Data Science involves: Statistics, Excel, Linear Algebra, Power BI, Machine Learning, SQL
β±οΈ Length: 31.3 total hours
β 4.28/5 rating
π₯ 4,506 students
π May 2025 update
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- Course Overview
- This “Complete Road Map for Data Science & ML for Beginners” serves as your comprehensive entry point into the world of data science and machine learning. Designed for absolute beginners, it offers a structured, step-by-step journey through critical concepts and practical applications, demystifying complex data theories.
- With 31.3 hours of content, this curriculum provides a holistic view of the data lifecycle, from initial understanding to actionable insights and predictive model building. The focus is on both theoretical understanding and hands-on proficiency, ensuring you grasp the ‘why’ behind the ‘what’.
- Boasting a strong 4.28/5 rating from over 4,506 students and a May 2025 update, this course ensures quality and contemporary relevance. It uses a project-based learning methodology, reinforcing knowledge through practical exercises and real-world scenarios.
- Embark on this journey to gain the essential toolkit for navigating the modern data landscape, empowering you to contribute effectively to data-driven projects and establish a solid career foundation adaptable to future industry trends.
- Requirements / Prerequisites
- No prior experience in programming or advanced mathematics is necessary; this course is explicitly created for beginners.
- Basic computer literacy and comfort with general software navigation are helpful.
- A stable internet connection and a personal computer (Windows, macOS, or Linux) capable of running Python environments and Power BI Desktop (free version) are required.
- Enthusiasm, a willingness to learn, and an eagerness to solve real-world problems with data are the key prerequisites for success.
- Skills Covered / Tools Used
- Statistical Foundations: Develop a strong understanding of essential statistical principles, including hypothesis testing, probability, and correlation, vital for interpreting data and drawing reliable conclusions.
- Excel for Data Handling: Master advanced Excel functionalities for efficient data cleaning, transformation, and initial visualization using pivot tables and compelling charts.
- Linear Algebra for ML Insight: Grasp fundamental concepts of linear algebra (vectors, matrices) that underpin machine learning algorithms, providing a conceptual framework for model operation.
- Power BI for Business Intelligence: Gain expertise in designing interactive dashboards and reports with Power BI, connecting diverse data sources, modeling data, and creating impactful visualizations for strategic decision-making.
- Machine Learning Model Application: Learn to build, train, and evaluate various machine learning models for predictive analytics, understanding algorithm selection, hyperparameter tuning, and performance assessment.
- SQL for Database Interaction: Become proficient in SQL to query, filter, and aggregate data from relational databases, mastering essential commands for data extraction and integrity.
- Python & Jupyter Ecosystem: Acquire practical experience with Python and its key libraries (Pandas, NumPy, Scikit-learn, Matplotlib) within Jupyter Notebooks, fundamental for data manipulation, analysis, and ML implementation.
- Data Cleaning & Preprocessing: Understand crucial techniques for handling missing values, outliers, and inconsistencies, transforming raw data into an analysis-ready format.
- Effective Data Communication: Develop skills to articulate complex data findings and insights clearly to various audiences, translating technical results into actionable business narratives.
- Benefits / Outcomes
- Career Readiness: Be prepared for entry-level roles such as Data Analyst or Junior Data Scientist, equipped with a comprehensive understanding of the data science workflow.
- Project Portfolio: Build a robust portfolio of five practical data science projects, showcasing your hands-on skills and problem-solving abilities to potential employers.
- End-to-End Data Mastery: Gain the ability to manage the entire data lifecycle, from acquisition and cleaning through analysis, model building, and impactful visualization.
- Enhanced Analytical Mindset: Cultivate strong analytical thinking, enabling you to interpret complex data, identify trends, and make informed, evidence-based decisions.
- Core ML Expertise: Acquire a solid practical and theoretical foundation in machine learning, enabling you to understand, implement, and critically evaluate predictive models.
- Versatile Tool Proficiency: Achieve proficiency in industry-standard tools like SQL, Excel, Power BI, and Python, making you a highly adaptable professional in the data industry.
- PROS
- High Student Satisfaction: A 4.28/5 rating from over 4,500 students reflects consistent quality and effective learning.
- Up-to-Date Curriculum: The May 2025 update ensures all content, tools, and techniques are current and relevant.
- Beginner-Friendly & Comprehensive: Offers a complete, structured roadmap for novices, covering a broad spectrum of essential data science and ML topics.
- Hands-on Project Experience: Five practical data science projects with IPython Notebooks provide invaluable real-world application and portfolio-building opportunities.
- Broad Skill Set: Covers key areas like Statistics, Excel, Linear Algebra, Power BI, Machine Learning, and SQL, creating well-rounded data professionals.
- CONS
- Foundational Depth: While comprehensive in breadth, the course provides foundational rather than deep, specialized expertise in any single advanced topic, potentially requiring further dedicated study for niche areas.
Learning Tracks: English,IT & Software,Other IT & Software
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