As the total amount of data created, captured, copied, and consumed globally increases rapidly, it is estimated that by 2025, global data creation will grow to more than 180 zettabytes. This data hailed as the “oil of the 21st century”, can be of value only when harnessed. But how does one use the data for business worth and real-world applications?
Data Scientists and IT professionals have been solving this problem for the past few years, using data mining information to predict customer behavior, find new revenue opportunities, and bring product innovation. They leverage Big Data for insights and data-driven decisions. With the increase in data generation, companies are keen to harness it for higher customer satisfaction and a competitive edge. Data Science is used in myriad everyday applications, from search engine image queries to social media analysis and Amazon Prime movie recommendations. It uses various techniques to harness data, develop hypotheses, and make predictions.
Companies and organizations want to recruit professionals with high analytical skills and knowledge of tools. They want skilled professionals who can gather, store and process the data for analysis and reporting. Businesses require Data Scientists for data crunching and discovering patterns and relationships, which requires dedicated formal learning.
Data Science Programs are the go-to platforms for comprehensive learning in Data Science. These programs also include advanced machine learning techniques for imparting skills for sophisticated data models.
What is Data Science
Data Science is the technique of applying advanced analytical and scientific methods to extract valuable information. It involves cleaning and processing raw data for extracting actionable insights. These insights are used for data-driven decisions and data modeling.
The process uses tools and techniques, Statistics principles, and Mathematics. Software programs and dedicated platforms are leveraged for data mining, data cleaning, crunching, and storage. It is thus considered an interdisciplinary practice, and data practitioners can choose the area of work based on their learning, passion, and toolkit arsenal. For instance, Data Scientists can be Data Analysts, Data Architects, Machine Learning Engineers, Actuarial Scientists, or Data Science Generalists!
The discipline of Data Science helps businesses understand and measure customer behavior, model products and services for the right audience, and reap profits, brand success, and higher ROMI (Returns on Marketing Investment) for shareholder confidence. Data Science also facilitates a deep understanding of manufacturing and operational processes to make decisions and tweak systems. Ultimately it enables cutting costs and streamlines business processes for better outcomes.
How does Data Science work?
Data Science sifts through raw information and moves through various stages for deep insights. It relies heavily on Machine Learning and Deep Learning to create predictive models using algorithms and tools.
Data fuels businesses and companies to use it for a competitive edge and to remain agile. As data professionals, you must understand the business domain and the value of data.
Companies go through a process of ‘data fluency’ on their path to leveraging Data Science. The process generally has a seven-stage lifecycle, as given below:
1. Data Capture
This stage includes the following steps
- Asking the right questions
- Data acquisition
- Data Collection and
- Data Extraction.
1A. Asking the right questions
To begin with, you must understand the business objectives. What is the goal? What do you want to do with the data? What business challenges do you want to solve? Gain a 360-degree perspective of the business problem and generate a set of questions to decide how your data can solve the problem.
1B.Data Acquisition
Once you have your answers, it is time to identify what data you need to fulfill the business objectives. Determine how to procure the data. How do you generate the data? Do you need to source it externally? What are the protocols? Assess your data collection methods for high-quality results.
1C. Data Collection
Once you have defined what data you need, your data team identifies the information to be captured and the best practices for doing so. Determine what data is unnecessary and how to collect the data, such as forms, surveys, interviews, direct observation of customer behavior, etc. Devise a plan to capture the data that you consider critical to your project. Adopt a scientific approach to data collection, capturing data for processing and storing.
1D. Aggregation
Your data must be converted into a usable format from the unstructured and semi-structured raw data. This phase is called Data Wrangling, where the data set is cleaned and transformed into a more accessible and usable format. Organizations clean the data (Data Cleaning), fix inconsistencies and missing values (Data Remediation), process it along the principles of collinearity, and scrape the necessary data for the project.
Data engineers collect all the data and format it to be cohesive.
2. Data Processing
Once data is collected, it must be processed for storage and analysis.
The data goes through the stages of
- Data compression, and
- Data encryption.
3. Data Storage
It is the phase where a Data Architect begins moving the data into a database infrastructure. Here data is staged, processed, and warehoused for future use. Databases or datasets are created and stored in the cloud, on servers, or on other physical storage like a hard drive. It also involves ensuring Data Security.
4. Data Management
Data Management, also called Database Management, involves organizing and retrieving data as necessary during the project lifecycle. This stage is an ongoing process of data crunching, where data is clustered, classified, summarized, and retrieved for use. Access logs and changelogs are implemented to track data access history and changes made.
5. Data Analysis
In this step, you slice and dice your data to extract insights. Various Statistical techniques, algorithms, and methods of analysis are used to discover hidden patterns and relationships in the data. The same is used to form hypotheses and make predictions.
6. Data Visualization
It refers to the process of presenting your conclusions and information as graphical representations. You communicate the findings using interactive visualization tools and dashboards to the management and other project stakeholders. You create visualizations with appropriate charts and graphs that communicate your insights into a compelling narrative.
The phase is about telling your data story. Although not mandated in all organizations, it is usually a required step in larger enterprises.
7. Modeling
The phase typically involves Feature Selection and Predictive Modelling.
In Predictive Modeling, you combine the attributes or features to make predictions of the likelihood of an outcome.
7A. Feature Engineering
Feature engineering processes the refined raw data and identifies the best attributes for modeling. This phase involves selecting important features and constructing meaningful types with your data.
7B. Predictive Modelling
It uses Statistical and Mathematical techniques to predict future events or outcomes based on the insights gained. Herein, you train Machine Learning models, evaluate their performance, and make predictions.
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Conclusion
The seven phases of the Data Science lifecycle work as a road map to empower you with more effective communication, analysis, and predictive modeling.