Over the last decade, there has been a massive surge in the data generated and retained by the companies. Nowadays, Data Science is a blend of different types of tools, algorithms, and machine learning principles with the need to figure out hidden patterns from the raw data.
It is a study that requires identification, representation, and extraction of meaningful information from varied data sources which can be used for business purposes. The concept of data science employs techniques and theories from many fields within the context of mathematics, statistics, information science, and computer science.
Why is data science so important?
Data science is important to any business who uses their data. From retrieving statistical data to gaining insights across workflows and hiring new candidates, data science has always been vital for any business. It also helps the senior staff to make better-informed decisions in terms of business operations.
Data science empowers management and officers to make better decisions. Companies tend to invest a lot of money in data science so they could get the right information to make the right decisions. With the use of data science, an organization now can connect with their customers in a personalized approach, thereby ensuring better brand power and engagement.
A person skilled in this domain is trained to identify data that stands out in some way or the other. They create statistical, network, path, and big data methodologies for predictive fraud propensity models. The data is then utilized to create alerts which help to ensure timely responses when unusual data is recognized.
Data science is accessible to almost all sectors. There is a large volume of data available nowadays and utilizing them in a proper manner can bring in success or failure for an organization. Utilizing data in a proper manner will hold the key to achieving goals for brands, especially in the coming times.
One of the most integral aspects of data science is that the results can be implemented in almost any sector like travel, healthcare, education, IT and almost any other sectors. Understanding the implications of data science can always be in helpful in different sectors to analyze their challenges and address them in an efficient manner.
Why is data-driven decision making important?
Data-driven business decisions can make or break an organization. Such governance is usually undertaken in order to be more competitive. Companies that go for data-driven decision-making approach collaboratively, generally tend to treat information as a real asset more than in companies with other approaches.
Data-driven decisions in an organization limit the risk and increase the likelihood of the desired outcome. It helps a business to define and set strategic, realistic and achievable goals.
Data-driven decision management (DDDM) is an approach to business control which gives importance to decisions that can be backed up with verifiable data. The success of the data-driven approach is dependant on the quality of the data that is collected and the effectiveness of its analysis and interpretation.
Is Big Data the same as data science?
Big data processing usually begins with collecting data from multiple sources. Hence, the field of data science is said to be evolved from big data. So, big data and data science are regarded as inseparable. But there are many differences between big data and data science.
Big Data: Big data refers to the huge volumes of data of varied types, i.e., structured, semi-structured, and unstructured. When data sets get so big that those cannot be analyzed by traditional data processing application tools, it becomes ‘Big Data’.
Data Science: Handling both unstructured and structured data, Data Science is a field comprising of everything related to data cleansing, preparation, and analysis of data. Data Science can be considered as a blend of statistics, mathematics, programming, problem-solving, capturing data in inventive ways. It is the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.
Why is Big Data important?
The reasons for which Big Data is important are as follows-
Comprehend the market condition: With the help of Big Data, an organization can foretell what future customer behavior will be like. Big Data is easy to figure out purchasing patterns, choices, product preferences. This will leverage the company and also help to contest competitors.
Know your Customer Better: With the help of big data analysis, companies can now come to know the general thought process and feedback beforehand and also make relevant course corrections. Companies can reduce complaints and resolve it before it is escalated at a higher level. There are big data tools which can predict negative emotions and prompt action can be taken to mitigate the same by organizations.
Control Online Reputation: Sentimental analysis can always be done through Big Data Tools. A company can check on online reputation and manage their online image efficiently and effectively with the help of these tools.
Cost Saving: There might be an initial cost of application of Big Data tools. In the long run, the benefits will surely outweigh the cost. With the application of real-time big data tools, the IT staff will now be less burdened. These resources can be used elsewhere. The application of big data technology will make data storage and more accurate.
Availability of Data: With the help of Big Data tools, relevant data is now available, in an accurate and structured format, in real time.
What are the 5 V’s of big data?
