What is Data Science and Analytics?

Data science teams are credit with a range of benefits including more effective fraud detection, lower financial risks on credit cards and loans, better supply chain management, stronger cybersecurity protections and faster business decision-making. They also create AI tools and technologies for deployment in specific applications.

Learn how to become a Data Scientist with Simmons’ online Bachelor of Science in Data Science and Analytics.

Data Collection

Data science and analytics are a set of disciplines that focus on making data-driven decisions in business. They are gaining momentum because of their ability to deliver value to enterprises, such as increased ROI and sales growth.

Data scientists analyze and interpret large, complex data repositories and provide recommendations to business stakeholders. They use their skills to solve problems in a wide range of industries, including medicine and healthcare, finance, retail, transportation and manufacturing.

Data collection is a crucial first step in the data science process. Developing a clear understanding of your program’s goals and data needs will help you select the best method to collect the information needed for analysis. Data can collect from first-party sources (data gathered directly by your company), second-party sources (data gathered and shared with another trusted partner) or third-party sources (data purchased from a service provider). The quality of the data you receive depends on how close it is to its origin.

Data Cleaning

Data cleaning is the process of correcting errors and inconsistencies that make it difficult to use your data. It may include standardiz dates, ensuring addresses are formatt consistently, reconciling duplicate data records or removing information that is irrelevant to your analytics applications.

This is an ongoing activity that can done manualor automatic through software tools. It should be perform regularly to prevent errors from accumulating in your system and negatively impacting analyses.

For example, a simple typo in data entry can create an invalid customer record that makes it impossible to communicate with or identify the person. A recurring error can also add costs to your operations, for example when you need to replace incorrect information. Data cleansing helps to minimize these types of errors and reduce the time it takes for analysts to analyze your data. Often, it includes the application of business logic to detect anomalies and remove or correct them.

Data Analysis

Data analysis is a crucial step in the process of gaining insights from raw data and making smart calculated decisions. In today’s information-driven world, being able to analyze and interpret data is essential for companies looking to prevent danger, provide security or make business operations more efficient.

This process involves identifying and interpreting patterns in large datasets to identify what’s important. It also includes removing irrelevant data, validating data and identifying outliers. It’s critical to do this because bad or incorrect data can have a major impact on the results of an analysis and decision-making.

Data scientists rely on mathematical, statistical and machine learning techniques to clean, process and interpret data sets in order to extract meaningful outcomes. They design advanced data modeling processes, predictive analytics, and ML algorithms to identify hidden trends and create solutions for business. They also work with various databases and are well-versed in programming languages (Python, R, SQL) and data visualization tools.

Data Visualization

Data visualization is the use of charts, graphs and maps to represent information and data. It helps business stakeholders, who might not have the technical knowledge of a data scientist, understand trends, patterns and outliers within a dataset.

It can also help communicate results to wider business teams. An infographic, for example, can efficiently convey results and insights to marketing teams. It can even be used to explain complex algorithms used in machine learning, such as neural networks or decision trees.

The types of charts, graphs and maps that are created depend on the data type and the analysis goal. For example, a line chart can be used to show trend changes over time and a scatter plot can display the relationship between two variables. Other popular visual tools include histograms (used for statistical analysis of numerical features) and a heat map (which uses color to illustrate relative intensity).