Pengolahan data menjadi pengetahuan, rumus atau pola dan kebijakan menggunakan pemrograman Python. Mulai dari pengenalan dasar Python, pembersihan data, visualisasi data, pomdelan (trainig dan testing) dan evaluasi
CONTENT
1. Introduction to Data Science and Decision Making
1.1 Python and Data Science
1.2 The Data Science Pipeline
1.3 Overview of the Contents
2. Python Installation and Libraries for Data Science
2.1 Installation and Setup
2.2 Datasets
2.3 Python Libraries for Data Science
3. Review of Python for Data Science
3.1 Working with Numbers and Logic
3.2 String Operations
3.3 Dealing with Conditional Statements & Iterations
3.4 Creation and Use of Python Functions
3.5 Data Storage
4. Data Acquisition
4.1 Types of Data
4.2 Loading Data into Memory
4.3 Sampling Data
4.4 Reading from Files
4.5 Getting Data from the Web
5. Data Preparation (Preprocessing)
5.1 Pandas for Data Preparation
5.2 Pandas Data Structures
5.3 Putting Data Together
5.4 Data Transformation
5.5 Selection of Data
6. Exploratory Data Analysis
6.1 Revealing Structure of Data
6.2 Plots and Charts
6.3 Testing Assumptions about Data
6.4 Selecting Important Features/Variables
7. Data Modeling and Evaluation using Machine Learning
7.1 Important Statistics for Data Science
7.2 Data Distributions
7.3 Basic Machine Learning Terminology
7.4 Supervised Learning: Regression
7.5 Supervised Learning: Classification
7.6 Unsupervised Learning
7.7 Evaluating Performance of the Trained Model
8. Interpretation and Reporting of Findings
8.1 Confusion Matrix
8.2 Receiver Operating Characteristics (ROC) Curve
8.3 Precision-Recall Curve
8.4 Regression Metrics
9. Data Science Projects
9.1 Regression
9.2 Classification
9.3 Face Recognition
10. Key Insights and Further Avenues
10.1 Key Insights
10.2 Data Science Resources
10.3 Challenges
Course Features
- Lectures 0
- Quizzes 0
- Duration 4 days
- Skill level All levels
- Language English
- Students 0
- Certificate No
- Assessments Yes