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Complete Introduction to Data Science: Lesson 1

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What is Data Science, and how does it work?


Businesses can use data science to detect patterns in massive amounts of structured and unstructured big data. As a result, businesses can improve efficiencies, manage costs, identify new market opportunities, and increase their competitive advantage.

Requesting a recommendation from a personal assistant like Alexa or Siri necessitates the use of data science. Operating a self-driving car, using a search engine that returns relevant results, and interacting with a chatbot for customer service all fall into this category. These are all real-world data science applications.

Definition of Data Science


Data science is the process of analyzing large amounts of unstructured and structured data to find patterns and extract actionable information. Data science is an interdisciplinary field in which statistics, inference, computer science, predictive analytics, machine learning algorithm development, and new technologies to gain insights from big data are all foundations.

Start with the life cycle of data science to define it and improve data science project management. Capture is the first step in the data science pipeline workflow: gathering data, extracting it if necessary, and entering it into the system. Data warehousing, data cleansing, data processing, data staging, and data architecture are all part of the maintenance stage.

Data processing is the next step, and it is one of the fundamentals of data science. Data scientists distinguish themselves from data engineers during data exploration and processing. The processes that create effective data include data mining, data classification and clustering, data modeling, and summarizing insights gleaned from the data.

The next stage is data analysis, which is equally important. Data scientists work here on exploratory and confirmatory work, regression, predictive analysis, qualitative analysis, and text mining, among other things. When done correctly, there is no such thing as cookie-cutter data science at this stage.

The data scientist communicates insights during the final stage. Data visualization, data reporting, the use of various business intelligence tools, and assisting businesses, policymakers, and others in making better decisions are all part of this.

Preparation and Exploration of Data Science


The most important data science skills are data preparation and analysis, but data preparation takes up 60 to 70% of a data scientist’s time. Data is rarely generated in a corrected, structured, and noise-free state. The data is transformed and prepared for use in this step.

This step entails data transformation and sampling, as well as feature and observation verification and noise removal using statistical techniques. This step also reveals whether the data set’s various features are independent of one another, as well as whether there are any missing values.

This step of exploration is also a key distinction between data science and data analytics. Data science takes a big picture approach, aiming to ask better questions of data in order to get more insights and knowledge out of it. Data analytics already has the questions and uses a more focused approach to find specific answers rather than exploring. See how OmniSci combines accelerated analytics and data science.

Modeling with Data Science


Data scientists use machine learning algorithms to fit the data into the model during the modeling stage. The type of data and the business requirement influence model selection.

The model is then put to the test to see how accurate it is and what other characteristics it has. As a result, the data scientist can tweak the model to get the desired result. If the model isn’t quite right for the task, the team can choose from a variety of data science models.

The model can be finalized and deployed once proper testing with good data produces the desired results for the business intelligence requirement.

The Importance of Data Science


By 2020, there will be approximately 40 zettabytes of data—40 trillion gigabytes—on the planet. The amount of data available is increasing at an exponential rate. According to sources like IBM and SINTEF, about 90% of this massive amount of data is generated in the last two years at any given time.

Every day, internet users generate roughly 2.5 quintillion bytes of data. Every person on the planet will generate about 146,880 GB of data per day by 2020, and 165 zettabytes per year by 2025.

This means there’s still a lot of work to be done in data science—a there’s lot more to learn. Only about 0.5 percent of all data was analyzed in 2012, according to The Guardian.

Data from a single source or a small amount of data can be interpreted using simple data analysis. Data science tools, on the other hand, are essential for comprehending big data and data from multiple sources in a meaningful way. A look at some of the business applications of data science demonstrates this point and provides a compelling introduction to data science.

What Are the Applications of Data Science?


In healthcare, marketing, banking and finance, and policy work, data science applications are frequently used. Here are a few examples of data science services in action in some of the most popular data science fields:

How is Data Science Changing Health Care?

Consumers and healthcare providers alike are using data generated by wearables to monitor and prevent health problems and emergencies, thanks to data science. A “big data revolution” in healthcare was described by McKinsey in 2018. According to McKinsey, incorporating data science into the US healthcare system could save $300 billion to $450 billion, or 12 to 17 percent of the total cost.

Data Analytics vs. Data Science

Although data scientists and data analysts work together on occasion, they are not the same thing. The term “data science analyst” refers to one of two things.

A data scientist enters the game earlier than a data analyst, tasked with examining a large data set, determining its potential, identifying trends and insights, and visualizing them for others. Data is seen by a data analyst at a later stage. They summarize what it tells them, prescribe improvements based on their findings, and optimize any data-related tools.

The data analyst is most likely examining a specific dataset of structured or numerical data in order to answer a specific question or questions. A data scientist is more likely to work with both structured and unstructured data in larger quantities. They will also develop, test, and evaluate data questions within the context of a larger strategy.

Data analytics is less about predictive modeling and machine learning and more about putting historical data into context. Data analysis isn’t about searching for the right question with an open mind; it’s about having the right questions in place from the beginning. Data analysts, unlike data scientists, do not usually create statistical models or train machine learning tools.

Data analysts, on the other hand, concentrate on business strategy, comparing data assets to various organizational hypotheses or plans. Localized data that has already been processed is also more likely to be used by data analysts. Processing raw data and analyzing it, on the other hand, requires both technical and non-technical data science skills. Both roles, of course, necessitate mathematical, analytical, and statistical abilities.

In their daily work, data analysts don’t require a broad business culture approach. Instead, when analyzing data, they tend to take a more measured, nailed-down approach. They will almost certainly have a smaller scope and purpose than a data scientist.

In conclusion, when looking at data, a data scientist is more likely to look ahead, predicting or forecasting. The data analyst’s relationship with the data is retrospective. A data analyst is more likely to dig into existing data sets that have already been processed for insights to answer specific questions.

Read Complete Introduction to Data Science: Lesson 2

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