Their work involves collecting and cleaning data, identifying patterns, building models, and communicating findings to both technical and non-technical audiences.
A bachelor's degree in a related field is typically required, and strong communication skills are essential to translate complex data into actionable business strategies.
What data scientists do
Gather and clean data:
They find and prepare data from various sources, a process that requires persistence and a strong understanding of statistics and software engineering.
Analyze data:
They use statistical analysis and machine learning to find patterns, build models, and develop algorithms to understand everything from product usage to overall business health.
Communicate findings:
A key part of the job is communicating complex results clearly, often using data visualizations, to help team members and leadership understand the implications and make data-driven decisions.
Skills and education
Technical skills:
Expertise in programming languages like Python and R, SQL [1], and tools for data wrangling, machine learning, and visualization (e.g., Tableau [2], Power BI [3]) are crucial.
Educational background:
A bachelor's degree in mathematics, statistics, computer science, or a related field is standard. Some employers prefer or require a master's or doctoral degree.
Soft skills:
Strong communication, teamwork, problem-solving, and open-mindedness are vital for success.
Job outlook and salary
Job growth:
The Bureau of Labor Statistics projects a 36% growth for data scientists between 2021 and 2031, which is much faster than average.
Salary:
The median annual wage was $112,590 in May 2024, according to the Bureau of Labor Statistics. Salaries can vary based on factors like experience, location, and industry.
1. SQL (pronounced "ess-kew-ell" or sometimes "sequel") stands for Structured Query Language and is the standard programming language used to communicate with and manage data in relational databases. It allows users and applications to store, retrieve, modify, and delete data in an efficient and structured manner.
Key Functions and Commands
SQL is a powerful language that uses simple, English-like keywords to interact with databases. The primary functions are categorized into different language types, which include:
Command Category
Description
Key Commands
Data Query Language (DQL) Used to retrieve data from the database. SELECT
Data Manipulation Language (DML) Used to add, modify, or delete records within a table. INSERT, UPDATE, DELETE
Data Definition Language (DDL) Used to define or modify the structure of database objects (like tables, views, etc.). CREATE, ALTER, DROP
Data Control Language (DCL) Used to manage user permissions and access controls. GRANT, REVOKE
Transaction Control Language (TCL) Used to manage transactions and ensure data integrity. COMMIT, ROLLBACK
Why is SQL Important?
SQL is the backbone of modern data management and is widely used across various industries, from social media to banking applications.
Standardization: Adopted as a standard by the American National Standards Institute (ANSI) and the International Organization for Standardization (ISO), SQL ensures compatibility across most major database management systems like MySQL, Oracle, and Microsoft SQL Server.
Ease of Use: SQL's syntax is relatively easy to learn and resembles common English, making it accessible to a wide range of professionals, including data analysts and developers.
Portability: SQL code is portable and can run on various devices and platforms.
Efficiency: It can process complex queries and massive datasets quickly and efficiently, enabling rapid data retrieval and analysis that drives informed business decisions.
SQL vs. MySQL
It is important to note the difference between SQL and MySQL:
SQL is the standard language itself.
MySQL (managed by Oracle) is one of the most popular open-source relational database management systems (RDBMS) that uses SQL as its language.
2. Tableau is a visual analytics platform that transforms raw data into easily understandable and interactive visualizations, such as dashboards and charts. It is used by businesses and individuals to analyze data, find insights, and make data-driven decisions without needing to code. Key features include a drag-and-drop interface, a wide range of data source connections, and collaborative tools for sharing insights.
How it works
Connects to data:
Tableau can connect to a wide variety of data sources, including spreadsheets, databases, and cloud-based data.
Visualizes data:
It uses a drag-and-drop interface to turn data into visual formats like bar charts, line graphs, and maps.
Creates dashboards:
Users can combine multiple visualizations into interactive dashboards to tell a complete data story.
Enables collaboration:
The platform allows users to publish and share their work, enabling teams to collaborate on data analysis and decision-making.
Key uses
Business Intelligence:
It helps organizations understand business performance, identify trends, and improve operations.
Data Exploration:
Users can explore complex data to find hidden patterns and gain deeper insights.
Education:
It is used in higher education to teach data analytics and visualization skills.
Public Sharing:
A free version, Tableau Public, allows users to create and share interactive visualizations using publicly available data.
What it is not
It is not a programming language, but a tool that helps translate data into a visual format.
3. Power BI is a business analytics tool from Microsoft that allows users to connect to and visualize data from various sources, analyze it, and share interactive reports and dashboards. It is used for creating self-service business intelligence by making it easier to build reports, summarize data, and use AI-powered insights.
Key functions
Data connection and transformation: Connects to over 100 different data sources, including cloud-based and on-premises databases, files, and web services. It includes tools like the Power Query Editor for cleaning and shaping the data.
Data modeling: Allows users to create relationships between tables and build calculations using languages like DAX (Data Analysis Expressions). (DAX, or Data Analysis Expressions, is a formula language used in Microsoft business intelligence tools like Power BI, Power Pivot, and Analysis Services to create custom calculations. It is designed to work with relational data and perform complex analysis, with functions that are similar to those in Excel but also include new functions for data modeling and aggregations. You can use DAX to create calculated measures and calculated columns to add new information to your data models.)
Visualization: Offers a wide variety of built-in and custom visuals to create compelling reports and dashboards. It can create interactive reports and is ideal for displaying Key Performance Indicators (KPIs).
AI-powered insights: Includes features that provide automated insights and allow users to ask questions about their data in natural language.
Collaboration: Enables users to share reports and dashboards with colleagues, fostering self-service BI within an organization.
Components: Available as a free desktop application for Windows, a cloud-based service (Power BI Service), and embedded in other applications.
How it works
Connect to data: Use Power BI Desktop to connect to one or more data sources.
Transform data: Use the Power Query Editor to clean, shape, and transform the data as needed.
Model data: Build relationships between tables and create new measures or calculated columns using DAX.
Create visuals: Build reports using the available visuals on a report canvas.
Publish: Publish the report to the Power BI service to share it with others or to create a dashboard.
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