Data Science Course Overview
This Data Science course is designed to take you from the fundamentals to advanced data analysis and machine learning concepts. Learn how to collect, process, and analyze data, and gain hands-on experience with real-world datasets and tools to build a strong career in Data Science and AI.
What You'll Learn in This Course:
- Understand data collection, cleaning, and preprocessing techniques
- Perform data analysis using Python libraries like Pandas and NumPy
- Build machine learning models for regression, classification, and clustering
- Create data visualizations using Matplotlib and Seaborn
- Prepare for Data Science roles in IT, corporate, and research sectors
By the end of this Data Science course, you will confidently analyze datasets, implement machine learning solutions, and derive actionable insights to solve real-world business and research problems.
Course Content
- Introduction to Python & IDEs (Jupyter, VS Code)
- Data Types, Variables, Operators
- Conditional Statements & Loops
- Functions, Lambda Functions
- Lists, Tuples, Dictionaries, Sets
- File Handling (CSV, JSON, Text)
- Exception Handling
- Object-Oriented Programming (Classes, Inheritance)
- NumPy arrays: indexing, slicing
- Data handling with Pandas
- Data cleaning: handling nulls, duplicates
- Grouping, merging, and filtering data
- Data visualization: Matplotlib (line, bar, scatter, histogram)
- Customizing plots: titles, labels, legends, subplots
- Seaborn: boxplot, distplot, heatmap, pairplot
- End-to-end Exploratory Data Analysis (EDA)
- Introduction to Machine Learning
- Types of ML: Supervised, Unsupervised, Reinforcement Learning
- ML Workflow & Tools
- Linear Regression, Logistic Regression
- Decision Trees & Random Forest
- K-Nearest Neighbors (KNN), Support Vector Machines (SVM)
- Model Evaluation: Accuracy, Confusion Matrix, ROC
- Clustering: K-Means, Hierarchical Clustering
- Dimensionality Reduction: PCA
- Train/Test Split & Cross-Validation
- Hyperparameter Tuning (GridSearchCV)
- Model Saving: Pickle & Joblib
- Introduction to Flask for ML model deployment
- Real-world dataset project: Classification or Regression
- End-to-end pipeline: Data preprocessing, model training, evaluation & deployment
- Introduction to Data Science and AI
- Introduction to Neural Networks
- Model evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
- Hyperparameter tuning & cross-validation
- Activation functions, backpropagation
- Building models using TensorFlow and Keras
- CNNs for image data, RNNs for sequences
- Overfitting, dropout, optimizers
- AI Basics: What is AI? Applications & Use Cases
- Computer Vision Fundamentals
- AI in real-world systems: Chatbots, Recommendation Systems
- Working with real-world datasets (healthcare, finance, retail)
- Data collection, cleaning, analysis, modeling, and visualization
- End-to-end Data Science project
- Introduction to databases and MySQL
- Installing and setting up MySQL environment
- Understanding tables, rows, columns, and data types
- Creating, altering, and dropping databases and tables
- CRUD Operations: Insert, Update, Delete, Select
- Filtering, Sorting, and Aggregation functions (COUNT, SUM, AVG, MIN, MAX)
- Grouping data with GROUP BY and HAVING clauses
- Joins: INNER, LEFT, RIGHT, FULL
- Subqueries and Nested Queries
- Indexes, Constraints, and Transactions
- Views, Stored Procedures, and Triggers
- User Management and Security (GRANT, REVOKE)
- Database Backup and Restore
Arjun Patel
August 22, 2025 at 10:15 amExcellent Data Science course! The practical projects helped me understand data analysis, visualization, and machine learning effectively.
Priya Sharma
August 23, 2025 at 3:40 pmGreat learning experience! The instructors are experts in Data Science and always ready to guide through machine learning and AI projects.
Vikram Singh
August 24, 2025 at 11:50 amLoved the hands-on projects and real-world data challenges. Highly recommend Codingsthan for anyone wanting to build a career in Data Science.