Data Science is a field of Scientific theories where an Unstructured, Raw data is taken and moulded into meaningful information by means of Programming, Business skills, and Analytics. The data science concept came into existence in 2008 when the companies needed a technique to Organize and Analyze a large junk of information. 

In 2009 McKinsey & Company posted an Article stating a Quote by Hal Varian

“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – that’s going to be a hugely important skill in the next decades”

Hal Varian is a chief economist at Google and UC Berkeley professor of information sciences, business, and economics. 

Data Science

Importance of Data Science in Current Scenario

Many Multinational Companies across the globe are using Digital methods to Rationalize their work and Maintaining their Inventory. This Technological advancement helps in various factors like Cost Saving, Resource Saving, and Time-Saving. During the Current Scenario Data Scientists are in huge Demand as stated too that the Highest Paying Job in America as per record is for Data Scientists. The IT sector is Hiring Highly skilled and Certified Data Scientists for their Company to make their work flexible.

BIG DATA: HOW BIG IS IT??

Big Data is that large volume of data counted in petabytes to Exabytes, It has from Millions to Billions to reach Trillions of Customers Data related every Digital Action performed. 

The various sources to collect Big Data are: 

  1. Sensors used in Shopping malls
  2. Websites Registrations 
  3. Sales/ Record of Transactions made from ATM
  4. Customer Call Centres
  5. Digitally Captured Pictures and Videos

Thus, Companies are getting data from every action performed by Customer and they have no Clue “How to Organise Data and Bring it in Use” 

How will you manage all this Data?? Will Data Science be useful enough??

The concept of Data Science comes Into action as Data Science brings together a lot of skillsets like mathematics, statistics, and business domain knowledge and helps an Organization to Organize data in ways like:

  • Reduced costs
  • Get into the new markets
  • Step on various demographics
  • Take lead on the marketing campaigns
  • Launching a new product or service

Data scientists rely completely upon the theories of artificial intelligence, especially its subfields of machine learning and deep learning, they are used to create models and make future predictions of making this raw data to useful purpose using algorithms and other techniques. We will further know important facts about data science in the article.

What is the 5 Step Life cycle of Data Science??

Data science generally has a life cycle that consists of five stages:

Step 1: CAPTURE: Data acquisition, data entry, signal reception, data extraction

Step 2: MAINTAIN: Data warehousing, data cleansing, data staging, data processing, data architecture

Step 3: PROCESS: Data mining, clustering/classification, data modeling, data summarization

Step 4: COMMUNICATE: Data reporting, data visualization, business intelligence, decision making

Step 5: ANALYZE: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis

The most important thing is that all 5 steps require different kinds of algorithms and Skills to make them effective. 

Thus, after understanding the Process of Data Science let’s see the uses of Data Science there are various uses such as:

  1. Automation and decision-making (background checks, creditworthiness, etc.)
  2. Classifications (in an email server, this could mean classifying emails as “important” or “junk”)
  3. Forecasting (sales, revenue, and customer retention)
  4. Pattern detection (weather patterns, financial market patterns, etc.)
  5. Recognition (facial, voice, text, etc.)
  6. Recommendations (based on learned preferences, recommendation engines can refer you to movies, restaurants, and books of your choice)
  7. Anomaly detection (fraud, disease, crime, etc.)

COMPONENTS OF DATA SCIENCE

The concept of Data science consists of various components or sub building units that help to segregate or segment data using calculus and algorithms, it’s a time-saving process. 

  • DATA

The data can be either in structured form or unstructured form, the structured form data can be in form of a tabular form or Excel sheets, etc whereas the unstructured form of data can be Images, audios, videos, pdf files, etc

  • STATISTICS AND PROBABILITY 

DML Data Manipulation Language is used to Manipulate and Extract meaningful data out of junk. Until and unless the Data Scientists don’t have a good knowledge about statistics and Probability they are not capable of segmenting data, it may lead to the high possibility of misinterpreting data and reaching to the incorrect conclusions.

  • PYTHON AND R PROGRAMMING LANGUAGES

The Data Manipulation can be done easily with the help of two most commonly used programming languages namely: R and Machine Learning with Python they are used for Machine Learning and Artificial intelligence too.

  • MACHINE LEARNING 

The Data Scientists have to work over the algorithms of Machine learning in day to day life. The regression and Classification concepts help the Data scientists to predict the valuable insights from all the Unstructured or structured form of Data available. 

