People of the world today have access to various types of technological resources and media. Examples include the internet, the web, and mobile devices. As the internet and the web emerged from being a digital display of information to becoming a medium for shared interactions of numerous users and for the expansion of online companies, the amount of data increased. Similarly, with the technological advancement of mobile devices, “it is possible to track movement, analyze physical behavior and even health-related data (number of steps you take per day),” These advances in the world of technology have resulted in continuous growth in the production of data available, opening a new world of possibilities in extracting useful information and knowledge using data.
Due to this new rise of data, difficulties arise in handling and using the large volumes of data available today. Introducing the technical field of data science supports solving these new masses of data problems. From these solutions, data science helps organizations use the data to derive useful information and insights.
What is Data Science?
Data science is the study and analysis of data. It involves using different tools and techniques to interpret and derive meaningful information, knowledge, and patterns within a large amount of data to build an understanding to make efficient decisions. The need for data science came about due to the massive production of data that forms each second of the day. Difficulties arise in handling the vast volumes of data that increase each day. Data science offers powerful, complex algorithms and technologies to solve the challenge of handling and evaluating this data. It involves the combination of mathematical, computational, and statistical disciplines.
In the modern world, data science has heavily grown involvement in businesses and organizations, solving company problems using knowledge from data. Data insights allow businesses to make effective decisions that will lead to the desired outcome for a business. An example of data-driven decision-making used in industries is customer prediction decisions in product marketing. By analyzing a system of data that includes information on customer behavior patterns, marketers can use this to predict customer desires, needs, future behaviors, and the overall likelihood of buying a product.
Data science is significant in the growth of businesses by modifying new strategies and operations for an increase in customer satisfaction with the use of the decisions and insights offered by data analysis.
Data Science Life Cycle
The process of data science in business project scenarios consists of a cycle of several steps. This cycle may vary for different companies but follows a similar process overall. Each step in the process is divided into different roles and responsibilities. The parts involved in the building and development of the project are business analyst, data analyst, data scientist, data engineer, data architect, and machine learning engineer. The following are the steps involved in the cycle of data projects:
- 1. Developing a business understanding of the problem the client is facing and what is needed to be achieved. This is the role of the business analyst. They are responsible for asking the required questions to gather details from the client, understand what is happening in the client’s business, and define the problem.
- 2. After recognizing the problem, the data collection process is done by the data analyst team. This step involves finding and reviewing all the various data sources (internal and external) available that address the problem that is being solved. Sources may include web server logs, social media posts, US Census datasets, etc. It is overall the process of gathering the data. To do this, data analysts need to have a proper way to source the data. The two tracks they source the data are web scraping and extracting data from third-party APIs.
- 3. Next is data preparation. This step is one of the most critical steps of the cycle that takes the most time compared to the others. It is the process referred to as data cleaning. It is formatting the data in sufficient structure and removing unnecessary functions. Data must be well organized and easy to understand to prepare for analysis. When data is found, it may not be in the best and most accurate format. It may need values, duplicate data, or incorrect data. With data cleaning, steps are made to identify and fix incorrect data.
- 4. Data modeling. It is the process of taking the prepared data and selecting a proper machine learning algorithm where the data set is formed into a model and visual representation. With the use of these tools, this is where the analysis of the data comes to play. The step gives the tools to understand the data better, bringing in insights, predictions, and outcomes from the data.
Programming plays a vital role in each data analysis project step. It plays a role in the stages of data collecting, data preparation, and data modeling. It is only easy to perform these tasks with the involvement and supporting tools of programming. In data collection, web scraping, the extraction of data from the web, is a process that lines of code can do. This is also the case for data cleaning and machine learning tasks. With various programming tools, the several functions in the life cycle of data science can and support be completed more efficiently. Over the past several years, Python has been the most used programming language for data analysis.
What is Python?
Python is an object-oriented, open-sourced, high-level programming language used for general-purpose programming. Python is mainly known for its simplicity; it has a cleaner and more readable syntax than other programming languages. Despite the simplicity of this programming language, Python is also powerful enough to handle complex applications. Because it is an object-oriented language, Python organizes code into objects that can interact and communicate with each other. Python can operate and be used in many ways. Web development, data analysis, artificial intelligence, and many more. Python is known for its increased productivity because there is no compilation step, and the edit-test-debug cycle is fast. Debugging Python programs is easy; a wrong input will raise an exception.
The factors considered before deciding which language is the best for data analysis are speed, availability of packages, and design goal. By using Python, one has access to a significant number of packages. Python’s simplicity and easy-to-understand syntax rules help build applications with a concise, readable codebase. A few lines of code can achieve many tasks to achieve design goals. Considering these factors using Python can lead to faster data project completion.
Python is a popular language used often in data science for this ease of understanding and accessibility to various libraries that include functions of prewritten chunks of code that programmers can reutilize and optimize a task. The extensive range of libraries and tools makes it easy and efficient for data scientists to work with large datasets, manipulate data, and visualize results.
Python Libraries for Data Analysis
Data scientists use Python’s NumPy and Pandas libraries, commonly used for data manipulation tasks. They use Python’s Matplatlib and Seaborn libraries for data visualization. These are the few libraries that data scientists use provided by Python that make their work easier.
Data scientists use Python’s NumPy because it can support multi-dimensional arrays. The arrays in the NumPy library are enhanced for numerical operations, making them operate much faster and more efficiently when working with larger data sets. The library also is popular for machine learning applications, as it makes machine learning algorithms much more efficient and straightforward. NumPy is also designed to be compatible with other Python libraries.
Data scientists also use a Python library named Panda. Panda is used because of its robust data manipulation, analysis, and modeling tools. This library makes it easy to clean and preprocess data and transform data into different formats. It also has tools that help data scientists explore data and spot patterns. These tools help with exploring data, visualization, and filtering. Panda also has a flexible data manipulation API. Which makes it easier to slice, index, and filter data. Having this flexible API, manipulating data sets, and performing advanced operations is easy.
Another library scientists use is Matplotlib, commonly used for data visualization. This library has a set of tools that can create high-quality visualizations of data. They use this library to create visualizations to help spot patterns and trends when exploring data. Not only does this library analyze data, but they also use it to make a visualization to present data they are trying to share with others. These visualizations include line charts and bar charts.
Seaborn is another Python library that data scientists use for visualization. The Seaborn library has a high-level interface for creating informative visualizations. This library has a more charming visualization style, making the charts and graphs quickly look good without customizing them. Seaborn has a variety of visualizations that can be used for data exploration. Like Matplotlib, Seaborn uses line charts and bar charts for visualization. Seaborn tools help with regression analysis and hypothesis testing. The library also uses the Jupyter Notebook environment to allow data scientists to explore data in real time and create interactive dashboards for presenting data.
As our world is continuously filled with numerous databases, data science’s primary goal is to help handle this available data, analyze it, and visualize it to solve problems and make insightful decisions and predictions. For this to happen, data scientists need efficient tools to deal with these large datasets. Python is a crucial tool that supports data science to accomplish its goal. Because of the vast number of available libraries and packages, data science programmers can access many tools in their toolboxes. When working on projects, Python helps make the job easier and become more effective. This allows data scientists to save time to focus on the main problem. With the collaboration of the computing power of Python and data analysts, we can form solutions to several data-related issues and improve decision-making.