Data Science

Introduction :

Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist.

What is Data Science :

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and presented in various formats. Now that you know what data science is, let’s see why data science is essential to today’s IT landscape.

Data science’s lifecycle consists of five distinct stages, each with its own tasks:

1. Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
2. Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
3. Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
4. Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.
5. Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports

What Does a Data Scientist Do?

A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:

• Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.
• The data scientist then determines the correct set of variables and data sets.
• The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc.
• Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.
• After the data has been rendered into a usable form, it’s fed into the analytic system—ML algorithm or a statistical model. This is where the data scientists analyze and identify patterns and trends.
• When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.
• The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.

Now we should be aware of some machine learning algorithms which are beneficial in understanding data science clearly.

Data Science Tools :

The data science profession is challenging, but fortunately, there are plenty of tools available to help the data scientist succeed at their job.

• Data Analysis: SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner
• Data Warehousing: Informatica / Talend, AWS Redshift
• Data Visualization: Jupyter, Tableau, Cognos, RAW
• Machine Learning: Spark MLib, Mahout, Azure ML studio

Applications of Data Science

Data science has found its applications in almost every industry.

1. Healthcare

Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.

2. Gaming

Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.

3. Image Recognition

Identifying patterns in images and detecting objects in an image is one of the most popular data science applications.

4. Recommendation Systems

Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.

5. Logistics

Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.

6. Fraud Detection

Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.  

Data Science Use Cases

Here are some brief overviews of a couple of use cases, showing data science’s versatility.

• Law Enforcement : In this scenario, data science is used to help police in Belgium to better understand where and when to deploy personnel to prevent crime. With only limited resources and a large area to cover data science used dashboards and reports to increase the officers’ situational awareness, allowing a police force that’s spread thin to maintain order and anticipate criminal activity.
• Pandemic Fighting : The state of Rhode Island wanted to reopen schools, but was naturally cautious, considering the ongoing COVID-19 pandemic. The state used data science to expedite case investigations and contact tracing, enabling a small staff to handle an overwhelming number of concerned calls from citizens. This information helped the state set up a call center and coordinate preventative measures.
• Driverless Vehicles : Lune wave, a sensor manufacturing company, was looking for a way to make sensor technology more cost-effective and accurate. They turned to data science and machine learning to train their sensors to be safer and more reliable, as well as using data to improve their 3D-printed sensor manufacturing process.