One of the most known and practically applied fields in most industries to learn and analyse their products and businesses is Data Science. However, there is a bit of difference between Data Science and Applied Data Science. Data science is a subpart of applied data science to some while for others, both terms are interchangeable. In layman’s terms, data science is the extraction of data to create a visualization, forecast, or manipulate the data accordingly. online courses and certifications It includes analysing the data and creating the visualizations as per the needs.

Coming to the difference between Data Science and Applied Data Science; in applied data science the art of researching is added on with data science.

Applied data science has a broad scope of data science which includes researching new applications of data science and creating new methods or functions for faster retrieval of data and analysis. Applied data scientists have higher and deep technical knowledge of how data science and its methods work as compared to data scientists.

Data Science vs. Data Analytics vs. Machine Learning: Expert Talk

Companies are now looking for professionals who can sift through the goldmine of data and help them drive swift business decisions efficiently. Data science, analytics, and machine learning are growing at an astronomical rate, and companies are now looking for professionals who can sift through the goldmine of data and help them drive swift business decisions efficiently. According to IBM, the number of job openings for all data professionals in the United States will increase by 364,000 to 2,720,000. In a Simplilearn Fireside Chat, we spoke with Eric Taylor, Senior Data Scientist at CircleUp, about what makes data science and data analytics such an intriguing field, as well as what skills would help professionals acquire a strong foothold in this fast-growing domain.

What is Data Science?

People have tried to define data science for over a decade now, and the best way to answer the question is via a Venn diagram. Created by Hugh Conway in 2010, this Venn diagram consists of three circles: math and statistics, subject expertise (knowledge about the domain to abstract and calculate), and hacking skills. Essentially if you can do all three, you are already highly knowledgeable in the field of data science.

Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions.

Skills Required to Become a Data Scientist

Anyone interested in building a strong career in this domain should gain critical skills in three departments: analytics, programming, and domain knowledge. Going one level deeper, the following skills will help you carve out a niche as a data scientist:

• Strong knowledge of Python, SAS, R, Scala
• Hands-on experience in SQL database coding
• Ability to work with unstructured data from various sources like video and social media
• Understand multiple analytical functions
• Knowledge of machine learning

What is a Data Analyst?

A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the necessary level of data science. 

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Skills Required to Become a Data Analyst

A data analyst should be able to take a specific question or topic, discuss what the data looks like, and represent that data to relevant stakeholders in the company. If you’re looking to step into the role of a data analyst, you must gain these four key skills:

• Knowledge of mathematical statistics
• Fluent understanding of R and Python
• Data wrangling
• Understand PIG/ HIVE

Data Science vs. Data Analytics

Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions, while a data analyst finds answers to the existing set of questions.

What is Machine Learning?

Machine learning can be defined as the practice of using algorithms to extract data, learn from it, and then forecast future trends for that topic. Traditional machine learning software is comprised of statistical analysis and predictive analysis that is used to spot patterns and catch hidden insights based on perceived data. 

A good example of machine learning implementation is Facebook. Facebook’s machine learning algorithms gather behavioral information for every user on the social platform. Based on one’s past behavior, the algorithm predicts interests and recommends articles and notifications on the news feed. Similarly, when Amazon recommends products, or when Netflix recommends movies based on past behaviors, machine learning is at work.

Skills Required to Become a Machine Learning Expert

Machine learning is just a different perspective on statistics. The following are critical skills that can help you jumpstart your career in this fast-growing domain:

• Expertise in computer fundamentals
• In-depth knowledge of programming skills
• Knowledge of probability and statistics
• Data modeling and evaluation skills

Data Science vs. Machine Learning

Because data science is a broad term for multiple disciplines, machine learning fits within data science. Machine learning uses various techniques, such as regression and supervised clustering. On the other hand, the data’ in data science may or may not evolve from a machine or a mechanical process. The main difference between the two is that data science as a broader term not only focuses on algorithms and statistics but also takes care of the entire data processing methodology.

Source: Quora

Data science can be seen as the incorporation of multiple parental disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, data analytics, and more. It includes retrieval, collection, ingestion, and transformation of large amounts of data, collectively known as big data. Data science is responsible for bringing structure to big data, searching for compelling patterns, and advising decision-makers to bring in the changes effectively to suit the business needs. Data analytics and machine learning are two of the many tools and processes that data science uses.

Data science, Data Analytics, and Machine Learning are some of the most in-demand domains in the industry right now. A combination of the right skill sets and real-world experience can help you secure a strong career in these trending domains.

Enroll in Our Data Analyst Master’s Program Today

If you’re ready to embark on your journey as a data analyst, the first step is enrolling in an accredited learning program that can prepare you for certification. Co-developed with IBM, our Data Analyst Master’s Program teaches students everything they need to become a skilled data analyst. 

Students in this course learn all of the tools and techniques that are needed to succeed as a data analyst, including SQL databases, and essential programming languages, such as Python and R. Enrollment includes lifetime access to self-paced learning, the opportunity to work on more than 15 real-world projects, $1,200 worth of IBM cloud credits, and so much more. 

