Graphics Design

About Course

Graphic design is the art of visual communication, combining creativity and technology to convey ideas, messages, or information through visually appealing and impactful designs. It encompasses various elements such as typography, images, colors, shapes, and layout to create designs that captivate, inform, and evoke emotions. Graphic designers use tools like software applications (e.g., Adobe Photoshop, Illustrator) to craft designs for a wide range of purposes, including advertisements, logos, posters, websites, packaging, and more. Effective graphic design not only captures attention but also communicates a specific message or brand identity, making it an essential component in today’s visually driven world.

About Winquest

WinQuest online provides live, interactive classes on Graphic Design. With highly qualified tutors, and consistent communication between teacher and student, our classes are suitable for beginners and advanced students alike. Avail for 1:1 sessions for personalized care, or choose group sessions to connect and study with other like-minded individuals. Flexible scheduling is done, allowing students to join sessions based on their availability.

Course Content

Prerequisites

Proficiency in Python programming.
Understanding of basic programming concepts (variables, loops, functions).
Familiarity with data structures (lists, dictionaries).
Basic knowledge of mathematical concepts (algebra, statistics).

Requirements

A laptop or desktop to connect.
Stable internet connection.
Python (3.6 or higher) with libraries: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn.
Jupyter Notebook for interactive coding and documentation.

Course Content:

Age Group: 13+ Duration: 25 hours
Units	Description
Lesson 1: What is Data Science?	Understanding the role of data science in real-world applications. Introducing the data science life cycle.
Lesson 2: Python for Data Science	Brief Python recap. Overview of essential libraries: NumPy, Pandas, Matplotlib.
Lesson 3: Data Collection and Sources	Exploring different data sources: CSV, APIs, databases. Retrieving data using libraries like requests or pandas.
Lesson 4: Data Cleaning and Preprocessing	Identifying and handling missing values, outliers, and inconsistencies. Transforming and shaping data for analysis.
Lesson 5: Data Visualization	Creating meaningful visualizations using Matplotlib and Seaborn. Analyzing distributions, trends, and relationships.
Lesson 6: Statistical Analysis	Basic statistical concepts: mean, median, variance, etc. Hypothesis testing and interpreting results.
Lesson 7: Introduction to Machine Learning	Overview of supervised and unsupervised learning. Choosing the right algorithm for the task.
Lesson 8: Model Training and Evaluation	Splitting data into training and testing sets. Evaluating model performance with metrics like accuracy and confusion matrix.
Lesson 9: Feature Engineering	Selecting and transforming features for improved model performance. Dealing with categorical data and encoding techniques.
Lesson 10: Dimensionality Reduction	Reducing data dimensions using techniques like PCA. Visualizing high-dimensional data in lower dimensions.
Lesson 11: Time Series Analysis	Understanding time-dependent data. Techniques for analyzing and forecasting time series data.
Lesson 12: Project 1 – Exploring a Real Dataset	Students choose a real-world dataset and apply data cleaning, EDA, and basic analysis.
Lesson 13: Project 2 – Predictive Modeling	Students build a predictive model using machine learning algorithms. Documenting the entire process, from data preparation to model evaluation.