What is Data Science

Data science is an important part of many industries today, with huge amounts of data being generated and is one of the most debated topics in IT circles. Its popularity grew over the years and companies started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we will learn what data science is.

Data science, in simple words, is the field of study that involves collecting, analyzing, and interpreting large sets of data to uncover insights, patterns, and trends that can be used to make informed decisions and solve real-world problems.

What Is Data Science?

Data science is the field of study that processes huge volumes of data using modern tools and techniques to find unseen patterns, derive valuable insights meaning and make business decisions.. Data science uses complex machine learning algorithms to create 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 the data science lifestyle.

The Data Science Lifecycle

Now that you know what is data science, next up let us focus on the data science lifecycle.. Data science’s lifecycle consists of five distinct stages, each with its own tasks:

• Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
• 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.
• Process: Data mining, clustering/classification, data modeling, data aggregation. Data scientists take prepared data and examine its patterns, ranges, and deviations to determine its usefulness in predictive analytics.
• Analytics: Discovery/Confirmation, Predictive Analytics, Regression, Text Mining, Qualitative Analysis. This is the real core of the life cycle. This step involves performing various analyzes on the data.
• Communication: Data reporting, data visualization, business intelligence, decision making. In this final phase, analysts prepare analyzes in easy-to-read forms such as tables, graphs, and reports.

Data Science Prerequisites:

Here are some technical concepts you need to know before you start learning what data science is.
1. Machine Learning: Machine learning is the backbone of data science. Data scientists must have strong knowledge of ML in addition to basic knowledge of statistics.
2. Modeling: Mathematical modeling allows you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves determining which algorithms are best suited to solve a given problem and how to train these models.
3.. Statistics: Statistics is the heart of data science. Mastering statistics can help you extract more information and achieve more meaningful results.
4.. Programming: A certain level of programming proficiency is required to successfully execute a data science project. The most popular programming languages ​​are Python and R. Python is especially popular because it is easy to learn and supports a number of libraries for data science and ML.
5.. Databases: A competent data scientist must understand how databases work, how to manage them, and how to extract data from them.

What is a Data Scientist?

If learning what data science is sounds interesting to you, then understanding its role will be much more interesting to you.. Data scientists are some of the newest data analytics experts with the technical ability to handle complex problems as well as the desire to investigate questions that need to be answered.. They are a combination of mathematicians, computer scientists and trend forecasters.. They are also in high demand and well paid as they work in both business and IT sectors.. On a daily basis, a data scientist may perform the following tasks:

• Explore patterns and trends in data sets to gain insights.
• Create predictive algorithms and data models
• Improve data quality or product delivery using machine learning techniques
• Distribute recommendations to other teams and senior management
• In data analytics, use data tools such as R, SAS, Python or SQL
• Leading innovations in data science
  

What Does Data Scientist Do?

You know what data science is and you’re probably wondering what exactly the job is like: here’s the answer.. A data scientist analyzes business data to derive meaningful insights.. In other words, data scientists solve business problems through a series of steps, including:

• Before embarking on data collection and analysis, data scientists determine problem by asking the right questions and gaining understanding.
• The data scientist will then determine the correct set of variables and data sets..
• Data Scientists bring together structured and unstructured data from a variety of 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 data to ensure uniformity, completeness and accuracy.
• Once the data has been presented in a usable form, it is fed into an analytics system: an ML algorithm or statistical model.. This is where data scientists analyze and identify patterns and trends..
• Once the data is completely returned, the data scientist will interpret the data to find opportunities and solutions.
• Data scientist’s complete tasks by preparing results and information to share with appropriate stakeholders and communicating results.

Uses of Data Science

• Data science can detect patterns in seemingly unstructured or unconnected data, allowing for conclusions and predictions to be made.
• Technology companies that collect user data can use strategies to turn that data into valuable or profitable information.
• Data science has also entered the transportation sector, such as driverless cars. It is simple to reduce the number of accidents through the use of driverless cars. For example, with driverless cars, training data is fed to the algorithm and the data is tested using data science methods, such as highway speed limits, very crowded streets casting, etc.
• Data Science Applications provide higher levels of therapy personalization through genetics and genomics research.

Data Science Tools:

Becoming a data scientist is a challenging job, but fortunately, there are many tools available to help data scientists succeed in their work. And now that we know what data science is, its lifecycle, and more about its role in general, let’s look at its tools.

• 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:

• Healthcare
• Gaming
• Image Recognition
• Recommendation Systems
•  Logistics
• Fraud Detection
• Internet Search
• Speech recognition
• Targeted Advertising
• Airline Route Planning
• Augmented Reality