Importance of Data Science – The Current Scenario
In a world which is increasingly becoming a digital space, organizations deal with zetta and yottabytes of structured and unstructured information every day. Evolving technologies have enabled better cost savings and smarter storage spaces to store critical data.
In the current industry, there is a huge need for skilled, certified data scientists. They are among the highest-paid professionals in the IT industry. According to Forbes, “the best job in America is as a Data Scientist with a median base salary of $110,000” annually. Only a few people have the capability to process it and provide valuable insight out of it. Furthermore, looking at the huge requirement that is increasing at a continual pace, McKinsey has predicted that there will be a 50 percent of the gap in the supply of data scientists versus its demand in the upcoming years.
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In the recent years, there is a huge growth in the field of Internet of Things (IoT), due to which the 90% of the data that is present in the current world has been generated. Every day 2.5 quintillion bytes of data is generated, and it accelerates with the growth of IoT. This data comes from all sources:
- The Sensors which are used in the shopping complex to gather shopper information.
- The posts which people make in social media platforms.
- The digital pictures and videos we capture in our phone.
- The purchase transaction which is made through e-commerce.
This data can be known as big data.
Companies are flooded with colossal amounts of data. Thus, it is very important to know what to do with this silo of data and how to utilize it.
Here, the role of data science comes in picture. Data science brings together a lot of skills like statistics, mathematics, and business domain knowledge and helps the organization to find ways to:
- reduce costs,
- getting into a new market,
- tapping a different demographic,
- gauging the effectiveness of a marketing campaign,
- launching a new product or service etc.
So regardless of the industry vertical, data science is likely to play a key role in your organization’s future success.
Look at the below infographic you will be able to understand how Data Science is creating its impression:
How Top Industry Players are Using Data Science?
IT organizations need to address their complex and expanding data environments in order to identify new value sources, to exploit future opportunities, and to grow or optimize efficiently. The differentiating factor for an organization is ‘what value they extract from their repository of data using analytics and how well they present it’. Here we list some of the biggest and best companies that are hiring data scientists at top-notch salaries.
So, What Is Data Science?
People say data science is little complex and it is a combination of many specific domains and skills. It encompasses all the ways in which information and knowledge are extracted from data. The term ‘Data Science’ is the study which deals with identification, extraction, and representation of meaningful information from raw data set to be used for business determinations.
As Data science is multidisciplinary, it deals with
- Statistical Modeling,
- Signal Processing,
- Computer Science & Programming,
- Database Technologies,
- Data Modeling,
- Machine Learning,
- Natural Language Processing,
- Predictive Analytics,
- and so on.
Many people get confused between Data science and Data analytics.
Yes, they are closely related, but one of the components of data science is data analytics, which is mainly used to understand how an organization’s data looks like, whereas the output of analytics is taken in the data science to solve problems and bring business insights.
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Data analysts and Data Scientists are different. Scientists ask questions or reports and analysts begin with mining or sourcing the data. Let’s look at some fundamental differences between a Data Scientist and a Data Analyst in the below table:
|Criteria||Data Scientist||Data Analyst|
|Goal||Asking business questions and working for solutions||Analyzing and sourcing data|
|Tasks||Data mining, preparation, and analysis to get information||Data inquiring, collecting and combining to find insights and patterns|
|Substantive expertise||Required||Not Required|
|Non-technical skills||Required||Not Required|
“Data Scientist is better at statistics than any software engineer and better at software engineering than any statistician.” ― Josh Wills, Director of Data Engineering at Slack
There is no clear-cut definition of what exactly comprises a Data Scientist’s roles and responsibilities. It can involve anything from optimizing the sales funnel to getting the right strategy.
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Data Science Life Cycle
For a better understanding of what data science is let’s explore its life cycle:
Suppose, Mr. X is the owner of a retail store and his goal is to improve sales of his store and which is the identify the drivers of sales. Also, to accomplish the goal he needs to answer the following questions:
- Which products in the store are the most profitable?
- How are his in-store promotions working?
- Are the product placements effectively working?
His goal is to uncover these important factors which would surely influence the outcome of the project. So, he appoints you as a Data Scientist. Let’s solve this problem using Data science life cycle.
- Data Discovery Phase
The first step is the Data Discovery Phase for any data science approach. It includes the ways to discovery data from various sources which could be unstructured data like in videos or images, structured data like in text files and relational database systems and so on. Organizations are also tapping into customer social media data and so on, to understand their customer mindset better.
As per our example, in this stage as a data scientist, our objective is to boost the sales of Mr. X retail store, the factors which are affecting the sales can be:
- store location,
- store staff,
- store hours,
- product placement,
- product pricing
- competitor location and promotions, etc.