The 5 V’s of big data are as follows:
Velocity relates speed at which new data is usually generated and the speed at which data tends to moves around. Almost every day, it has been seen that the number of emails, photos, twitter messages, video clips, and other forms of data increases at lightning speeds across the world.
The data must not only be analyzed, but the speed of data transmission and access to the data must also remain immediate to allow for real-time access to website, instant messaging and credit card verification. Big data technology enables us to analyze the data seamlessly while it is being generated, without putting it into databases.
Volume talks about the vast amount of data generated every second. The vast amount of data is so much in terms of volume, that we can no longer store and analyze data using traditional database technology. Distributed systems are now in use, where several parts of the are is stored in different locations and are usually brought together by software.
Variety refers to the different forms of data that we can use. Data nowadays look very different from the form of data that was there the past. There is no longer just structured data (name, phone number, address, financials, etc) which fits perfectly into a data table. But nowadays data is unstructured.
It is said that 80% of all the world’s data fits into this category, which includes photos, social media updates, video sequences, etc. With big data technology, we can now make use of differed types of data that include messages, photos, social media conversations, sensor data, voice or video recordings and bring them together in a comprehensive manner. The new and innovative big data technology now allows structured and unstructured data to be harvested, stored, and used simultaneously.
Veracity refers to uncertain or imprecise data in an organization. According to the definition, unstructured data comprises of a significant amount of imprecise and uncertain data. For example, social media data is uncertain. With many forms of big data available, quality and accuracy are now less controllable, Big data and analytics technology now allow us to work with these type of data.
The volumes usually cover up for the lack of quality or accuracy. So, organizations now must analyze both structured and unstructured data which is uncertain and imprecise. The level of uncertainty and imprecision will usually vary for different cases yet it must be factored.
Value refers to the ability to turn our data into value. It is important that businesses make a case for any attempt to collect and leverage big data. This refers to the ability to transform large volumes of data into business. When it is about value, the worth of the data being extracted is always referred.
Endless amounts of data are useless if it cannot be turned into value. The most important part of going for a Big Data initiative is to understand and figure out the costs and benefits of collecting and analyzing the data in order to ensure that the data that is reaped can be monetized.
What is the difference between a data analyst and a data scientist?
A Data Analyst usually runs a query against the new data to figure out trends that are important for an organization. They help to prepare data for the Data Scientists. Data Analysts are proficient in SQL as well as being knowledgable of the core metrics an organization considers as important.
They are also able to write scripts and produce intuitive visuals. Data Analysts also plays an important role in Data Science. They perform a variety of tasks like collecting and organizing data and gathering statistical information out of the data collected. They are also responsible for representing the data in the form of charts, tables, and graphs and use the same to develop relational databases for systems.
Based on the skills sets, a Data Analyst can also be divided into 4 different roles
- Data Architects
- Database Administrators
- Analytics Engineer
A Data Scientist is a professional who has the ability to understand data from a business point of view. The person is in charge of making predictions to help businesses take accurate decisions. Data scientists possess in-depth knowledge of computer applications, statistics, modeling, and mathematics.
What sets a data scientist apart is their proficiency in business coupled with great communication skills, to handle both business and IT leaders. They are efficient in figuring out specific problems, which will add value to the organization after resolving it.
A Data Scientist is entrusted with the responsibility of building models with the help of machine learning concepts. These models are expected to develop an organization’s software with product features that predict and explain thereby making the application more adaptive.
The quality of a model developed by a data scientist depends directly on how well they understand and prepare data. So, they work with the Data Analyst when it comes to an understanding and preparing data to build better models.
A Data Scientist can also be divided into 4 different roles based on the skill sets.
- Data Researcher
- Data Creatives
- Data Developers
- Data Businesspeople
Will data science become automated?
According to Gartner, more than 40% of data science tasks will be automated by the year 2020. While this can be true percentage wise, realistically AI can only replace data scientists when it comes to lower-level responsibilities, like data cleansing, ingesting, visualization, delivery, and model fitting.
