Data Science Ultimate (Bundle Course)

Course Overview

Course duration: 4 months

1. Introduction to Data Science

1. Overview of Data Science
2. Applications of Data Science in industries
3. The Data Science workflow

2. Programming for Data Science

1. Python or R: Basics to advanced programming
2. Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
3. SQL for Data Manipulation and Querying

3. Mathematics and Statistics

1. Linear Algebra: Vectors, matrices, transformations
2. Probability and Statistics: Distributions, hypothesis testing, confidence intervals
3. Calculus: Optimization, derivatives for machine learning

4. Data Wrangling and Preprocessing

1. Data cleaning and handling missing values
2. Exploratory Data Analysis (EDA)
3. Feature engineering and scaling techniques

5. Data Visualization

1. Tools: Matplotlib, Seaborn, Tableau, or Power BI
2. Visual storytelling and dashboard creation

6. Machine Learning

1. Supervised Learning: Regression, Classification
2. Unsupervised Learning: Clustering, Dimensionality Reduction
3. Model evaluation and validation techniques
4. Advanced topics: Ensemble models, XGBoost, Random Forest

7. Big Data and Tools

1. Overview of Big Data and its ecosystems
2. Tools: Hadoop, Spark, Hive
3. Distributed data processing

8. Deep Learning and Neural Networks

1. Introduction to Deep Learning
2. Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)
3. Frameworks: TensorFlow, PyTorch, Keras

9. Natural Language Processing (NLP)

1. Text processing techniques
2. Sentiment Analysis, Topic Modeling
3. Applications: Chatbots, Recommendation Systems

10. Time Series Analysis

1. Concepts of Time Series
2. ARIMA, SARIMA models
3. Forecasting techniques

11. Deployment and Tools

1. Building APIs for machine learning models
2. Deployment on cloud platforms (AWS, Azure, GCP)
3. Monitoring and scaling models

12. Capstone Project

1. End-to-end project on a real-world dataset
2. Application of the entire data science pipeline