In this article we learn about 'what is data science and how does it work'. It will also help you obtain knowledge about how data science works, and it will enable you to explore various job profiles associated with Data Science.
Nowadays, organizations are overwhelmed with data, Data Science will help in extracting meaningful insights from that by combining various methods, technology, and tools. In the fields of e-commerce, finance, medicine, human resources, etc, businesses come across huge amounts of data and Data Science tools and technologies help them to process all of them.
Early in the 1960s, the term “Data Science” was coined to help the comprehension and analysis of the massive volumes of data that were being gathered at the time. Data science is a discipline that is constantly developing, employing computer science and statistical methods to acquire insights and generate valuable predictions in a variety of industries.
Data science relies on statistics to capture and transform data patterns into usable evidence through the use of complex machine learning techniques.
Python, R, and SQL are the most common programming languages. To successfully execute a data science project, it is important to instill some level of programming knowledge.
Making accurate forecasts and estimates is made possible by Machine Learning, which is a crucial component of data science. You must have a firm understanding of machine learning if you want to succeed in the field of data science.
A clear understanding of the functioning of Databases, and skills to manage and extract data is a must in this domain.
You may quickly calculate and predict using mathematical models based on the data you already know. Modeling helps in determining which algorithm is best suited to handle a certain issue and how to train these models.
It helps in accurately displaying data points for patterns that may appear that satisfy all of the data’s requirements. In other words, it involves organizing, ordering, and manipulating data to produce information that is insightful about the supplied data. It also involves converting raw data into a form that will make it simple to grasp and interpret.
It is the process of using historical data along with various techniques like data mining, statistical modeling, and machine learning to forecast future results. Utilizing trends in this data, businesses use predictive analytics to spot dangers and opportunities.
It is an in-depth examination to understand why something happened. Techniques like drill-down, data discovery, data mining, and correlations are used to describe it. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.
Prescriptive analysis advances the use of predictive data. It not only foresees what is most likely to occur but also offers the best course of action for dealing with that result. It can assess the probable effects of various decisions and suggest the optimal course of action. It makes use of machine learning recommendation engines, complicated event processing, neural networks, simulation, graph analysis, and simulation.
The first step is to identify what type of data needs to be analyzed, and this data needs to be exported to an excel or a CSV file.
It is essential because before you can read the data, you must ensure it is in a perfectly readable state, without any mistakes, with no missing or wrong values.
Analyzing the data is done by visualizing the data in various ways and identifying patterns to spot anything out of the ordinary. To analyze the data, you must have excellent attention to detail to identify if anything is out of place.
A data engineer or scientist writes down instructions for the Machine Learning algorithm to follow based on the Data that has to be analyzed. The algorithm iteratively uses these instructions to come up with the correct output.
In this step, you uncover your findings and present them to the organization. The most critical skill in this would be your ability to explain your results.
Here are a few examples of tools that will assist Data Scientists to make their job easier.
The product recommendation technique can influence customers to buy similar products. For example, a salesperson of Big Bazaar is trying to increase the store’s sales by bundling the products together and giving discounts. So he bundled shampoo and conditioner together and gave a discount on them. Furthermore, customers will buy them together for a discounted price.
It is one of the widely applied techniques in Data Science. On the basis of various types of data that are collected from various sources weather forecasting and future forecasting are done.
It is one of the most logical applications of Data Science. Since online transactions are booming, losing your data is possible. For example, Credit card fraud detection depends on the amount, merchant, location, time, and other variables. If any of them looks unnatural, the transaction will be automatically canceled, and it will block your card for 24 hours or more.
The self-driving car is one of the most successful inventions in today’s world. We train our car to make decisions independently based on the previous data. In this process, we can penalize our model if it does not perform well. The car becomes more intelligent with time when it starts learning through all the real-time experiences.
When you want to recognize some images, data science can detect the object and classify it. The most famous example of image recognition is face recognition – If you tell your smartphone to unblock it, it will scan your face. So first, the system will detect the face, then classify your face as a human face, and after that, it will decide if the phone belongs to the actual owner or not.
Speech recognition is a process of understanding natural language by the computer. We are quite familiar with virtual assistants like Siri, Alexa, and Google Assistant.
Data Science helps in various branches of healthcare such as Medical Image Analysis, Development of new drugs, Genetics and Genomics, and providing virtual assistance to patients.
Google, Yahoo, Bing, Ask, etc. provides us with a lot of results within a fraction of a second. It is made possible using various data science algorithms.
Data science is a field of study that uses data for various research and reporting purposes to derive insights and meaning from that data.
Data scientists create and use algorithms to analyze data. This process generally involves using and building machine learning tools and personalized data products to help businesses and clients interpret data in a useful manner.
One of the most important examples of data science now would be its use in studying the COVID-19 virus and coming up with a vaccine or a treatment. Data science also includes fraud detection, customer care automation, healthcare recommendations, fake news detection, eCommerce and entertainment recommendation systems, and more.
Check out Great Learning’s MS in Data Science Programme for all the details you need.
Yes, but to become an expert, you must enroll in a course that offers you proper training, guidance, and mentoring.
Data Science tools and techniques contribute a lot to the growth of a business. Every business is undergoing a digital transformation, and there is an increasing demand for candidates with relevant skills and knowledge companies offer competitive salaries for the right talent. If you’re interested in a career in data science or shift your career to roles such as Business Analysts, Data Analysts, Data Engineers, Analytics Engineers, etc. Check out Great Learning’s postgraduate program in Data Science and Engineering, which will help you acquire relevant data science tools, techniques, and hands-on applications through industry case studies.
Original article source at: https://www.mygreatlearning.com
For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.
IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.
With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.
Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.
#people #data science aspirants #data science course director interview #data science courses #data science education #data science education market #data science interview
Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.
Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.
Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.
Some Of The Advantages Are Mentioned Below:-
Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.
Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.
Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.
Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.
Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-
Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.
Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.
#data science training in noida #data science training in delhi #data science online training #data science online course #data science course #data science training
Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this article, we list down 50 latest job openings in data science that opened just last week.
(The jobs are sorted according to the years of experience r
Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.
Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.
Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.
Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.
**Location: **Bibinagar, Telangana
Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.
#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india