  • BIG DATA

The concept of Big data helps to extract the main information out of all the possible raw data available. Like we can separate oil from water. The Data Scientist uses various concepts and skills to extract data like JAVA, R, Apache Spark, Hadoop Etc

Thus, there are many more components of data science available and every component works on different algorithms. 

APPLICATIONS OF DATA SCIENCE IN REAL LIFE 

The data science theories are implied in many days to day activities carried by an individual some of them are: 

  • HEALTHCARE 

The Data science theory helped effectively in various healthcare-related services. As we have a vast amount of data available now from various sources mentioned above; everything from the EMRs method to the clinical database recorded to personal health trackers, the medical professionals from everywhere are finding new and creative ways to understand the disease, the preventive medicine to cure the disease, the proper and effective diagnose for the particular diseases with speed and exploring the new treatment options.

  • LOGISTICS 

The United Parcel Services have turned the tables for data science to maximize the efficiency, both with the methods of internal and External passways and along with its delivery ways. The company’s On-road Integrated Optimization and Navigation tool is used in the data science algorithm to have a backup for statistical modeling and algorithms that helps to maximize the optimal routes for delivery for the efficient drivers based on the weather reports, sound and Huge traffic, High-level construction, etc. It’s stated that data science is benefiting the logistics company up to a record of 49 million gallons of fuel consumed and more than 200 million deliveries in miles carried out in a particular record of each year. 

  • SELF DRIVING CARS

The big units of the industries have proposed a high-class service for the people of the country to have a relaxed and comfortable driving experience. BIG names like Tesla, Ford, and Volkswagen are working on the implementation of such a lavish lifestyle process and predictive analytics came into the role to present a new trend for increasing the Sales pitch of autonomous vehicles. These cars by big names use the number of countable tiny cameras and sensors attached in places to provide the information in real-time. Using the algorithm of machine learning, and the concept of predictive analytics and the theory of data science, the application of self-driving cars can adjust it’s speed limits to the max but lesser than risk turns to avoid the dangerous lane changes and even take passengers from wrong to the correct lane through the quickest route.

  • FINANCE 

The Concept of the latest techniques of Machine learning and the famous data science have efficiently saved the financial industry billions of dollars and not only once or twice but like an uncountable number of times. For example, John Pierpont Morgan’s Contract Special platform which uses Natural Language Processing to process the information and extract the important or useful data out of the raw data available about 20,000 commercial policies a year. The concept of data science, which would take around 260,000 hand workers labor hours to complete one particular task is now finished in a few hours simply. Also, fintech companies present in the scenario like Stripe and Paypal are investing in the high amount of money in data science to create machine learning tools that can quickly detect and prevent fraudulent activities happening in and around. 

  • CYBERSECURITY 

Data science Algorithms are useful in almost every industry, but we have still not lightened up the need for Data Science in cybersecurity. The top Multi-National Or International Company named cybersecurity firm Kaspersky is using data science and machine learning algorithms to detect over 560,000 new samples of malware present on the user interface daily basis. They are able to detect it instantly and implement theories and also learn new methods of dealing with various cyber crimes, through data science, which is essential to our safety and security in the future.

  • ENTERTAINMENT 

Did you ever wonder how Musical apps like Wynk and Spotify just randomly help you to recommend that perfect song you’re in the mood for? Or how the famous movie platforms like Amazon Prime or Netflix knows just what shows you’ll love to binge? The concept of data science, the music streaming wildly, and most times can be recorded in the curiosity lists of songs based upon the music genre or band you’re currently mingling into. Really isn’t it amazing?? Amazon prime or Hoststar data services will recognize your need for further inspiration and recommend the same kind of shows from its huge collection.

  • MANUFACTURING

In the Modern period, The concept and key partners of  Data Scientists are the new factory workers. That means that data scientists have achieved a remarkable position in the manufacturing industries to prove their success. Data Science is being Highly and very efficiently used in manufacturing industries these days for all sorts of activities like optimizing production, reducing the costs, and maximizing the profits. Moreover, with the emerging of new and effective technologies like the Internet of Things (IoT), data science has provided an interface to the companies to forecast the potential problems, monitor systems, and analyze the continuous stream of data.

Also, with the concept of data science, companies can keep a check on their energy costs and can also optimize their production hours.

