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Diploma in Data Science and Analytics
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Course Outline
10 sections
1. Big Data Analytics
90 mins
9 topics
Introduction to Big Data Analytics
Data Collection and Preprocessing
Data Storage and Management
Data Mining and Machine Learning
Data Visualization
2. Introduction to Data Science
300 mins
30 topics
What is Data Science?
History of Data Science
Data Science Lifecycle
Data Types and Data Sources
Data Preprocessing
3. Statistics for Data Science
300 mins
30 topics
Introduction to Statistics
Probability Theory
Data Visualization
Sampling and Estimation
Hypothesis Testing
4. Deep Learning
300 mins
30 topics
Introduction to Deep Learning
Neural Networks
Activation Functions
Loss Functions
Backpropagation
5. Natural Language Processing
300 mins
30 topics
Introduction to Natural Language Processing
Text Preprocessing in NLP
Language Models in NLP
Sentiment Analysis
Named Entity Recognition (NER)
6. Capstone Project
80 mins
8 topics
Choosing a Capstone Project Topic
Developing a Capstone Project Proposal
Conducting Literature Review for Capstone Project
Data Collection and Analysis Methods
Writing the Capstone Project Report
7. Data Wrangling and Preprocessing
80 mins
8 topics
Introduction to Data Wrangling
Data Cleaning Techniques
Data Transformation Methods
Data Integration and Aggregation
Handling Time Series Data
8. Machine Learning Fundamentals
80 mins
8 topics
Introduction to Machine Learning
Data Preprocessing
Supervised Learning
Unsupervised Learning
Model Evaluation and Selection
9. Data Visualization and Interpretation
100 mins
10 topics
Introduction to Data Visualization
Data Visualization Tools
Choosing the Right Visualization
Design Principles in Data Visualization
Interactive Data Visualization
10. Predictive Analytics
90 mins
9 topics
Introduction to Predictive Analytics
Data Collection and Preparation for Predictive Analytics
Exploratory Data Analysis (EDA) for Predictive Analytics
Predictive Modeling Techniques
Model Evaluation and Selection