The ability to dig insights from raw data is something data scientists are skilled at. Machines, or in other words, AI cannot judge what organizations required similar to what a human can. While AI is good at figuring out trends and patterns, AI is never expected to understand the trends with respect to the real world context and how it can impact business performance. So, Bots are only expected to automate a lower level task and not do tasks that a data scientist can do.
What is MS in data science?
A Master of Science in Data Science is considered to be an interdisciplinary degree program which is designed to provide studies in scientific methods, processes, and systems to extract knowledge or valuable insights from data in varied forms, either structured or unstructured.
It is a highly selective program for students having a strong background in mathematics, applied statistics and computer science. The degree usually focuses on the development of new methods for data science.
Pursuing data science can be a wise career choice for students looking forward to gaining a wide breadth of skills in various tech-related sectors. A typical data science program coursework features topics like applied statistics and hones a student’s programming skills in topics like SQL and Python as well.
Our experts believe that a master’s in data science will help prepare you for job opportunities in a wide variety of fields and sectors, which include data architecture, computer engineering, and programming.
Is Data Science a major?
Data Science is a major. It is a rapidly growing field offering students with exciting career opportunities and also facilitating advanced studies. The Data Science major provides students with a foundation in those aspects of computer science, statistics, and mathematics that are relevant for analyzing and manipulating voluminous and/or complex data.
It’s necessary for data science students to take courses in multiple academic departments:
- Computer science
- Machine learning
Is machine learning part of data science?
Machine learning and statistics are considered to be a part of data science. The very word learning in machine learning suggests that algorithms depend on some data, which can be used as a training set, to fine-tune some model or algorithm parameters.
Data science is a broad term considered for multiple disciplines, whereas machine learning usually fits within data science. The main difference between the two is that data science is considered as a broader term which not only focusses on algorithms and statistics but it also takes care of the entire data processing methodology. So, machine learning is a part of data science.
How To Become a Data Scientist
Can anyone learn data science?
Data science experts come from diverse backgrounds which include chemical engineering, economics, physics, statistics, mathematics, computer science, operations research, etc. You will find several data scientists with a bachelor’s degree in statistics and machine learning but this is not a requirement to learn data science. However, being familiar with the basic concepts of Math and Statistics like Linear Algebra, Calculus, Probability, etc. is important to learn data science.
Data scientists are highly educated. 88% have at least a Master’s degree and 46% have PhDs. A very strong educational background is usually required to develop the depth of knowledge required to be a data scientist.
What degree do you need in order to be a data scientist?
In order to be a data scientist, you need to have a bachelor’s degree in one of the following domain:
- Computer science
- Social science
- Applied math
At the end of pursuing one or more than one of these degrees, you are expected to have a wide range of skills which are applicable to data science. These skills include experimentation, quantitative problem solving, coding, handling large sets of data, and others.
The ability to understand people, marketing and businesses are also regarded as a powerful tool in a data science career. The skills are often seen to be highlighted in business, political science, psychology, and various liberal arts degrees. These are often considered as a great minor, which complements a data science degree or a technical degree.
Once you are done with the bachelor ‘s degree, you can apply for master’s in data or other relevant fields if you aim for a higher level position in this domain. Also, relevant experience in the field you wish to work in is equally important to be a data scientist.
Do you need a master’s degree or a Ph.D. degree to be a data scientist?
No, a Ph.D. is not necessary to become a data scientist but can be helpful if your Ph.D. was in some sort of quantitative field. This being said, some companies prefer hiring data scientists with PhDs and will not hire data scientists with only bachelor’s degrees (unless they have experience).
Real world data science experience always outweighs the time spent in pursuing a Master’s degree or a Ph.D. because getting these degrees can prove to be an extremely long grind. You have to work hard for a long period of time to acquire these degrees but eventually, you will have no real world experience in this domain.
