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What is Data Science?

Data science is a multidisciplinary approach to discovering, extracting, and presenting patterns in data by fusing analytical methods, domain expertise, and techniques.

Data science includes areas such as data mining, forecasting, machine learning, predictive analytics, statistics, and text analytics. With data growing at an alarming rate, companies are racing to capitalize on the insights in their data. Yet most organizations face a lack of experts to analyze their big data to find insights and explore problems that companies have yet to identify. To realize and monetize the value of data science, organizations must inject predictive insights, forecasting, and optimization strategies into business and operational systems. Many businesses are providing platforms to their knowledge workers to help them carry out their machine-learning projects and tasks. Being able to extract trends and opportunities from the vast amounts of data-infused into the business will give organizations a competitive advantage.

Data science includes descriptive, diagnostic, predictive, and prescriptive capabilities. This means that with data science, institutions can use data to figure out what happened, why it happened, what happened, and what they should do with expected outcomes.

Data analysis is the process of studying and summarizing data in detail. It is roughly the same as data science, except for one important detail. Data analysis will first establish data analysis models and goals, collect large amounts of data for analysis, and finally extract useful information and form conclusions that are helpful to the business.

What is a Data Scientist?

Data science is academic knowledge that spans many fields, including statistics, data mining, scientific method, artificial intelligence (AI) and data analysis, etc. The purpose is to extract value from data. Preparing data for analysis is an important part of data science. The process includes cleaning, summarizing, and processing data. But in larger enterprises, there are so-called data engineers to complete this task. In the end, data science is all about examining the results of analytics to help businesses, academia, or any data-related project gain informed insights.

A data scientist is a professional who knows all these things well. Their work involves applying a range of skills to analyze a wide variety of data: from the Internet to smartphones, sensors, and other sources, and derive actionable insights from it. A data scientist is all about identifying problems, asking them, designing methods, and analyzing the available data. A complete data team consists of data scientists, data analysts, and data engineers. Data analysts perform statistical work such as experimental design, data analysis, etc. Data engineers work on program implementation, such as the collection and organization of big data, and the deployment of algorithms.

• Python Coding/R Programming:
  Although programming is not the main work of a data scientist, it will take relatively time to perform statistical operations without using tools. In addition, the amount of data is usually very large. Without using Python or R to assist in sorting, there is no way to use it.
• Database/Coding:
  When it comes to data collection, it must be cleaned up and managed. Data scientists must have very high skills in this area. The main reason is the same as the quantity and quality of data. If you want to use the data well, you must Be sure to organize and manage the data first.
• Machine Learning and AI:
  Data scientists must have a certain grasp of machine learning and artificial intelligence because their data are inseparable. Machine learning, or artificial intelligence, cannot be done without data.
• Data Analysis:
  It was mentioned earlier that data scientists need to use SQL database management systems, mainly to store and effectively process data, and then use the stored data for analysis. Data scientists also need to be very familiar with data analysis and be able to find value from data and make use of it.
• Data Visualization:
  In addition to knowing how to analyze data, data scientists must also be able to visualize it for interpretation. The stakeholders that data scientists need to face include bosses, managers, C-levels, senior managers, and other important roles in the company, but these people are not necessarily so proficient in the observation of data, so the visualization of data has become an important part of data science.
• Mathematics and Statistics:
  Mathematics and statistics are essential skills for data scientists. In the work of data scientists, they face complex mathematical and statistical problems every day. Calculating risks, calculating possibilities, forecasting, etc., all need to be equivalent to mathematics and statistics. academic understanding.
• Communication Skills and Teamwork:
  The ability to communicate and communicate is indispensable for teamwork, especially for data scientists. In a data or data team, in addition to scientists, there are data analysts, data engineers, and interested teams, so excellent communication skills and department exchanges will be particularly important.
• Marketing Theory and Practical Experience:
  Data scientists also need to have marketing knowledge and practical experience, because their analysis and calculations often have a direct impact on the company's operations and marketing, and understanding marketing is of great help in judging the value of data.

The Difference Between Data Science and Data Analysis:

The way to distinguish data science and data analysis is to cover the scope of the problem. Data science covers a broader problem than data analysis. Data analysis will first establish the data analysis model and goal and then summarize. On the other hand, data science is deeper and asks more questions to uncover new knowledge and goals.

Data analysts perform content analysis on data from one dataset, while data scientists combine datasets from different sources to generate new insights.

What is Data Science Used for?

