Every day, software companies and technology corporations are coming out with new data science tools and programs. While many of these tools act as excellent resources for students and professionals new to the data science industry, many data science tools require advanced specialization and knowledge of the field in order for them to be useful. So it is important for beginners in the field to have a good grasp on what tools offer an introduction to data science and which tools require more advanced knowledge and experience. This article includes a brief overview of the primary uses of data science tools as well as a data science toolkit for beginners, including a curated list of the software and programs that you can use to start and complete a simple data science project.
Why Do You Need Data Science Tools?
But first things first, Why are there so many data science tools to choose from, and what are they being used for? As a field and area of research which is relevant to multiple industries, the multiplicity of data science tools reflects the variety of data science projects that students and professionals may be required to take on as well as the complexities of data types and categories.
In addition, completing a data science project is a multi-step process, and while many data science tools have an end-to-end design that encompasses all of these steps in one program, many data science tools correspond to a specific aspect of the process of working with data. The list below includes some of the primary categories that data science tools tend to fall into:
- Data Collection and Organization tools are primarily used to contain and keep data safe after it has been collected.
- Database Management tools are used to store, prepare, and annotate data in a way that makes it easier to search, analyze, and understand.
- Data Exploration and Analysis tools can be used to uncover the relationships between data, patterns, trends, and other information which can be extrapolated from the data.
- Data Modeling and Visualization tools offer more aesthetically pleasing options for communicating data through different types of graphs, charts, and other methods of representing the findings of a dataset.
The Beginner Data Science Tool-Kit
Keeping these different uses in mind, the following list acts as an example of tools that would be most useful for anyone who is a beginner in data science. As a data science toolkit, this list offers a combination of tools that can be used together to complete a data science project from start to finish.
1. Microsoft Excel
Commonly used within the world of business and finance, Microsoft Excel is a great beginner data science tool when working through the first step of a data analysis project i.e. Data collection. Offering an efficient and straightforward visual structure, the rows and columns format of Excel makes it easy to keep a dataset clean and contained. In addition, Microsoft Excel produces .csv files which make it easier to import any data that you collect in Excel to other types of software or programs. Microsoft Excel can also be used for data organization and cleaning, simple statistical analyses, as well as creating graphs and visualizations. This well-known tool is also capable of completing an entire data science project if you are working with smaller datasets. However, if you want to work on more complex projects, continue reading for data science tools that were specially created to handle larger datasets.
2. MySQL
Known for its uses as a relational database management system, MySQL is an essential data organization tool for data science beginners and experts alike. For students that are working with Excel data, it is easy to import .csv files into the MySQL platform. From there, you can use this tool to clean and organize your data in a way that makes it easy to create tables, search, and find missing data or remove data that you do not need. Although MySQL is commonly used with the SQL programming language, this program is also compatible with other programming languages, such as R and Python, when using language-specific packages.
3. Python
As one of the most popular programming languages for data analysis, Python is known for its ease of use due to its grammar and syntax. Based on English keywords and commands, learning how to read and write code and create programs in this commonly used language requires little prior training or education in computer science or statistics. Due to its popularity, there are also hundreds and thousands of online resources that are compatible with completing data science projects in Python. Whether you need a data science library or step-by-step instruction on how to complete an analysis of a dataset, the Python community offers a lot of support for beginners in data science.
4. Tableau
Tableau is a popular visual analysis platform that is not only used for data visualization and modeling but also other aspects of completing a data science project. When working with this versatile tool, you can use Tableau to prepare, analyze, and visualize data. While Tableau is compatible with multiple programming languages, the platform is also useful for making drag and drop selections from the program menu. Through these preset functions, the platform makes a useful addition to any data science toolkit.
5. Jupyter Notebook
For beginner data science students and professionals that are interested in collaborating on projects, or that want to begin practicing how to create programs and code, notebooks are a useful data science tool. Jupyter Notebook in particular can be used to create and share code with others, and allow you to complete projects in multiple programming languages. Commonly used within education and teaching data science, Jupyter Notebook is a staple within online and offline classroom settings, so if you are interested in furthering your knowledge of data science through classes, Jupyter Notebook is an excellent tool to have.
Interested in learning more data science tools?
Now that you know some of the best data science tools for beginners, you might also want to know more about how to use these tools and what other tools are available as your progress in your studies and career. With multiple workshops and bootcamps, Noble Desktop offers all of the options that you need to learn how to use the latest data science software and tools. This includes Microsoft Excel courses, SQL courses, Tableau classes, as well as multiple Python classes and bootcamps.
Offering data science classes for students at all experience levels, many of the Noble Desktop certificate programs and bootcamps are based on curriculums crafted for beginner students and offer opportunities to write code with Jupyter Notebook. So, whether you are interested in learning through live-online instruction in a virtual learning environment, or through in-person instruction at a training center near you, there are so many ways for beginner students and professionals to learn data science tools!