According to our experts, a master’s in data science or a Ph.D. can be a good way to go, in developing a technical data science skill set for potential employers but it is not a requirement to start with a career in the field of data science.
Lack of a quantitative degree does not stop one from studying data science. It is possible to learn data science even without having a Master’s degree. Ph.D.s will matter only if you apply for a higher level in the domain of data science. When you begin to learn data science, Ph.D. or a Master’s Degree is not a necessity. If your goal is to opt for an advanced leadership position, you may have to earn either a master’s degree or doctorate.
What skills are needed to be a data scientist?
There are certain schools which offer specialized data science programs, which are specific to the educational requirements to pursue a career in data science. Students who do not wish to opt for this extensive approach can pursue other options in this domain.
This includes directed Massive Open Online Courses (MOOCs) and boot camps. Some of the data science programs that are worth exploring are Simplilearn’s Big Data & Analytics certification courses. These programs can help deepen your understanding of the core subjects which support the need to be a data scientist, along with providing a practical learning approach that you will not find in any textbook.
Important technical skills that are required to become a data scientist include:
Programming: You need to have in-depth knowledge of programming languages like Python, C/C++, Perl, SQL and Java. Python is regarded as the most common coding language required in data science roles. Programming languages help one clean and organize an unstructured set of data.
In-depth knowledge of SAS and other important analytical tools: The knowledge of analytical tools will help you extract valuable insights out of the cleaned and organized data set. Some of the most popular tools that data scientists commonly use include SAS, Hadoop, Spark, Hive, Pig, and R. Certifications in this domain will further help you to establish your expertise in the use of these analytical tools.
Must be skilled at working with unstructured data: This specifically emphasizes on the ability of a data scientist to understand and manage data which is coming unstructured from varied channels. If a data scientist works on a marketing project to help the marketing team offer insightful research, the professional should be proficient in handling social media as well.
Must possess a strong business acumen: The technical skills cannot be utilized in a productive way if a data scientist does not have proper business acumen and sound know-how of the elements that develop a successful business model. You won’t be able to recognize the problems and potential challenges that need solving for the business to sustain and develop. Without business acumen, you won’t really be able to help your organization explore new business opportunities.
Need to possess strong communication skills: If you are a data scientist, you should be able to understand data better than anyone. However, to be successful in your role, and for your organization to benefit from your services, you should be able to strongly communicate your level of understanding with someone who is a non-technical user of large volumes of data. You need to possess strong communication skills in order to be a data scientist.
Must have great data intuition: This is one of the most important skills that a data scientist requires. Great data intuition means observing patterns where none are noticeable on the surface and understanding the presence of where the value rests in the unexplored pile of data samples. This makes a data scientist more efficient in their work. This is an important skill which comes with experience and boot camps are an ideal way of polishing it
Why is python used for data science?
When it comes to data science domain, Python is considered to be a very powerful tool. Python is open sourced and flexible, which adds more to its popularity. It is known to have massive libraries for data manipulation and is extremely easy to learn and use for all data analysts.
People who are familiar with programming languages such as, Java, C++ or C, and Visual Basic, will find this tool to be very accessible and easy to work with. Apart from remaining an independent platform, this tool has the ability to efficiently integrate with the existing Infrastructure system and can also solve the most difficult of problems in a simplified way.
It is said, that this tool is powerful, friendly, easy and plays well with others, apart from running everywhere.
What are the other languages used?
Apart from Python, other languages used are-
Best Programs In Data Science
Which universities are the best ones for data science?
The following colleges are the best colleges/universities for data science.
- Southern Methodist University
- The University Of California-Berkeley
- Arizona State University
- Carnegie Mellon University
- Columbia University
- Cornell University
- Georgia Tech.
What is the cost of MS programs in data science across the world?
