Data science is an interdisciplinary field that focuses on collecting knowledge from huge data sets and applying that knowledge and insights to address issues in a variety of application sectors. Preparing data for analysis, framing data science challenges, analyzing data, generating data-driven solutions, and presenting findings to make high-level decisions in a wide range of application domains are all part of the field. As such, it combines computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication, and business skills.
Data is omnipresent, and it is one of the most crucial aspects of any organization, allowing it to thrive by making decisions based on facts, statistical statistics, and trends. Data science, or data analysis, is vital because it allows us to discover meaningful information from data, answer questions, and even predict the future or the unknown. It extracts knowledge and insight from massive amounts of data using scientific methodologies, procedures, algorithms, and the framework.
Data science combines ideas, data analysis, Machine Learning, and related methodologies to analyze and dissect real-world occurrences using data.
It is a vast field that employs many methods and concepts from different disciplines such as information science, statistics, mathematics, and computer science. Machine learning, visualization, pattern recognition, probability models, data engineering, signal processing, and other techniques are used in Data Science.
The data science cycle/pipeline involves overlapping and continuing processes:
- Setting the research goal:Understanding the business or activity that our data science project is part of is key to ensuring its success and the first phase of any sound data analytics project. Defining the what, the why, and the how of our project in a project charter is the foremost task. Now sit down to define a timeline and concrete key performance indicators and this is the essential first step to kick-start our data initiative!
- Retrieving data:This is the gathering of raw structured and unstructured data from all relevant sources via just about any method—from manual entry and web scraping to capturing data from systems and devices in real time.
- Data preparation:The next data science step is the dreaded data preparation process that typically takes up to 80% of the time dedicated to our data project. Checking and remediating data errors, enriching the data with data from other data sources, and transforming it into a suitable format for your models.
- Prepare and maintain: This involves putting the raw data into a consistent format for analytics or machine learning or deep learning models. This can include everything from cleansing, deduplicating, and reformatting the data, to using ETL(extract, transform, load) or other data integration technologies to combine the data into a data warehouse, data lake, or other unified store for analysis.
- Preprocess or process:Here, data scientists examine biases, patterns, ranges, and distributions of values within the data to determine the data’s suitability for use with predictive analytics, machine learning, and/or deep learning algorithms (or other analytical methods).
- Analyze: This is where the discovery happens—where data scientists perform statistical analysis, predictive analytics, regression, machine learning and deep learning algorithms, and more to extract insights from the prepared data.
- Communicate: Finally, the insights are presented as reports, charts, and other data visualizations that make the insights—and their impact on the business—easier for decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternatively, data scientists can use dedicated visualization tools.
Some of the tools used in data science include SAS, Apache Spark, BigML, R , Python, Matplotlib etc.