Introduction to Data Science: Lifecycle, Applications, Requirements and Roles

Data science is a vital part of any business today, given the colossal amounts of data generated, and is one of the most discussed subjects in IT sectors. Its fame has grown over the years, and organizations have begun introducing data science practices to expand their enterprises and increase customer satisfaction.

Data science is a field of study that deals with massive data leveraging modern techniques and tools to determine hidden patterns, derive meaningful insights, and make business decisions.

This domain uses complex ML algorithms to create predictive models. The data leveraged for analysis can come from various sources and are presented in several formats.

The Lifecycle of Data Science

Data science's lifecycle includes five phases, each with its own activities:

1. CAPTURE: This phase involves collecting raw structured and unstructured data, and its activities are data acquisition, data entry, signal reception, and data extraction.
2. MAINTAIN: This phase covers raw gathering data and placing it in a form that can be leveraged. Its activities include data warehousing, staging and cleaning, architecture, and processing.
3. PROCESS: Here, data scientists take the prepared data and probe its ranges, patterns, and biases to find how beneficial it'll be in predictive analysis. Its activities include data mining, data summarization, classification/clustering, and data modeling.
4. ANALYZE: This phase involves executing several analyses of the data. The activities include predictive analysis, qualitative analysis, confirmatory/exploratory, text mining, and regression.
5. COMMUNICATE: This is the final lifecycle stage, where data scientists prepare the analyzes in easily readable formats like reports, charts, and graphs. In this phase, the activities include data reporting, Business Intelligence (BI), data visualization, and decision-making.

Requirements for Data Science

Here are a few technical concepts you should know before starting to learn what data science is.

• Machine Learning (ML): ML is the backbone of data science; hence, data scientists must have a strong understanding of the topic.
• Modeling: Mathematical models allow us to make quick calculations and predictions based on what we know about the data. Modeling is also a part of ML and involves finding which algorithm is most suitable for solving the given issue and how to train these models.
• Statistics: It's the core of data science, where a sturdy handle on statistics helps you extract more intelligence and acquire meaningful insights.
• Programming: Some level of programming is needed to perform a successful data science project. The most common languages are Python and R.
• Databases: A potential data scientist requires to learn how databases operate, how to manage them, and how to extract data from them.

Roles of Data Scientists

Data scientists are the most recent analytical data experts who have the technical potential to tackle complex problems and the desire to probe what questions require to be answered.

Some of the daily routine activities or tasks for a data scientists include:

• Identify patterns and trends in datasets to get insights
• Enhance data quality by leveraging ML techniques
• Leverage data tools like SQL, R, SAS, or Python for data analysis
• Create forecasting algorithms and data model

Other than these daily tasks, a data scientist also solves business issues through a series of procedures, including:

• Before handling the data collection and analysis, the data scientist identifies the issue by asking the right questions and meaningful insights.
• They then determine the accurate data and variable sets.
• The scientists then collect unstructured and structured data from several disparate sources such as public data, enterprise data, and more.
• Once the data is collected, they process the raw information and convert it into a suitable format for analysis. 
• Once the data is rendered, it's fed into the analytic system, i.e., ML algorithm or statistical model. This is where the scientists analyze and determine trends and patterns.
• When the data is completely rendered, they interpret the data to identify opportunities and solutions. 
• They complete the task by preparing the outcomes and insights to share with suitable stakeholders and communicating the results.

Application of Data Science

Some of the sectors where data science has become massively popular are:

• Image recognition
• Fraud detection
• Augmented Reality
• Recommendation systems
• Gaming
• Internet search
• Healthcare
• Logistics