With a complete and better analysis of customer reviews, data scientists can help the industries to make prominently very reasonable decisions and improve the quality of their products. Another important aspect of data science in industries working in the field of Automation. With the help of historical and real-time data, industries are able to develop autonomous systems that are helpful in boosting the production of manufacturing lines. It has reduced the possible opportunities for all the copied or dual basis jobs and introduced powerful machines that use machine learning technologies like reinforcement learning.

What are the Career Opportunities you can seek in the field of Data Science???

There are many career options present in the field of Data science other than just simply the Data Scientist, One needs to have a Certification in Data Science and learn all the concepts and algorithms of Data science to have a great career opportunity in the field of emerging Data Science that works on the concept of Artificial intelligence and Machine Learning.

  • Machine Learning Scientist: The role of Machine learning scientists is to carry out research and find out new methods of data analysis and create algorithms.
  • Data Analyst: The role of Data analysts is to utilize large data sets available in the raw form and to gather the information that meets their company’s needs.
  • Data Consultant: The role of Data consultants is to work efficiently in particular businesses to determine the best usage of the information segmenting and segregating through the data analysis.
  • Data Architect: The role of Data architects is to build possible and Optimal data solutions that are optimized for performance and design applications.
  • Applications Architect: The role of  Applications architects is to easily keep track of how the particular applications are used in a business entity and how they interact with users and other applications.

Why should you become a Data Scientist???

The following Diagram showcase a study regarding why an Individual should opt Data Science as their career: 

28%

Demand Increase by 2020

4,524

Number of Job Openings

$120,931

Average Base Salary

#1

Best Job in America 2016, 2017, 2018

What are the Techniques used and worked upon when Data Science comes into Play?

There are so far 3 most important techniques an Individual must focus upon:

  • Dimensionality reduction: It is a very useful technique to reduce the complexity of data computation so that it can be performed more easily and quickly.
  • Clustering: It is a technique used to cluster the data together.
  • Machine learning: It is a technique used to perform tasks by recognizing patterns from data.
What are the Technologies used in the Process of Data Science???

The Data Science concept consists of certain Techniques and Technologies which help a business Entity to Maximize their profit in no time.

  • Apache Hadoop is a software framework that is used to process data over large distributed systems.
  • Python is a programming language with a very simple syntax that is commonly used for data science. There are a number of python libraries that are used in data science including NumPy, pandas, and scipy.
  • Tableau makes a variety of software that is used for data visualization.
  • TensorFlow is a framework for creating machine learning models developed by Google.
  • Pytorch is another framework for machine learning developed by Facebook.
  • R is a programming language that was designed for statisticians and data mining and is optimized for computation.
  • Jupyter Notebook is an interactive web interface for Python that allows faster experimentation.

What are the Impacts of Data Science on the Current Scenario?

Big data is very quickly becoming a vital tool for businesses and companies of all sizes. The availability and interpretation of big data have altered the business models of old industries and enabled the creation of new ones. Data-driven businesses are worth trillion collectively, an increase from billion in the years. Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations. As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two. 

What are the various Jobs Offered through the knowledge of Data Science?

  • Data Scientist

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to the source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills Required: Programming skills (SAS, R, Python), statistical and mathematical skills, data visualization, Hadoop, SQL, machine learning.

  • Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills Required: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

  • Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills Required: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Name Few Certifications that can help to apply for a position on Data Scientists??? 

The list of Top 15 data science certifications are: 

  1. Certified Analytics Professional (CAP)
  2. Cloudera Certified Associate: Data Analyst
  3. Cloudera Certified Professional: CCP Data Engineer
  4. Data Science Council of America (DASCA) Senior Data Scientist (SDS)
  5. Data Science Council of America (DASCA) Principle Data Scientist (PDS)
  6. Dell EMC Data Science Track
  7. Google Certified Professional Data Engineer
  8. Google Data and Machine Learning
  9. IBM Data Science Professional Certificate
  10. Microsoft MCSE: Data Management and Analytics
  11. Microsoft Certified Azure Data Scientist Associate
  12. Open Certified Data Scientist (Open CDS)
  13. SAS Certified Advanced Analytics Professional
  14. SAS Certified Big Data Professional
  15. SAS Certified Data Scientist
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A Computer Science Engineer Munchkin Finding her paths Mingling in Marketing field of MBA. Having profound Interest in Writing Techie and Non -Techie stuff. Besides, Love to wander in all Valleys and Alleys too.

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