Ai IIntegrated Course
Overview
Curriculum
Benefits
Course Overview
Our Data Science with Python training provides a comprehensive, hands-on experience designed to help students master the essential skills for analyzing, visualizing, and interpreting complex data. This 8-week intensive program is structured to take you from the fundamentals of Python programming to advanced data manipulation using industry-standard libraries like NumPy and Pandas. With a focus on practical learning, the course balances daily classroom instruction with dedicated time for tasks and real-world projects, ensuring you can apply statistical methods and data cleaning techniques to actual datasets.
As the course progresses, students dive deep into Exploratory Data Analysis (EDA) and professional data visualization using Matplotlib and Seaborn to tell compelling stories with data. The final phase of the training transitions into the world of Machine Learning, covering everything from feature engineering and SQL basics to building predictive models with algorithms like Random Forest and XGBoost. Whether you are a student, a professional, or a career switcher, this program equips you with the technical expertise and project-based experience needed to excel in data-oriented roles both locally and internationally.
Curriculum
Week 1: Python Basics + Intro to Data Science
Introduction to Data Science
• What is Data Science?
• AI vs ML vs Data Science vs Data Analytics
• Real-world applications of Data Science
Python Fundamentals
• Installing Python & Jupyter Notebook
• Variables & Data Types
• Operators
• Conditional Statements (if-else)
• Loops (for, while)
• Functions
• Projects
Week 2: Python for Data Handling
Lists, Tuples, Sets, Dictionaries
• String Operations
• File Handling
• Object-Oriented Programming (OOP)
• Exception Handling
• Projects
Week 3: NumPy + Statistics Basics
NumPy – Numerical Computing
• Arrays vs Lists
• Creating Arrays
• Indexing & Slicing
• Mathematical Operations
• Aggregations
Statistics
• Mean, Median, Mode
• Variance & Standard Deviation
• Introduction to Probability
• Data Distribution
• Projects
Week 4: Pandas - Core Data Handling
• Series & DataFrame
• Loading Datasets (CSV, Excel)
• Data Inspection Methods
• Data Filtering & Selection
• Handling Missing Values
• Data Cleaning & Preprocessing Basics
• Data Manipulation Projects
Week 5: Data Visualization (Matplotlib + Seaborn)
Matplotlib
• Basics of Plotting
• Charts
• Customization of Plots
• Advanced Plotting Concepts
Seaborn
• Distribution Plots
• Categorical Plots
• Relationship Plots
• Heatmaps
• Styling & Themes
Week 6: Exploratory Data Analysis (EDA)
Data Cleaning (Missing Values, Duplicates)
• Outlier Detection
• Correlation Analysis
• Feature Relationships
• GroupBy & Aggregation
• Data Storytelling
• Univariate Analysis
• Bivariate Analysis
• Multivariate Analysis
• Multiple Real-World Data Projects
Week 7: Feature Engineering + SQL + Intro to ML
Feature Engineering
• Handling Missing Values and Outliers
• Feature Scaling:
◦ Standardization
◦ Normalization
• Data Encoding:
◦ Label Encoding
◦ One-Hot Encoding
• Handling Imbalanced Datasets:
◦ Oversampling, SMOTE Concept
◦ Undersampling
• Train-Test Split Concept
SQL Basics
• Introduction to Databases
• CRUD Operations
• Aggregate Functions
• JOINs
Introduction to Machine Learning
• What is Machine Learning?
• Types of ML:
◦ Supervised Learning
◦ Unsupervised Learning
◦ Reinforcement Learning
• Linear Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Naive Bayes
Week 8: Advanced ML + Clustering + PCA + Projects
Advanced Algorithms
• Support Vector Machine (SVM)
• Decision Tree
• Random Forest
• XGBoost
Model Evaluation
• Accuracy, R-squared, Adjusted R-squared, MSE, MAE, RMSE
• Precision, Recall, F1-Score
• Confusion Matrix
• Bias vs Variance
• Overfitting vs Underfitting
Clustering & Dimensionality Reduction
• K-Means Clustering, DBSCAN Clustering and Hierarchical Clustering
• PCA (Principal Component Analysis)
• Multiple Classification, Regression & Clustering Projects
FINAL OUTCOMES
By the end of this course, students will be able to:
- Understand the difference between AI, ML, Data Science and Data Analysis
- Work with Python for data science tasks
- Use NumPy & Pandas efficiently
- Perform statistical analysis on real datasets
- Create professional visualizations with Matplotlib & Seaborn
- Perform full Exploratory Data Analysis (EDA) on any dataset
- Apply feature engineering techniques
- Handle imbalanced datasets
- Build ML models for classification & regression
- Perform clustering & dimensionality reduction with PCA
- Build end-to-end data science projects
• Series & DataFrame
• Loading Datasets (CSV, Excel)
• Data Inspection Methods
• Data Filtering & Selection
• Handling Missing Values
• Data Cleaning & Preprocessing Basics
• Data Manipulation Projects
Empowering your digital journey through education, marketing excellence, and innovative software solutions.
Visit Us
Anamnagar, Kathmandu Us
Email Us
info@techdint.com
Call Us
9864776754
© 2026 TechDint. All rights reserved.