What will you learn in What is Data Science? Course
Define data science and its role in solving real-world problems.
Describe the data science lifecycle: from problem formulation to data collection, analysis, and deployment.
Identify common tools and environments used by data scientists (Python, Jupyter, Git, SQL).
Understand key roles on a data science team and collaboration dynamics.
Recognize the ethical and societal implications of data-driven decision-making.
Program Overview
Module 1: Introduction to Data Science
⏳ 1.5 hours
Topics: What is data science; examples of data-driven projects; the impact of data science across industries.
Hands-on: Reflect on use cases in your own organization and sketch a simple data-driven question.
Module 2: Data Science Tools & Ecosystem
⏳ 2 hours
Topics: Overview of Python, Jupyter notebooks, Git/GitHub, SQL databases, and basic command-line workflows.
Hands-on: Launch a Jupyter notebook, run sample Python cells, and explore a GitHub data repository.
Module 3: The Data Science Lifecycle
⏳ 2.5 hours
Topics: Defining the problem, acquiring and cleaning data, exploratory data analysis, modeling basics, and deployment concepts.
Hands-on: Outline each step for a sample project and perform a brief data inspection in Python.
Module 4: Roles, Teams & Ethical Considerations
⏳ 1.5 hours
Topics: Data scientist vs. data engineer vs. ML engineer; teamwork and communication; ethics, bias, and privacy in data science.
Hands-on: Conduct an ethical risk assessment for a hypothetical predictive model.
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Job Outlook
Roles: Data Scientist, Data Analyst, Analytics Consultant, and Machine Learning Engineer.
Demand: Continued growth across technology, finance, healthcare, and retail—LinkedIn lists over 150,000 open data science roles in the U.S. alone.
Salaries: Entry-level data roles average $75K–$95K USD, rising to $120K+ for experienced data scientists and specialized engineers.
Growth: Mastery of the data science lifecycle and tooling opens pathways into leadership positions in AI and analytics.
Explore More Learning Paths
Take your data science knowledge to the next level with these carefully selected programs designed to enhance your skills and prepare you for a career in this fast-growing field.
Related Courses
Foundations of Data Science Course – Gain a solid foundation in data science concepts, statistical methods, and data-driven problem-solving techniques.
Tools for Data Science Course – Learn essential tools and technologies used in the data science workflow, including Python, SQL, and visualization frameworks.
Executive Data Science Specialization Course – Develop advanced data science skills for leadership roles, focusing on analytics strategy, model interpretation, and decision-making.
Related Reading
Gain deeper insight into managing and leveraging data effectively:
What Is Data Management? – Understand how data management practices support data science by organizing, securing, and optimizing the use of information for better decision-making.
Specification: What is Data Science? Course
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FAQs
- No prior coding or data experience is required.
- Provides a conceptual overview of data science workflows and lifecycle.
- Introduces tools like Python, Jupyter, Git, and SQL in an accessible manner.
- Hands-on exercises are minimal and focus on exploration, not coding depth.
- Ideal for learners considering further specialization in data science.
- Includes exercises to reflect on data-driven problems.
- Students can sketch simple data questions using sample datasets.
- Hands-on exposure is introductory and conceptual.
- Provides a safe environment for exploring tools without installation hassles.
- Helps build foundational understanding before more advanced courses.
- No deep ML or AI algorithms are covered.
- Introduces basic modeling concepts conceptually.
- Focus on data problem formulation, cleaning, and exploratory analysis.
- Prepares learners for future courses that include ML/AI.
- Emphasizes understanding roles, ethics, and collaboration in data science.
- Estimated total time: 7–8 hours.
- Self-paced with lifetime access.
- Modules range from 1.5 to 2.5 hours each.
- Flexible scheduling allows learners to proceed at their own pace.
- Ideal for busy professionals or students testing their interest in data science.
- Introduces key roles: data scientist, data analyst, ML engineer.
- Covers tools and workflows used in real-world projects.
- Highlights ethical considerations and collaborative dynamics.
- Provides a roadmap for pursuing further data science courses or certifications.
- Helps learners understand whether data science aligns with their interests.