Upon completion, students receive industry-recognized certificates from both Simplilearn and IBM, which can help put them one step ahead of the competition. Get started by enrolling today!

Find our Post Graduate Program in Data Science Online Bootcamp in top cities:

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What’s the Difference Between Data Science and Applied Statistics?

February 26, 2019     

Statistics and the use of data are big business. With the advent of technological advances in data collection and storage, it has become easier and less expensive to collect data in many fields, calling for new job openings for data scientists and statisticians. But these terms are often used interchangeably, which can be confusing, and leads to the question: what’s the difference between data science and applied statistics?

Data Science vs. Statistics: How Do They Differ?

In 2015, the American Statistical Association (ASA) released a statement regarding the newer field of data science — with no clear consensus. Data science was understood to include areas like:

• Database management
• Statistics and machine learning
• Distributed and parallel systems

Three years later ASA continued the discussion, with the roles and definitions of data science and statistics, continuing to cross categories and defy specificity. At times, this boils down to singular understandings like, “a data scientist is a data analyst who can code.” However, upon review of the variety of definitions and roles, it can be claimed that the purpose of a data scientist is to employ large data sets (the proverbial “big data”) to solve business and other everyday problems.

Statistics has been generally understood to be the use of both theory and experience to gain an understanding of a particular phenomenon or relationships between behaviors or events. Michigan Technological University’s online statistics program offers a deep dive into “statistical theory and methods to address the practical problems of an evolving society.”Learn More About Our Masters Program

Data Scientist vs. Statistician

While statisticians are thoroughly immersed in statistical theory, they can seamlessly move beyond theory and solve practical problems in the manner of a data scientist. Programs like Michigan Tech’s online master’s in applied statistics, prepares students to use theory to create innovative solutions that incorporate business culture and practices. In this sense, it becomes clear that there is no difference between data science and statistics that renders a statistician’s skills as being less practical than a data scientist’s.

It is also important to acknowledge that statisticians have always worked with large data sets and that work has informed data science as it is generally understood today. Statisticians aren’t strangers to developing models to organize large data sets into information that can be accurately interpreted and used to create new products, interventions, and practices. Applied statisticians use this ability to not only innovate, but to also provide a deep understanding of the problem to drive organizational growth and change.

Data Science and Statistics: Collaboration and Innovation

Research proves the value of exploratory analysis has increased with the expansion of data availability and computation; value has also increased for individuals’ ability to use data to think innovatively and create hypotheses. Technology is growing at an exponential rate to produce even more data with less expense. With the advent of more data obtained much more quickly, the need for statisticians and data scientists is growing. Working together, the statistician and the data scientist can join abilities to:

• Organize data
• Create appropriate and efficient computations
• Present accurate interpretations and efficient solutions

With the right educational program, the statistician and the data scientist can be one person.

Michigan Tech continues to lead the field of statistics, finding ways for practitioners to evolve, and paving the way for applied statisticians and data scientists to work in sync.

Intrigued by the various applications of statistics in the evolving world of business and ready to learn more? Start by exploring Michigan Tech’s master’s in statistics online.Learn More About Our Statistics Program

Sources:

• http://bulletin.imstat.org/2014/09/data-science-how-is-it-different-to-statistics%E2%80%89/
• https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
• http://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/
• http://stattrak.amstat.org/2018/07/01/dataanalystvsscientist/
• https://arxiv.org/abs/1801.00371

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Careers in Data Science and Applied Statistics

While data scientists and statisticians are often found working on the same teams and in the same fields, the two disciplines are distinct, and each provides exciting career opportunities.

Advanced education is a significant asset in both professions, as 50% of data scientists and statisticians in the U.S. have a master’s degree or higher qualification.

Data Science Careers

Data scientists work within many industries and fields, including computer systems design, management companies and enterprises, consulting services in management and scientific and technical fields, insurance, scientific research and development, and more. Another rapidly growing field of opportunity for data scientists is cloud computing, which gives small- and midsized organizations access to the benefits and trends of data science.

Data Scientist Job Outlook

Individuals with the proper training and skill set for data science work are still relatively rare and in high demand. A recent IBM survey found opportunities growing over 5% per year. The Bureau of Labor Statistics projects job growth of 15% by 2029 for computer and information research scientists. 

The BLS records a median annual wage of $98,230 for data scientists and mathematical science occupations. The highest ten percent of earners in the field earn over $165,000.

Applied Statistics Careers

Statisticians work in essentially every type of business and organization, including financial and insurance industries, marketing companies, consulting firms, medical and healthcare, politics, engineering, and professional and collegiate sports teams.

Statistician Job Outlook

Statistics careers are lauded for higher income levels, impressive job satisfaction rates and desirable work-life balance. The BLS projects a 35% growth in job opportunities for statisticians by the year 2029. US News and World Report, the magazine known for its investment in research and analyzing the best jobs in the country, ranks statistician as its #6 overall job, #2 best business job, and #5 best STEM job. 

The BLS reports a median annual wage of $92,270 for statisticians, while the upper end of the profession earns over $150,840 per year.

Learn more by reading this detailed blog about skills and career opportunities for those with advanced education in applied statistics.