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Analytical Maths Using Python for Junior School for School Children
Week 1: Introduction to Data Science and AI
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Overview of Data Science and AI
- Definition of Data Science and AI
- Historical background and evolution
- Importance and impact on society
- Real-world applications of Data Science and AI
- Healthcare, transportation, entertainment, etc.
- Introduction to Python and setting up the environment
- Installing Python and Jupyter Notebook
- Introduction to Google Colab
- Assignment:
- Install Python and Jupyter Notebook on your computer or set up Google Colab
Session 2
- Topics:
- Basic Python syntax and data types
- Variables, data types (integer, float, string, boolean)
- Basic operations and expressions
- Writing simple Python programs
- Input and output functions
- Basic control structures (if-else, loops)
- Assignment:
- Write a Python program to print "Hello, World!" and another to take user input and print a greeting
Week 2: Understanding Data
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- What is data? Different types of data: qualitative vs. quantitative
- Definitions and examples
- Data in everyday life (surveys, measurements, etc.)
- Measures of central tendency: mean, median, and mode
- Definitions and calculations
- When to use each measure
- Assignment:
- Collect a small dataset (e.g., heights of classmates) and calculate mean, median, and mode using Python
Session 2
- Topics:
- Cardinal vs. Ordinal Data
- Definitions, examples, and differences
- Simple exercises to identify types of data
- Interactive examples and quizzes
- Assignment:
- Classify given data into cardinal and ordinal categories and justify your classification
Week 3: Basics of Statistics
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Introduction to probability
- Basic concepts and terminology
- Simple probability calculations and examples
- Writing Python programs for probability exercises
- Simulating random events (coin toss, dice roll)
- Assignment:
- Write a Python program to simulate the roll of a dice and calculate probabilities of different outcomes
Session 2
- Topics:
- Cardinal vs. Ordinal Data
- Definitions, examples, and differences
- Simple exercises to identify types of data
- Interactive examples and quizzes
- Assignment:
- Classify given data into cardinal and ordinal categories and justify your classification
Week 4: Data Collection and Cleaning
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Introduction to data cleaning: handling missing values, removing duplicates
- Practical examples and hands-on exercises
- Data cleaning using Python libraries (Pandas)
- Introduction to Pandas library
- Basic data manipulation with Pandas
- Assignment:
- Clean the collected survey data using Python (remove duplicates, handle missing values)
Session 2
- Topics:
- Cardinal vs. Ordinal Data
- Definitions, examples, and differences
- Simple exercises to identify types of data
- Interactive examples and quizzes
- Assignment:
- Classify given data into cardinal and ordinal categories and justify your classification
Week 5: Data Preparation
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Data preprocessing techniques
- Standardization, normalization, and encoding
- Data transformation and normalization
- Why normalization is important
- How to normalize data using Python
- Assignment:
- Normalize a given dataset using Python and visualize the changes
Session 2
- Topics:
- Splitting data into training and testing sets
- Importance of splitting data for machine learning models
- Introduction to basic data preprocessing tools in Python
- Practical examples and hands-on exercises
- Assignment:
- Split a sample dataset into training and testing sets using Python
Week 6: Data Presentation
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Importance of data presentation
- Effective communication of data insights
- Different methods of presenting data
- Tables, charts, graphs, reports
- Assignment:
- Create a simple presentation of a dataset (using PowerPoint, Google Slides, or Jupyter Notebook)
Session 2
- Topics:
- Introduction to Matplotlib for creating plots and graphs
- Basic plotting functions and customization
- Creating simple plots using Python
- Bar charts, line graphs, histograms
- Assignment:
- Create a bar chart and a line graph using Matplotlib with a given dataset
Week 7: Data Visualization
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Advanced data visualization with Seaborn
- Introduction to Seaborn library
- Creating advanced plots (heatmaps, box plots, pair plots)
- Customizing plots for better readability
- Assignment:
- Visualize a dataset using Seaborn and customize the plots
Session 2
- Topics:
- Introduction to plotly for interactive visualizations
- Overview of plotly and its features
- Creating interactive plots using plotly
- Step-by-step guide to creating interactive charts
- Assignment:
- Create an interactive plot using plotly with a given dataset
Week 8: Introduction to Machine Learning
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Basics of machine learning
- Definition and importance
- Types of machine learning: supervised vs. unsupervised
- Applications of machine learning
- Real-world examples and case studies
- Assignment:
- Research and list 3 examples of machine learning in daily life and explain how they work
Session 2
- Topics:
- Introduction to scikit-learn
- Overview of scikit-learn library
- Basic functions and usage
- Building a simple machine learning model
- Step-by-step guide to creating a linear regression model
- Assignment:
- Build a simple linear regression model using scikit-learn and evaluate its performance
Week 9: AI Tools and Applications
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Introduction to AI tools like TensorFlow and Keras
- Overview of TensorFlow and Keras
- Basic concepts and usage
- Basics of neural networks
- Introduction to neural networks
- How neural networks work
- Assignment:
- Install TensorFlow and Keras and write a simple program using these libraries
Session 2
- Topics:
- Building a simple neural network
- Step-by-step guide to creating a neural network
- Training and testing the neural network
- Training data, validation data, and testing data
- Evaluating the performance of the neural network
- Assignment:
- Train a simple neural network on a dataset and evaluate its performance
Week 10: Advanced Data Analysis
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Advanced data analysis techniques
- Clustering, classification, and regression
- Using Python for advanced data analysis
- Practical examples and hands-on exercises
- Assignment:
- Perform an advanced data analysis on a given dataset using clustering or classification techniques
Session 2
- Topics:
- Time series analysis
- Introduction to time series data
- Analyzing time series data using Python
- Practical examples and hands-on exercises
- Assignment:
- Analyze a time series dataset and visualize the results
Week 11: Project Development
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Introduction to the course project
- Project guidelines and expectations
- Choosing a project topic
- Planning and organizing the project
- Assignment:
- Start planning the course project: define the problem, collect data, and outline the analysis
Session 2
- Topics:
- Work on the course project
- Guidance and troubleshooting
- Applying the skills learned throughout the course
- Assignment:
- Continue working on the course project: perform data analysis and visualization
Week 12: Project Presentation and Review
- Duration : 1.5hours/week, 3 hours of assignments
Session 1
- Topics:
- Presentation of projects by students
- Each student presents their project
- Discussion and feedback
- Assignment:
- Finalize and submit the project: prepare a presentation summarizing the findings
Session 2
- Topics:
- Course review and key takeaways
- Recap of important concepts and skills
- Discussion on future learning paths and resources
- Q&A session and final thoughts
- Assignment:
- Reflect on the course and provide feedback: What did you learn? What did you enjoy the most? What could be improved?
US / Dubai / Singapore Price
$100 / month
- Course Duration : 12 weeks/ 3 months
INDIA PRICE
(For residents of India Only)
₹6000
- Course Duration : 12 weeks/ 3 month
Pay using QR Code
Experts
Sourish Sarkar
Master of Science in QMS
Indian Statistical Institute
Statistical Quality Control , Operation Research