Data science can be intimidating if you’re just starting out. But with the right tools, anyone can get started with data science and data mining projects.
Getting Started with Data Science and Data Mining Projects
Below, we will cover all the steps you need to get open running with data science and data mining quickly and easily.
Data Mining and Data Science Defined
Data science is the discipline dedicated to analyzing and making use of data using statistical methods. It is related to computer science, mathematics, and similar disciplines, such as engineering.
In a business context, data science typically focuses on using data to answer business questions and solve problems.
The data science pipeline includes steps such as:
- Defining the business problem. Since every data science application is designed to solve a problem, it is important to clarify that problem from the outset.
- Data collection. Once the problem has been defined, data sources must be selected and collected. Examples include websites, social media, physical sensors, process mining data, and much more.
- Data cleaning. Data that has been collected often includes a great deal of noise. That is, not all of the data is useful. Some of it may be mislabeled, some of it may not be labeled at all, some of it may be inappropriate, and so forth.
- Data mining. Data mining is the process of looking for patterns in data and turning those patterns into models.
- Data modeling. A data model, or data structure, is a visual representation of data designed to illustrate the connections between data points.
- Data exploration. Once data has been modeled, the relationships and potential insights of that data are explored.
- Interpretation. Finally, data is interpreted and turned into a “story” that can be used to inform business decisions and address the original business problem.
Although higher-level data science projects require experience and knowledge, beginner level projects do not.
Must-Have Tools for Data Science and Data Mining Projects
As a data scientist, one of your most critical jobs is data mining, which requires more technical expertise and insights than other steps, such as data cleaning.
If you are seriously interested in data science and data mining, it is important to have a bit of a background, or at least an interest in, tools and technologies such as:
- Python. Python is the most popular programming language in the world for applications such as artificial intelligence, machine learning, and data science. If you were just getting started with data science and data mining, begin with the Python programming language.
- R. R is another programming language that is useful for data science. It is most commonly used in fields related to statistics, data science, and mathematics.
- Java. Java is another programming language. Although it is not as easy to learn as Python, it can be more useful in certain scenarios that require high-performance applications, such as industrial and commercial data science projects.
- SQL and MySQL. SQL stands for Structured Query Language. This is a language used in relational databases, one of the most common types of databases used for data science applications.
- Talend. Talend is an open source data integration tool used for data preparation, integration, statistics, cleaning, and more.
- Mozenda. Mozenda is a web-scraping tool useful for gathering data for projects that make use of web data.
- Amazon Redshift. Amazon Redshift is a managed data warehouse service. As a cloud service, it is operated on a pay-per-use basis, which makes it both affordable and scalable.
- Google BigQuery. Like Amazon Redshift, Google BigQuery it’s a cloud based, scalable data warehouse.
- Snowflake. Snowflake is another data warehouse that requires no management, supports a variety of types of business data, is scalable, and performs well.
- Apache Spark. Apache spark is an analytics engine that can process data in real time. It is high-performance includes a number of API’s that can be programmed with Python, R, Java, and more.
- BigML. BigML is a graphical environment that is easy to use. It has features making it particularly useful for business use cases, such as web interfaces that make it easy to visualize data.
- MATLAB. MATLAB is a virtual mathematics laboratory that is popular with data scientists. It can be used for tasks spanning the entire data science pipeline, from cleaning to modeling.
- Tableau. Tableau is a graphical data science tool that can be learned regardless of your coding ability. It is ideal for visualizing data, real-time data analysis, and creating data dashboards.
- PyTorch. PyTorch is a machine learning library for Python. It is open-source, easy to install and use, and free.
- TensorFlow. TensorFlow is a Google AI framework. Like PyTorch, it is free framework that can be used with Python.
Data science is a vast field. The tools listed here are only a few of the many that you can use to get started with data science and data-driven methods. This list does, however, offer enough to get you started.