
0/4
Definition and Importance of Data Science
Overview of Industry Use Cases
Understanding the Data Science Workflow
Tools and Technologies
Programming for Data Science
0/3
Python Basics
Essential Python Libraries
Data Manipulation Techniques
Data Analysis and Visualization
0/3
Descriptive Statistics
Data Visualization Tools
Exploratory Data Analysis (EDA)
Databases and SQL
0/3
Introduction to Relational Databases
Writing SQL Queries
Integrating SQL with Python
Basic Probability and Statistics
0/3
Probability Theory
Statistical Inference
Correlation vs. Causation
Hands-On Labs
0/3
Analyze and Visualize a Real-World Dataset
Write and Execute SQL Queries
Perform Exploratory Data Analysis
Advanced Data Science and Machine Learning
Advanced Data Wrangling
0/3
Feature Engineering
Handling Missing Data
Outlier Detection
Machine Learning Fundamentals
0/3
Supervised Learning
Unsupervised Learning
Model Evaluation
Ensemble Learning Methods
0/2
Random Forests
Gradient Boosting
Natural Language Processing (NLP)
0/3
Text Preprocessing
Text Representation
Applications
Introduction to Deep Learning
0/3
Basics of Neural Networks
Activation Functions
Implementing Simple Neural Networks
Hands-On Labs
0/3
Build Predictive Models
Perform Clustering
Develop a Sentiment Analysis Tool
Specialized Applications and Capstone Project
Advanced Machine Learning Techniques
0/3
Time Series Analysis
Recommender Systems
Model Optimization
Big Data and Scalable Systems
0/3
Introduction to Big Data Ecosystem
PySpark
Real-Time Data Processing
Advanced Deep Learning
0/3
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Transfer Learning
Ethical and Responsible AI
0/3
Bias and Fairness
Explainability
Data Privacy and Security
Capstone Project
0/4
Problem Selection
Solution Design
Implementation
Presentation
Certified Data Scientist Program Syllabus
Previous
Next