What will you learn in Data Science Foundations Specialization Course
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Build a foundational understanding of data science processes, including data collection, analysis, predictive modeling, and algorithmic thinking using flowcharts and pseudocode.
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Gain hands-on skills in Python, R, SQL, Jupyter Notebooks, and GitHub, applying them to real datasets.
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Learn basic machine learning and predictive modeling, including regression and clustering.
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Practice fundamental data visualization and dashboard creation.
Program Overview
1. What is Data Science?
🕒 Duration: 1 week
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Topics: Defining data science and its relevance today. Introduction to data science roles and applications.
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Hands-on: Reflection exercises to connect course content to real-world examples.
2. Tools for Data Science
🕒 Duration: 2 weeks
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Topics: Jupyter notebooks, RStudio, GitHub, SQL, Python basics.
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Hands-on: Practice labs in Jupyter and RStudio Cloud environments.
3. Data Science Methodology
🕒 Duration: 2 weeks
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Topics: Nine-step data science methodology for problem solving, from business understanding to deployment.
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Hands-on: Mapping a methodology to a practical case scenario.
4. Python for Data Science, AI & Development
🕒 Duration: 2 weeks
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Topics: Python basics, data structures, functions, and libraries like Pandas and Numpy.
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Hands-on: Writing Python scripts and using real-world data in coding exercises.
5. Databases and SQL for Data Science
🕒 Duration: 2 weeks
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Topics: Relational databases, SQL queries, JOIN operations, and database design.
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Hands-on: Writing SQL queries in cloud-based database tools.
6. Data Analysis with Python
🕒 Duration: 2 weeks
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Topics: Exploratory data analysis, regression models, and data visualization.
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Hands-on: Data manipulation with Pandas and visualizations using Seaborn/Matplotlib.
7. Data Visualization with Python
🕒 Duration: 2 weeks
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Topics: Creating plots with Matplotlib, Seaborn, and Folium. Best practices in visualization.
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Hands-on: Building complex, multi-layered visualizations from datasets.
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Job Outlook
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Entry-level pathways: Data Analyst, Business Intelligence Associate, Junior Data Scientist, SQL Analyst.
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Skills in Python, R, SQL, visualization, and ML basics are highly applicable to sectors like finance, healthcare, consulting, and public policy.
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Strong credential for resumes, especially for non-technical professionals breaking into data-driven roles.
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Potential salary: ₹5 L–12 L in India; $60K–$90K in the U.S. for junior analytics positions.
Explore More Learning Paths
Strengthen your data science foundation with these hand-picked courses designed to help you master the tools, methodologies, and essential concepts needed for a successful data-driven career.
Related Courses
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Tools for Data Science Course – Learn essential data science tools, including Python, SQL, and data visualization platforms.
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Data Science Methodology Course – Understand the end-to-end methodology of data science projects, from problem definition to model deployment.
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Foundations of Data Science Course – Build a solid understanding of data analysis, statistical reasoning, and foundational concepts in data science.
Related Reading
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What Is Data Management? – Discover how effective data management underpins successful data science workflows and decision-making.
