Course Overview
Learn Data Science to analyze data, build predictive models, and extract meaningful insights using statistics, Python, and machine learning, preparing you for roles like Data Analyst, Data Scientist, Business Analyst, or Machine Learning Engineer.
Instead of focusing only on theory, you will:
- Work with real datasets used in business and industry
- Learn how to analyze, clean, and visualize data
- Understand how to extract meaningful insights from raw data
- Build basic machine learning models for prediction and decision-making
Whether you want to become a data analyst, move into artificial intelligence, or work in tech-driven companies, this course provides a strong practical foundation.
Why Choose Data Science Training from AITC Education
Practical, Hands-On Learning
Work directly with real datasets instead of only studying formulas and theory
Industry-Relevant Tools
Learn tools and workflows used by data professionals in real companies
Project-Based Portfolio Development
Build real data analysis and machine learning projects for your resume
Expert Mentorship
Learn from experienced professionals in data analytics and machine learning
Career-Focused Curriculum
Designed to match current industry requirements in data roles
Flexible Learning Options
Available in both online and physical classes based on student needs
What You Will Be Able to Do After This Course
By the end of this training, you will be able to:
- Collect, clean, and prepare raw data for analysis
- Perform exploratory data analysis (EDA)
- Create data visualizations to explain insights clearly
- Work with spreadsheets and programming tools for analysis
- Build basic machine learning models for prediction
- Understand patterns and trends in business data
- Work on real-world data science projects
- Present insights in a structured and professional way
Tools and Technologies You Will Use
- Python – Core programming language for data science
- Pandas – Data manipulation and analysis
- NumPy – Numerical computing
- Matplotlib / Seaborn – Data visualization
- Scikit-learn – Machine learning models
- Jupyter Notebook – Interactive coding environment
- Excel – Basic data handling and reporting
- SQL – Database querying and data extraction
Who This Data Science Course is For
- Beginners interested in data and analytics careers
- Students from IT, management, or engineering backgrounds
- Professionals looking to switch into data-related roles
- Freelancers interested in analytics and reporting projects
- Anyone interested in machine learning and AI foundations
No advanced math or programming experience is required. The course starts from the fundamentals.
Career Opportunities in Nepal and Online
After completing this training, you can work as:
- Data Analyst
- Junior Data Scientist
- Business Intelligence Analyst
- Machine Learning Assistant
- Data Reporting Specialist
Career opportunities exist in:
- IT companies in Nepal
- Banks and financial institutions
- E-commerce and startup companies
- Research and analytics firms
- Freelancing platforms for data projects
Portfolio Development Projects
During the course, you will complete:
- Data cleaning and analysis projects
- Business insights reporting project
- Data visualization dashboard
- Basic machine learning prediction model
- Final capstone data science project
These projects will help you demonstrate real-world problem-solving skills.
Certification
You will receive a Certificate of Completion after successfully finishing the course and completing all required projects.
Syllabus
- 8 Sections
- 37 Lessons
- 12 Weeks
- Introduction to Data Science and Tools4
- 1.1What is Data Science? Lifecycle and Use Cases
- 1.2Roles: Data Analyst, Scientist, Engineer
- 1.3Python for Data Science: Jupyter, Anaconda, Git
- 1.4Libraries Overview
- Mathematics & Statistics for Data Science3
- 2.1Linear Algebra Essentials
- 2.2Probability & Statistics
- 2.3Calculus & Optimization
- Data Collection & Preprocessing5
- 3.1Data Sources: CSV, APIs, Databases, Web Scraping
- 3.2Cleaning Data: Handling Missing, Duplicates, Outliers
- 3.3Feature Engineering: Encoding, Scaling, Binning
- 3.4Data Transformation: Aggregation, Pivoting, Grouping
- 3.5Mini Project 1: Student Performance Analysis
- Exploratory Data Analysis & Visualization4
- 4.1Univariate, Bivariate, Multivariate Analysis
- 4.2Correlation Matrix, Pairplots, Heatmaps
- 4.3Histograms, KDE Plots, Boxplots
- 4.4Dashboarding Tools (optional): Streamlit or Dash
- Supervised Machine Learning3
- 5.1Classification Algorithms
- 5.2Regression Algorithms
- 5.3Mini Project 2: Student Performance Prediction
- Image Classification & Face Recognition7
- 6.1Image Data Basics: Pixels, Channels, Arrays
- 6.2Preprocessing: Resizing, Normalizing, Augmenting
- 6.3CNN from Scratch: Convolutions, Filters, Pooling
- 6.4Transfer Learning: Using Pretrained Models
- 6.5Face Recognition: Face Detection (Haar/YOLO), Embeddings, FaceNet
- 6.6Project 3: Face Recognition System
- 6.7Project 4: Image Classification (Cats vs Dogs or Custom)
- Time Series & Forecasting6
- 7.1Time Series Basics: Trends, Seasonality, Stationarity
- 7.2Data Preparation: Indexing, Resampling, Rolling Mean
- 7.3ARIMA & SARIMA (with ACF/PACF Explanation)
- 7.4Facebook Prophet for Quick Forecasting
- 7.5LSTM for Sequential Prediction
- 7.6Project 5: Stock Price Prediction
- Final Capstone & Deployment5
- 8.1Model Selection & Comparison
- 8.2Saving Models with Pickle / Joblib
- 8.3Streamlit or Flask for Web App Deployment
- 8.4GitHub + Documentation Best Practices
- 8.5Presenting Project Findings
