Data Science Learning Resources
Data science impacts the real world in a variety of ways. For example, data scientists build models to predict future outcomes of certain situations. This is known as predictive modeling. This model can predict stock price changes, what equipment will likely fail next, and how consumers will behave over the next year. Data science is helpful when creating customer insights.
Large amounts of data are mined and analyzed to understand how customers behave and why they make those choices. Data science can identify and predict fraud by analyzing large amounts of transactions. Another critical real-world application is in healthcare. Data is analyzed to improve patient outcomes. By analyzing large amounts of data, healthcare providers can better understand how specific symptoms behave to produce better treatments.
One way that data science impacts almost everyone’s life in today’s world is through recommendation systems. For example, when a streaming service suggests certain movies or television shows, it uses data science to make those recommendations. It works the same way when an e-commerce platform offers specific shoes or other items using data it analyzes from previous purchases and things the customer has searched for and used in the past.
What Is Data Science?
Data science is an interdisciplinary field. That means the field of data science uses parts of many other areas. Data scientists use statistical, computational, and domain-specific methods to extract insights and knowledge from data. Once they pull the data, they search for patterns in the data that give information that helps people make decisions and even help push more tasks toward complete automation. Data science, for example, is one of the things that makes AI possible.
- What Is Data Science?
- What is Data Science? The Ultimate Guide
- Defining Data Science (PDF)
- Why Is It Hard to Define Data Science?
- A Very Short History Of Data Science
- Data Science
Careers in Data Science
Studying data science opens up various career paths in fields as diverse as retail, finance, healthcare, and technology. Titles given to people working in data science differ. It’s easy to understand that data scientists, data engineers, data analysts, and big data engineers are all working in the broad field of data science. Job titles include positions like business intelligence analyst, data visualization specialist, and artificial intelligence engineer.
Colleges and universities offer a variety of degrees that relate to data science. Common majors for people interested in pursuing a career in data science include statistics, applied mathematics, information systems, economics, and physics.
- Exploring Careers in Data Science One Byte at a Time
- What is a Data Scientist?
- 12 Data Science Careers To Pursue (Plus Salaries and Duties)
- Data Scientists: Occupational Outlook
- What Does a Data Scientist Do?
- A Complete Guide To Data Science Career Path
Computer Coding and Programming
Data science and computer coding are closely related and overlap in several ways. Many data science tasks require proficiency in coding, such as data cleaning, feature engineering, model building, and deployment. The most common programming languages used in data science include Python, SQL, Java, and Scala. Data scientists use these programming languages to manipulate data, build algorithms, create visualizations, and deploy machine learning models.
- Coding Habits for Data Scientists
- Ten Good Coding Practices for Data Scientists
- Data Science vs. Software Engineering: Key Differences
- How Much Coding is Required for Data Science for Beginners?
- The Basics of Coding and Programming That You Need to Know
- 15 Free Tools to Learn the Basics of Coding
Data Analytics and Statistics
Data analysis and statistics are essential parts of data science. Data analysis involves cleaning, transforming, and modeling data to gather meaningful insights and information. Statistics provides the mathematical foundation for data analysis and is used to test hypotheses, make predictions, and draw conclusions based on data. Statistical techniques such as regression, hypothesis testing, and machine learning are used in data science to analyze and model data.
These techniques help data scientists to identify patterns, make predictions, and make informed decisions based on data. Together, data analysis and statistics form the cornerstone of data science and play a critical role in extracting insights and making informed decisions from data.
- The Difference Between Data Analytics and Statistics
- Statistics vs. Analytics: What’s the Difference?
- Difference between Data Analysis and Statistical Analysis
- What Is Statistical Analysis?
- Data Analytics: What It Is, How It’s Used, and 4 Basic Techniques
- Best Statistical Tools in Research and Data Analysis