Full Stack Data Science refers to a comprehensive approach to data science where professionals possess a broad set of skills, covering the entire data pipeline—from data collection, cleaning, analysis, and model building to deployment and scaling. A Full Stack Data Scientist not only handles the statistical and analytical aspects of data science but also understands the engineering and development processes necessary to implement data-driven solutions in production environments.
Core Components of Full Stack Data Science:
- Data Collection & Acquisition:
- Data Sourcing: Gathering data from various sources such as databases, APIs, web scraping, or streaming services.
- Database Management: Understanding of databases (SQL, NoSQL) to efficiently extract, query, and manipulate data.
- ETL (Extract, Transform, Load): Processes for moving data from its original source to a destination (e.g., data warehouse). Familiarity with tools like Apache Airflow, Talend, or custom ETL scripts is crucial.
- Data Cleaning & Preprocessing:
- Data Wrangling: Handling missing data, correcting inconsistencies, dealing with outliers, and transforming raw data into a usable format.
- Feature Engineering: Creating new features or variables from raw data that improve model performance.
- Data Normalization: Scaling and standardizing data for optimal machine learning model performance.
- Exploratory Data Analysis (EDA):
- Data Visualization: Using tools like Matplotlib, Seaborn, Plotly, or Power BI to understand data trends, distributions, and relationships.
- Statistical Analysis: Applying statistical tests and techniques (e.g., hypothesis testing, correlation, regression) to derive insights from data.
Course Features
- Lectures 4
- Quizzes 0
- Duration 80 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
Requirements
- Bachelor's degree or any equivalent