Keeping these factors in our mind, we will develop clarity on the data and will be procured this for our analysis. At the end of this stage, you will collect all data that contain all this information listed.
- Data Preparation Phase
Once the data discovery phase is completed the next stage is the Data Preparation Phase. It includes converting disparate sources of data into a common format in order to work with it seamlessly. This process involves collecting the clean data subsets, inserting the suitable defaults, or it can involve more complex methods like identifying the missing values by modeling. Once the data cleaning from various data sources is done then the next step is to integrate and create the conclusion from the dataset for analysis. This involves the Integration of data which includes the merging two or more table sets of same objects but stores different information or the summarization of fields in a table by using aggregation. Here, we will also try to explore and understand what patterns and values our dataset has.
- Mathematical Models
Do you know, all data science projects have certain Mathematical Models driving them. These models are planned and built by the data scientists in order to suit the specific need of the business organization. This might involve various areas of the mathematical domain including statistics, logistic and linear regression, differential and integral calculus. The various tools and apparatus used could be R statistical computing tools, Python programming languages, SAS advanced analytical tools, SQL and various data visualization tools like Tableau and QlikView. Here, to generate a satisfactory result, one model will not be enough. We need to use two or more models. In this scenario, Data scientist will create a group of models which can be used for the task. After measuring the model, Data scientist will revise the parameter and fine-tune them for the next modeling run. This process will continue until you strongly believe that you have found the best model.
In this stage, Mr. X Data scientist will build the mathematical models based on the business needs of Mr. X that is product A or product B is the most profitable in the store or are they placement effectively working in the store.
- Getting things in Action
Once the data is prepared and the models are built it is time to getting the models working in order to get the desired results. There might be various discrepancies and a lot of troubleshooting that might be needed and thus the model might have to be tweaked. Here, your model evaluation explains the performance of a model.
In this stage, Mr. X Data scientist will gather information and derive outcomes based on the business requirements of Mr. X
- Communicating the findings
Communicating the findings is last but not the last step in the data science endeavor. The data scientist needs to be a liaison between the various teams and should be able to seamlessly communicate his findings to the key stakeholders and decision-makers in the organizations so that actions can be taken based on the recommendations of the data scientist. Here, based on the finding Mr. X Data scientist will communicate and recommend the changes in the business strategy so that Mr. X can earn maximum profit.
Data Science Components
Some of the key Components of Data Science are:
- Data (and its various types)
The raw dataset is the foundation of data science and it can be of various types like Structured Data (Tabular form), Unstructured Data (images, videos, emails, PDF files etc.)
- Programming (Python, R)
Data management and analysis is done by computer programming. In the data science, two programming languages are most popular – Python and R.
- Statistics and Probability
Data is manipulated to extract information out of it. The mathematical foundation of data science is statistics and probability. Without having a clear knowledge of statistics and probability, there is a high possibility of misinterpreting the data and reaching an incorrect conclusion. That’s the reason, why Statistics and Probability play a crucial role in data science.
- Machine Learning
As a Data scientist, every day you will be using machine learning algorithms like regression and classification methods. So, it is very important for a data scientist to know Machine learning as a part of their job, they must predict valuable insights from available data.
- Big Data
In the current world, data is compared with crude oil which is a valuable raw material, and as we extract the refined oil from the crude oil similarly by applying data science we can extract different kinds of information from raw data. The different tools used by data scientists to process big data are Java, Hadoop, R, Pig, Spark, etc.
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How Intellipaat Helps You Making a Career in Data Science?
Data science is not all about money, however, it allows you to gain immense knowledge also. So, it is this heady mix of money and deep domain knowledge that makes data science such an enviable career option for budding technology professionals.
Intellipaat provides huge opportunities to the aspirants who are willing to establish themselves as all-rounders in this area. Hence, getting trained in data science technologies provided by Intellipaat will be the best career move you will ever make. Intellipaat offers a wide range of courses dedicated to providing you an end-to-end knowledge about the trending and highly in-demand Data science skills in this domain.
It is not for nothing that the Harvard Business Review has mentioned that Data Science is the hottest job opportunity of the twenty-first century. Today if any digitally driven organization is starved of data even for a short duration of time then it loses its competitive edge. Data Scientists help organizations to make sense of their customers, markets, and business as a whole.
If you want to become a Google Data Scientist at the best salaries, then you need to be at the top of your game. If you are wondering how to learn data science, then Intellipaat is just the right place to start your incredible data science journey.
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