• Descriptive analysis:
  The descriptive analysis examines data to gain insight into what happened or is happening in the data environment of the data. It features data visualization, such as pie charts, bar charts, line charts, tables, or generated narratives.
• Diagnostic analysis:
  The diagnostic analysis is the examination of in-depth or detailed data to understand why something happened. It is characterized by techniques such as in-depth research, data exploration, data mining, and correlation. Multiple data manipulations and transformations can be performed on a given data set to discover unique patterns within each technology.
• Predictive analytics:
  Predictive analytics uses historical data to make accurate predictions about possible future data patterns. It features techniques such as machine learning, prediction, pattern matching, and predictive modeling. In each technique, computers are trained to reverse engineer causal associations in the data.
• Normative analysis:
  Prescriptive analytics take predictive data to the next level. Not only does it predict what might happen, but it also suggests the best way to respond to that outcome. It can analyze the potential impact of different options and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning.

91ÊÓƵ¹ÙÍø Data Science Works?

• Understand the business problem: The process of data science starts with understanding the problem that business users are trying to solve.
• Collect and integrate raw data: Once the business problem is understood, the next step is to collect and integrate raw data. First, analysts must look at the available data. Often, the data will be in many different formats and many different systems, so data wrangling and data preparation techniques are often used to transform the raw data into a usable format suitable for the specific analytical technique that will be used. If data is not available, data scientists, data engineers, and IT often collaborate to bring new data into a sandbox environment for testing.
• Explore, transform, cleanse, and prepare data: Most data science practitioners will use data visualization tools to organize data into graphs and visualizations that help them see general patterns, high-level correlations, and any potential outliers in the data. Analysts will transform, create variables, and prepare data for modeling.
• Create and select a model from the data: Most analysts use algorithms to create models from input data and use techniques such as machine learning, deep learning, prediction, or natural language processing to test different models.
• Test, tune, and deploy the model: Statistical models and algorithms are applied to datasets to attempt to generalize the behavior of target variables based on input predictors (factors that affect the target). The outputs are typically predictions, exceptions, and optimizations, which can be displayed in dashboards or embedded reports, or injected directly into business systems to make decisions near the point of impact. Then after the models are deployed into visualizations or business systems, they are used to score new input data that has never been seen before.
• Monitor, test, refresh, and manage the model: Once the model is deployed, it must be monitored so that it can be updated and retrained as data changes due to changes in the behavior of real-world events. Organizations must therefore develop a model operations strategy to govern and manage changes to the production model. In addition to deploying models to dashboards and production systems, data scientists can create complex data science pipelines that can be invoked from visualization or dashboard tools. Often these have a reduced and simplified set of parameters and factors that can be tuned by citizen data scientists. This helps address the skills shortages described above. Therefore, citizen data scientists are usually business or domain experts who can select parameters of interest and run complex data science workflows without understanding the complexities behind them. This allows them to test different scenarios without involving data scientists.

What are the Benefits of Data Science for Businesses?

Data science is revolutionizing the way companies do business. Many businesses require a strong data science strategy to drive growth and maintain a competitive advantage.

• Explore unknown transformation models:
  Organizations can use data science to discover new patterns and relationships that have the potential to transform organizations. It exposes low-cost changes in resource management that have the greatest impact on profit margins.
• Innovation of new products and solutions:
  Data science uncovers gaps and problems that might otherwise go unnoticed. A deeper understanding of purchasing decisions, customer feedback, and business processes can drive innovation in internal operations and external solutions.
• Instant optimization:
  Responding to changing conditions in real-time can be extremely challenging for businesses. This may result in significant loss or interruption of business activities. Data science can help companies predict changes and respond optimally to different situations.
• Data Science Trends and Industries:
  Innovations in artificial intelligence and machine learning have made data processing faster and more efficient. Driven by industry demand, an ecosystem of disciplines, degrees, and job titles has formed within the data science field. Industry demands for cross-functional skills and expertise have seen data science exhibit strong expected growth in the coming decades.
• Big Data Analytics:
  The development of data analysis benefits from the development of big data. The concept of big data is based on the concepts of volume, velocity, and veracity. In the era of big data, the three most important requirements are large, fast, and real data. While storing large amounts of data has historically been no problem, the methods for analyzing and processing this data are relatively limited.
• Data Science Automation:
  According to the estimates of industry professionals, data science automation will have more breakthrough development in a very short period. The world is now in a phase of automation of data science models. With data science, model automation will be used more and more widely.
• Cloud and Data Science:
  Cloud computing not only greatly reduces the cost of the device, software, and platform. At the same time, it greatly reduces the application and maintenance costs in data analysis. Cloud-based data science and machine learning platforms provide an ideal environment for data scientists to process and analyze data from the cloud.
• Natural Language Processing:
  Deep learning has become one of the most popular fields in data science. Deep learning has attracted much attention because of its ability to learn complex nonlinear relationships.
• Internet of Things:
  Data science is the core of the Internet of Things. It can obtain information from connected objects, and achieve data transmission through the connection between machines and the Internet. Up to now, automatic driving adopts data science to a certain extent technology.