- USA- $55,000
- Australia- 28,000 Euro/year (A$ 40,160)
- Canada- 30, 000 EUR/year (CAD- 44983.44)
- Germany – 20,000 EUR/year
- UK- 20.000 EUR/Year (17515.60)
- New Zealand- 27.000 EUR/year (NZD- 44847.78)
- Sweden- 14,000 EUR/year (kr 145217.34)
- Singapore- S$35,000 (USD $25,560)
What are the best MS in data science programs across the world?
- MS in Data Science, Columbia University
- MS in Data Science, New York University
- MS in Computational Data Science, Carnegie Mellon University
- MS in Analytics, Northwestern University
- MS in Analytics, Georgia Institute of Technology
- MS in Machine Learning, Carnegie Mellon University
- MS in Analytics, Georgia Institute of Technology
- MS in Analytics, North Carolina State University
- Master of Data Science, University of British Columbia
- M.Sc. in Computing & Data Analytics, Saint Mary’s University
- Master’s in Big Data, Simon Fraser University
- Masters in Data Science and Analytics, Ryerson University
- Master of Management Analytics, Queen’s University.
- University of Magdeburg, Data Science and Knowledge Engineering (M.Sc.)
- The Technical University of Munich, Master’s Mathematics in Data Science
- The University of Mannheim, Data Science M.Sc.
- The University of Hildesheim, Master’s Program in Data Analytics
- The Leuphana University of Lüneburg. Management & Data Science (MSc)
- Monash University, Master of Data Science
- University of New South Wales (UNSW), Master of Information Technology (Data Science)
- The University of Melbourne, Master of Data Science
- The University of Queensland, Master of Data Science
- RMIT University, Master of Data Science
- Master of Applied Data Science- University of Canterbury
- Master of Analytics- Massey University
- Master of Business Data Science- University of Otago
- Master of Professional Studies in Data Science- The University of Auckland
Masters in Data science/Big data)
- The University of Warwick-
- London School of Economics
- The University of Leeds
- UCL- London’s Global University
- University of London
- University of Dundee
- The University of Edinburgh
- The University of Glasgow
- Masters in Data Science- Chalmers University of Technology
- Masters in Data Science- University of Skovde
- Applied Data Science- University of Gothenburg
- Master in Data Science- EIT Digital Master School
Is Data Science in demand?
Almost every industry, starting from the retail industry to manufacturing, collects data on their customers. That causes a surge of demand for data scientists who are skilled at interpreting all that data. According to LinkedIn salary data, nowadays, data scientists earn an average of $107,000 a year.
Powered by big data and AI, the demand for data science skills is rapidly growing exponential rate. However, the supply of skilled applicants is growing at a slower pace. It’s a great time to be a data scientist entering the job market.
The current demand for qualified data science professionals is just the beginning. In the next few years, the data science market will evolve to at least one-third of the global IT market from the current one-tenth scenario.
Is there a shortage of data scientists?
Data scientists are considered to be high in demand. Research by the Accenture Institute for High Performance has figured out that the world is facing a severe shortage of data scientists. There is simply not enough Ph.D. workforce to fill the jobs.
One of the main reason is that data scientists require a scarce combination of skills required to work as a data scientist in any organization. They must be equipped with high-level statistical and quantitative methods and tools, along with the new computing backgrounds, languages and techniques for managing and integrating large volumes of data sets.
Data scientists must also be skilled in industry knowledge and certain business acumen to create models and solve real-world intricacies. They also need to possess excellent communication and data visualization abilities in order to explain their models and findings to others. That combination is hard to find.
The shortage is particularly severe in the U.S. where 80 percent of new data scientist jobs are created between 2010 and 2011 and have not been filled. The shortage is getting worse.
How do I start a data science career?
The steps to start your data science career are as follows-
- You need to figure out what you need to learn. Data science requires the knowledge of a programming language and the ability to work with data in that language. A basic understanding of mathematics is also required to get started.
- Get comfortable with Python. R is also a great choice to begin with.
- Get acquainted with data analysis, manipulation, and visualization techniques. If you need to work data in Python, you should learn to use the pandas library.
- Learn machine learning tools and techniques.
