Data science is a rapidly growing field. It is fueled by several factors which include: big data, increased computing power, artificial intelligence and machine learning. While no specific term can conclusively define data science, we can all agree that it is an umbrella term for data science that describes the application of scientific methods to generate insights from data.
Python is a popular tool for data science considering it is a high-level programming language with comparatively easy syntax. It has a wealthy ecosystem of libraries and tools that help carry out data science tasks.
Some of the tools include NumPy for mathematical operations on arrays, Pandas for reading data in different formats, Matplotlib for data visualization and Seaborn for creating statistical graphics and Scikit-learn for machine learning.
With python, you get various frameworks that enhance your experience of working in data science. They include Jupyter Notebook, a web-based environment, Spyder and PyCharm.
The techniques involved in data science include:
Data cleaning and pre-processing, data visualization, Machine Learning and Deep Learning.
Data science typically involves several phases used to transform raw data into useful information. Below are some of the popular ones in a data science project:
Data Collection: Involves collecting data from various sources, such as databases, APIs, or web scraping.
Data Cleaning and Preprocessing: Raw data is often incomplete, inconsistent, or contains errors or outliers. Through data cleaning and preprocessing stage these issues are identified and corrected. .
Data Analysis: The data analysis stage involves using statistical methods and algorithms to identify patterns and relationships in the data.
Data Visualization: This presents insights and patterns in the data visually.
Model Building and Evaluation: In this stage, a predictive model is built using machine learning algorithms or statistical models. The model is then evaluated to determine its accuracy and effectiveness in making predictions or classifications.
Deployment and Integration: This may involve creating an API or deploying the model on a cloud platform.
Monitoring and Maintenance.
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