Course Details
This comprehensive Data Science course takes you through the entire process of extracting valuable insights from data. Whether you are new to the field or aiming to strengthen your analytical skills, you will gain a strong foundation in data handling, statistical analysis, and predictive modeling.
You’ll Learn about:
- Data Analysis Fundamentals – Learn how to work with and explore datasets effectively.
- Statistics for Data Science – Apply statistical methods to interpret and analyze data.
- Machine Learning Basics – Understand key algorithms for classification, regression, and clustering.
- Data Visualization – Present insights clearly through compelling charts and dashboards.
By the end of this course, you will be able to turn raw data into actionable insights, enabling informed decision-making in real-world scenarios.
What You’ll Learn
This comprehensive course covers the complete journey of Data Science. Here are the key skills and concepts you’ll master:
- Fundamentals of Data Science – Understand its scope and applications across industries.
- Data Science Lifecycle – Explore each stage from data collection to deployment.
- Types of Data – Learn structured, unstructured, and semi-structured data and their characteristics.
- Data Cleaning & Preprocessing – Prepare, transform, and refine data for analysis.
- Statistical Methods & Probability – Apply core concepts for accurate data interpretation.
- Data Visualization – Use tools and techniques to present insights effectively.
- Machine Learning Foundations – Understand concepts and their role in data science.
- Predictive & Classification Models – Build models using popular algorithms.
- Hands-on with Python & R – Work with key libraries and programming for real-world tasks.
- Data Storytelling – Communicate findings clearly to stakeholders.
- Ethics, Privacy & Governance – Apply responsible practices in data science projects.
Pro Tip: This course blends theory with practical exercises, ensuring you gain job-ready skills while building a strong portfolio.
Course Content
Requirements
This course is designed to be beginner-friendly and is open to anyone,
regardless of prior experience or background.
Course Description
The Data Science course provides a comprehensive introduction to the
concepts, tools, and techniques used to extract meaningful insights from data.
You will explore the complete data science lifecycle, including data collection,
cleaning, preprocessing, and transformation, followed by statistical analysis
and visualization to uncover patterns and trends. The course covers both
foundational and advanced topics such as probability, machine learning
algorithms, and predictive modeling, using tools like Python, R, and industry-
standard libraries. You will gain hands-on experience in building models,
analyzing real-world datasets, and creating impactful visualizations that
support data-driven decision-making. Special emphasis is placed on data
storytelling, ethical considerations, and compliance with data privacy
regulations, ensuring you are equipped to handle data responsibly in any
professional context. By the end of the course, you will have the skills and
confidence to work on end-to-end data science projects across various
industries.
Course Content
Master the comprehensive methodology and lifecycle of data science projects from business understanding to model deployment
- Data Analytics in Practice
- Data Analytics Methodologies
- Data Science Method
- Summary
- Test Your Knowledge (5 Questions)
- Review Questions
- Integrated Environment for Data Science Projects
- Cloud-based Data Science Lifecycle
- Data Science Capabilities on the Cloud
- Lab 1 – IBM Cloud and Watson Studio
- Test Your Knowledge (5 Questions)
- Review Questions
- Business Understanding
- Explore Data
- Prepare Data
- Understanding Data
- Summary
- Lab 2 – Explore and Understand Data
- Test Your Knowledge (5 Questions)
- Review Questions
- Statistics and Representation Techniques
- Data Transformation
- Represent and Transform Unstructured Data
- Data Transformation Tool
- Summary
- Lab 3 – Data Preparation and Conversion
- Test Your Knowledge (5 Questions)
- Review Questions
- Decision-centered Visualization
- Fundamentals of Visualizations
- Common Graphs
- Common Tools
- Summary
- Lab 4 – Visualization
- Test Your Knowledge (5 Questions)
- Review Questions
- Overview of Modeling Techniques
- Machine Learning Techniques
- Accuracy, Precision and Recall
- Model Deployment
- Summary
- Lab 5 – Building and Deploying Models with AutoAI
- Test Your Knowledge (5 Questions)
- Review Questions
- About Machine Learning
- From Regression to Neural Nets
- Decision Tree Classifier
- Machine Learning Framework
- Summary
- Lab 6 – Fraud Analyzed in Jupyter Notebooks
- Lab 7 – Predicting Insurance Fraud using Images
- Test Your Knowledge (5 Questions)
- Review Questions
- Final Exam (10 Questions)
- How to Claim your Certificate
Highlights of this Course:
Upon successful completion of the course, you will receive a Course Completion Certificate and a Project Completion Certificate.