- Understand the concept of machine learning in more depth. Focus on more practical applications and not just the theory part.
- Keep learning and practicing. Kaggle competitions are regarded as a great way to practice data science without coming up with the problem yourself.
- Join a peer group that would keep you motivated.
- Network, but do not waste much of your time on it!
What jobs are in data science?
Some of the popular jobs in the domain of data science, apart from being a data scientist include-
Data engineer: A data engineer is someone who develops, tests, constructs, and maintains architectures, such as databases and large-scale processing systems in an organization.
Data analyst: A Data Analyst interprets data and turns it into information which can offer ways to improve a business, thereby affecting business decisions.
Machine learning engineer: Apart from possessing in-depth knowledge in technologies such as SQL, REST APIs, and others, a machine learning engineer is also expected to perform A/B testing, and implement common machine learning algorithms like classification, clustering, etc and build data pipelines for an organization.
Data architect: A data architect develops the blueprints for data management so that the databases can be easily integrated, centralized, and also protected with the best security measures.
Data and analytics manager: A data and analytics manager looks after the data science operations. The person assigns the duties to their team according to skills and expertise. Their strengths include skilled in technologies like SAS, R, SQL, etc. and management.
Database Administrator: Database administrators are responsible for the proper functioning of all the databases in an organization. They grant or revoke its services to the employees of the organization depending on their requirements. A database administrator is also responsible for database backups and recoveries.
Statistician: A statistician has a sound knowledge of statistical theories and data organization. They extract and provide valuable insights from the data clusters. A statistician also helps to create new methodologies for the data engineers to apply.
Business Analyst: A business analyst has a good understanding of how data-oriented technologies work and on ways to handle large volumes of data. They are also skilled at separating the high-value data from the low-value data. A business analyst identifies how the Big Data can be linked to actionable business insights for suitable business growth.
Business Intelligence professional: Those adept at using OLAP tools, reports, and dashboards to look at historical trends in data sets are business intelligence professionals. Business intelligence can include data visualization. Popular business intelligence platforms include Qlik, Tableau and Microsoft Power BI.
Advanced analytics professional: An advanced analytics professional would typically perform simulations, predictive analytics, prescriptive analytics, and other forms of advanced analysis. He /she would differ from data scientists because he/she would not work with exceptionally large data sets or with unstructured data.
What do data scientists do?
Data scientists make use of their statistical, analytical, and programming skills to collect, analyze, and interpret large volumes of data sets. Data scientists are equipped with an array of technical competencies which usually include statistics, coding languages, databases, machine learning, and other reporting technologies.
- A data scientist works with stakeholders throughout the organization to figure out opportunities for leveraging company data in order to drive business solutions.
- Mine and analyze all the data from company databases in order to drive optimization and improvement of product development, business strategies, and marketing techniques.
- Assess and analyze the effectiveness and accuracy of new data sources and other data gathering techniques
- Build custom data models and algorithms to apply to data sets
- Enhancing data collection modes to include information that is relevant for building analytic methods
- Selecting appropriate features, along with building and optimizing classifiers with the help of machine learning techniques
- Extending the company’s data with a third party source of information as and when required
- Processing, cleansing, and also verifying the integrity of data used for analysis
- Performing ad-hoc analysis and presenting the results clearly.
- Developing automated anomaly detection modes and constant tracking of its performance
- Data scientist duties usually include creating various machine learning-based tools or processes within the organization, for example, recommendation engines or automated lead scoring systems. People within this role must be able to perform statistical analysis.
How many hours do data scientists work?
With a shortage in the number of data scientists, the approximate work hours are usually between 50- 60 hours per week.
What is the salary of a data scientist?
The average salary for a Data Scientist is $130,288 per year, but this varies widely based on a number of factors. One important factor that will determine salary is your actual title and relevant job skills and responsibilities.
The highest paying skills associated with this job are Data Mining, Data Warehouse, Machine Learning, Java, Apache Hadoop, and Python.