Introduction to Data Science
- Lesson 1. What is Data Science?
- Lesson 2. Need of Data Science
- Lesson 3. Applications of Data Science
- Lesson 4. Libraries used in Data Science
- Programming
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NumPy Library
- Lesson 1. Introduction to NumPy Library
- Lesson 2. Creating NumPy Arrays
- Lesson 3. Numpy Array Indexing
- Lesson 4. Numpy Array Slicing
- Lesson 4. Numpy Array Operations
Pandas Library
- Lesson 1. Introduction to Pandas Library
- Lesson 2. Creating Series
- Lesson 3. Accessing Series
- Lesson 4. Creating DataFrames
- Lesson 5. Accessing DataFrames
- Lesson 6. Adding and Removing Rows in DataFrame
- Lesson 7. Adding and Removing Columns in DataFrame
- Lesson 8. Working with Missing Values in DataFrame
- Lesson 9. Iterating elements of DataFrame
- Lesson 10. Descriptive statistics in DataFrame
- Lesson 11. Creating DataFrame from CSV file
- Lesson 12. Writing DataFrame data into a CSV file
Matplotlib Library
- Lesson 1. Matplotlib Library
- Lesson 2. Introduction to Matplotlib Library
- Lesson 3. Creating a Line Plot
- Lesson 4. Formatting a Line Plot
- Lesson 5. Creating a Bar Plot
- Lesson 6. Formatting a Bar Plot
- Lesson 7. Creating a Scatter Plot
- Lesson 8. Formatting a Scatter Plot
- Lesson 9. Creating a Histogram Plot
- Lesson 10. Formatting a Histogram Plot
- Lesson 11. Creating a Box Plot
- Lesson 12. Formatting a Box Plot
- Lesson 13. Creating a Pie Plot
- Lesson 14. Formatting a Pie Plot
Seaborn Library
- Lesson 1. Introduction to Seaborn Library
- Lesson 2. Distribution Plots
- Lesson 3. Categorical Plots
- Lesson 4. Matrix Plots
- Lesson 5. Grids
- Lesson 6. Regression Plots
- Lesson 7. Styling and Coloring Plots
Introduction to Machine Learning
- Lesson 1. What is Machine Learning?
- Lesson 2. Need of Machine Learning
- Lesson 3. History of Machine Learning
- Lesson 4. Applications of Machine Learning
- Lesson 5. Libraries used in Machine Learning
- Lesson 6. Types of Machine Learning Algorithm
Supervised Machine Learning Algorithms & Linear Regression Algorithm
- Lesson 1. Linear Regression Algorithm Concept
- Lesson 2. Linear Regression Algorithm Program Implementation
Logistic Regression Algorithm
- Lesson 1. Logistic Regression Algorithm Concept
- Lesson 2. Logistic Regression Algorithm Program Implementation
KNN Algorithm
- Lesson 1. KNN Algorithm Concept
- Lesson 2. KNN Algorithm Program Implementation
Naive Bayes Algorithm
- Lesson 1. Naive Bayes Algorithm Concept
- Lesson 2. Naive Bayes Algorithm Implementation
Decision Tree Algorithm
- Lesson 1. Decision Tree Algorithm Concept
- Lesson 2. Decision Tree Algorithm Program Implementation
Random Forest Algorithm
- Lesson 1. Random Forest Algorithm Concept
- Lesson 2. Random Forest Algorithm Program Implementation
Support Vector Machine Algorithm
- Lesson 1. Support Vector Algorithm Concept
- Lesson 2. Support Vector Algorithm Program Implementation
Unsupervised Machine Learning Algorithms
- Lesson 1. K-Means Algorithm
- Lesson 2. K-Means Algorithm Concept
- Lesson 3. K-Means Algorithm Program Implementation
Advance Concept - ANN Algorithm
- Lesson 1. ANN Algorithm Concept
- Lesson 2. ANN Algorithm Program Implementation
Data Science Introduction
Data science is a 'concept to unify statistics, data analysis, informatics, and their related methods' to 'understand and analyze actual phenomena' with data.
Why Learn Data Science
We learn data science to understand information, make smart choices, and solve tricky problems. It involves studying data using statistics, computers, and machine learning to gain valuable insights and knowledge.
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