Forum Discussion
Getting started with data science
Hi Dokta1017 ,
Thank you for reaching out to the Microsoft Community Forum.
For getting started, begin with the fundamentals, Python basics, SQL querying, and introductory statistics. Once you are comfortable with these core building blocks, you can start applying them in practical scenarios.
Microsoft Fabric provides an end-to-end environment where you can practice notebook development, Lakehouse data preparation, and visualization with Power BI all in one integrated experience.
The below Microsoft doc provides a guided learning path for beginners to start hands-on with Fabric data science workloads:
Data science in Microsoft Fabric - Microsoft Fabric | Microsoft Learn
Data science tutorial - get started - Microsoft Fabric | Microsoft Learn
This helps you progress step by step from basic concepts to applying them in real projects within a scalable environment. If you have a particular use case in the future, you’re welcome to share it here.
Thank you.
Step 1: Learn Core MathematicsStatistics: Focus on probability, distributions, and hypothesis testing.Linear Algebra: Learn matrices, vectors, and eigenvalues.Calculus: Understand derivatives and gradients for optimization.Step 2: Master Programming & SQLPython: Learn core syntax, loops, functions, and data structures.SQL: Master querying databases, joins, aggregations, and subqueries.Step 3: Learn Data Analysis & VisualizationPandas & NumPy: Master data manipulation and cleaning.Matplotlib & Seaborn: Learn to create charts and plots.EDA: Practice Exploratory Data Analysis to find trends.Step 4: Understand Machine Learning BasicsScikit-Learn: Learn this primary Python library for modeling.Algorithms: Study regression, classification, and clustering techniques.Evaluation: Understand metrics like accuracy, precision, and recall.Step 5: Build Projects & Share PortfolioKaggle: Practice on real-world datasets and entry-level competitions.GitHub: Store your clean code and document your projects.Portfolio: Create 2-3 end-to-end projects showing data cleaning to prediction.