https://github.com/ribtas007/campusx_ds
This repo will contain my notes for the CampusX course
https://github.com/ribtas007/campusx_ds
Last synced: 5 months ago
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This repo will contain my notes for the CampusX course
- Host: GitHub
- URL: https://github.com/ribtas007/campusx_ds
- Owner: RIBTAS007
- Created: 2022-11-12T13:54:04.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-11-12T13:58:36.000Z (over 3 years ago)
- Last Synced: 2025-01-24T12:45:23.086Z (over 1 year ago)
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# CampusX Data Science Course
## Syllabus
### **Module-1 Python for Data Science**
#### Fundamentals of Python
- Python Basics
- Python Data Structures
- Functions
- Functional Programming
- OOPS
- Exception Handling
- File Handling
- Modules and Packages
#### Numpy
- Why Numpy
- Python Lists Vs Nd-Arrays
- Creating Nd-arrays
- Array Operations & Functions
- Broadcasting
- Plotting Graphs
- Boolean Indexing
- Fancy Indexing
#### Pandas
- Import and Export
- Series & DataFrames
- GroupBy
- Working with Text data
- Multiindex DataFrame
- Reshaping and Pivoting
- Plotting and Visualization
- Working with Date and Time
- Time Series
### **Module-2 Data Visualization**
#### Basic Statistics
- Data and its types
- Measure of Central Tendency
- Measure of Variability
- Percentiles and Quartiles
- 5 number summary
- Kurtosis and Skew
- PDF and CDF
#### Matplotlib
- 2-D Plots
- Subplots
- 3D Plots
#### Seaborn
- Scatterplot
- Countplot and Barplot
- Boxplot and Violinplot
- Distplot
- Joinplot
- Regplot
### **Module-3 SQL for Data Science**
#### Database Basics
- What is a Database
- Types of Databases
- Normalization(OLAP vs OLTP)
- ER Diagram
#### SQL Basics
- Installing MySQL
- DDL Commands
- SELECT Query
- WHERE Clause
- LIMIT
- DISTINCT
- ORDER BY
- HAVING
- CASE
- Operators & Functions
- Joins
- Subquery
- DCL Commands
#### Advanced SQL
- Views
- Window Functions
- Common Table Expressions
- Date & Time Manipulations
### **Module-4 Data Analytics Process**
#### Data Acquisition
- Working with JSON data
- Working with APIs
- Web Scraping
- Working with Databases
#### Data Cleaning
#### Data Wrangling
#### EDA
### **Module-5 Machine Learning Basics**
#### Machine Learning Theory
- What is Machine Learning?
- ML Vs DL Vs AI
- Types of Machine Learning
- Offline Vs Online ML
- Instance Vs Model Based ML
- Challenges in Machine Learning
- Applications of ML
- ML Development Lifecycle
- Data Engineer Vs Data Analyst Vs Data - Scientist Vs ML Engineer
- Tensors
- Installing Anaconda
#### ML Metrics
- Regression Metrics
- Classification Metrics
#### End-to-End Project
### **Module-6 Maths for Data Science**
#### Advanced Statistics
- Population and Sample
- Normal Distribution
- Standard Normal Variate and Standardization
- KDE
- Central Limit Theorem
- QQ Plot
- Probability Distributions
- Co-variance & Pearson Correlation
- Confidence Interval
- Hypothesis Testing
#### Probability
#### Calculus
#### Linear Algebra
### **Module-7 Machine Learning Algorithm**
#### Linear Regression
#### Gradient Descent
#### Logistic Regression
#### SVM & SVR
#### Naive Bayes
#### KNN
#### Decision Trees
#### Random Forest
#### Bagging Ensemble
#### Adaboost
#### Gradient Boosting
#### XgBoost
#### PCA
#### K-Means
#### Hierarchical Clustering
#### DBSCAN
#### T-sne
### **Module-8 Practical Machine Learning**
#### Bias Variance Trade-off
#### Regularization
#### Cross Validation
#### Working with Missing Data
#### Feature Scaling
#### Feature Encoding
#### Feature Transformation
#### Pipelines
#### Date and Time
#### Outliers
#### Feature Construction
#### Feature Selection
#### Model Tuning
#### Imbalanced Datasets
#### Multicollinearity
#### Data Leakage
#### Working with large dataset
### Module-9 **Model Production and Deployment**
#### Details to be updated soon!
### Module-10 **End to End Case Study/Projects**
#### Details to be updated soon!