Data Science Explained: Exploring the fundamental concepts and applications of Data Science.
Importance of Data in Decision Making: Discussing how data influences business and organizational decisions.
Data Related Roles and Responsibilities: Understanding the variety of roles in the data science field.
Skillset Required for Data Science: Reviewing the essential skills needed for a career in Data Science.
Data Science Overview: Providing a general overview of the Data Science field.
Understanding Business Objectives: Grasping the goals and objectives in data science projects.
Problem Identification: Identifying and defining problems to be solved by data science.
Defining Success Metrics: Establishing criteria for success in data science initiatives.
Scope Definition: Outlining the boundaries and scope of data science projects.
Data Understanding & Availability: Assessing the availability and comprehension of necessary data.
Feasibility Study: Conducting studies to evaluate the practicality of the project.
Formulating Hypotheses: Developing hypotheses for data-driven investigation.
Project Planning: Effectively planning and structuring data science projects.
Understanding Data Needs: Recognizing the specific data requirements for a project.
Data Sources Identification: Identifying potential sources of data.
Data Acquisition Methods: Learning about different methods for collecting data.
Data Formats and Storage: Understanding various data formats and storage options.
Data Quality Assessment: Evaluating the quality of collected data.
Data Cleaning and Preprocessing: Preparing data for analysis through cleaning and preprocessing.
The Data Analysis Process: Delving into the step-by-step process of data analysis.
Types of Data Analysis: Exploring different methodologies in data analysis.
Exploratory Data Analysis: Conducting preliminary analysis to understand data patterns.
Statistical Analysis Fundamentals: Understanding basic statistical methods in data analysis.
Data Visualization: Using visual tools to represent data for easier comprehension.
Introduction to Machine Learning: Exploring the basics of machine learning.
Model Selection and Evaluation: Learning how to select and assess the performance of models.
Supervised and Unsupervised Learning: Discussing two primary types of machine learning.
Feature Engineering and Selection: Understanding the importance of feature selection in modeling.
Data Preprocessing for Machine Learning: Preparing data specifically for machine learning applications.
Overfitting and Underfitting: Learning about common pitfalls in model training.
Model Training: Delving into the process of training data models.
Ensemble Methods: Exploring advanced techniques in machine learning.
Understanding Your Audience: Tailoring data communication to different audiences.
Data Storytelling: Mastering the art of narrating data-driven stories.
Principles of Effective Data Visualization: Learning key principles for effective data visualization.
Writing Effective Reports and Presentations: Developing skills for creating impactful reports and presentations.
Interactive Data Tools and Dashboards: Utilizing tools for interactive data analysis and presentation.
Continuous Improvement – The ‘Kaisen’ Strategy: Embracing ongoing improvement in data science practices.
Project Selection and Proposal: Choosing and outlining a project that showcases data science skills and interests.
Project Methodology: Implementing a comprehensive approach that includes gathering and analyzing data, and developing and validating models for accuracy.
Project Deliverables: Presenting results with a well-crafted narrative and effective data visualization.
Feedback and Peer Review: Participating in constructive feedback sessions and peer reviews.
Reflection on Learning: Reflecting on the learning journey and skills developed throughout the course.