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Data Science
Lesson 29/29
|
Study Time: 5 Min
Course:
Beginner’s Guide to Smart Data Science
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Blake Turner
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Class Sessions
1- What is Data Science
2- Importance and Applications in Various Industries
3- Overview of the Data Science Lifecycle
4- Types of Data: Structured, Unstructured, Semi-structured
5- Introduction to Python (or R) programming
6- Data Structures in Python
7- Key Libraries: NumPy, Pandas
8- Basic Programming Concepts and Syntax
9- Basic Statistics: Descriptive and Inferential Statistics
10- Probability Fundamentals and Distributions
11- Linear Algebra Essentials: Vectors and Matrices
12- Introduction to Calculus Concepts relevant to Data Science
13- Data Acquisition Methods
14- Handling Missing Data and Outliers
15- Data Transformation and Normalization
16- Exploratory Data Analysis (EDA) Using Pandas and NumPy
17- Fundamentals of Data Visualization
18- Visualization Tools: Matplotlib, Seaborn
19- Creating Charts and Dashboards for Insights
20- Introduction to Machine Learning and its Types
21- Basic Machine Learning Algorithms
22- Model Evaluation Metrics and Validation Techniques
23- Implementing ML algorithms with Scikit-learn
24- Feature Engineering Basics
25- Training, Testing, and Improving Models
26- Data Privacy and Security Basics
27- Ethical Implications of AI and ML
28- Bias and Fairness in Machine Learning Models
29- Data Science