Data Science Certification Exam

What Is Data Science?

Data science is an interdisciplinary field that use scientific methods, procedures, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, as well as to apply that knowledge and actionable insights to a variety of application areas. Data mining, machine learning, and big data are all connected to data science.

Data science is a "concept that unifies statistics, data analysis, informatics, and related approaches" in order to use data to "understand and analyze actual occurrences." Within the framework of mathematics, statistics, computer science, information science, and domain knowledge, it employs techniques and theories from a variety of domains. Data science, on the other hand, is distinct from computer science and information science. Data science, according to Turing Award recipient Jim Gray, is the "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven), and "everything about research is changing because of the impact of information technology" and the data flood.

Data science employs a wide range of technologies and procedures, which vary depending on the application. For data science and machine learning, full-featured, end-to-end platforms have recently been developed and are widely used. The following are a few of the most prevalent techniques:

• Linear regression
• Logistic regression
• For classification and data fitting, decision trees are employed as prediction models. Based on the observation attributes, the decision tree structure can be used to build rules that categorize or predict target/class/label variables.
• Cluster analysis is a technique for combining data into groups.
• Dimensionality reduction is a technique for reducing the complexity of data computations so that they can be completed faster.
• Machine learning is a task-solving technology that infers patterns from data.
• By applying the Bayes' theorem, naive Bayes classifiers are utilized to categorize. They're mainly employed in datasets with a lot of data, and they're good at producing reliable findings.

Salary Range of A Data Science Professional

Depending on the experience level and the demographic area, the salary of a Data Science Professional varies widely.

The following is the average Data Science Professional Salary in USA:

The following is the average Data Science Professional Salary in India:

What Is Data Science Certification?

Data Science Certification assesses a person's knowledge of the data science as well as their understanding of digital concepts. A variety of certifying authorities, ranging from government agencies to commercial enterprises and organisations, offer the Data Science certification. Certifications are normally obtained by the completion of an online or offline exam.

All certificates have their own set of benefits, such as international recognition, career opportunities, freelancing, and so on. So, Data Science certification is an online exam that evaluates a Professional's skills and knowledge in order to match them with the suitable opportunities.

Why should you take this Online Data Science Certification?

The online Data Science certification from Loopskill will assist you in becoming a certified Professional. You can take this exam and by scoring 70% you will become an internationally certified Data Science Professional. This certification will help you in three different ways:

• You can demonstrate your Data Science certification to potential employers and can stand out of the crowd.
• You can apply for great jobs using loopskill website or app; moreover, our partners companies will contact you directly for full-time or part-time opportunities depending on your skills & requirements.
• Loopskill is not just a platform to get certified or to find full time jobs; here being a certified Professional you can also do freelancing for the clients around the globe. You will be approached by the clients who need your help in building some web based platform or some app based platform.

The loopskill’s online Data Science certification is created to help people in exploring and achieving their full potential so they can get connected to the best opportunities around the globe.

Data Science Application

Data Science principles are explored in numerous disciplines, including the following:

Detection of Fraud and Risk

Finance was one of the first industries to use data science. Every year, businesses were fed up with bad loans and losses. They did, however, have a lot of data that was acquired during the first filing for loan approval. They decided to hire data scientists to help them recover from their losses.

Through genetics and genomics research, Healthcare Data Science applications also provide a higher level of therapy customisation. The goal is to discover specific biological linkages between genetics, illnesses, and treatment response in order to better understand the impact of DNA on our health. Data science tools enable the integration of various types of data with genomic data in illness research, allowing for a better understanding of genetic concerns in medication and disease reactions. We will have a better grasp of human DNA as soon as we have solid personal genome data. Advanced genetic risk prediction will be a significant step toward more personalized care.

Internet Lookup

When we think of search, we immediately think of Google. Right? However, there are other more search engines, such as Yahoo, Bing, Ask, AOL, and others. Data science techniques are used by all of these search engines (including Google) to offer the best result for our searched query in a matter of seconds. In light of the fact that Google processes over 20 petabytes of data per day.

System of Recommendations

Aren't we all used to Amazon's suggestions for similar products? They not only assist you in locating suitable products from the billions of products accessible, but they also enhance the user experience.

Recognition of images

You share a photograph on Facebook with your pals, and you start receiving suggestions to tag your friends. Face recognition method is used in this automatic tag recommendation function. Facebook's recent post details the extra progress they've achieved in this area, highlighting their improvements in image recognition accuracy and capacity.

Recognized Speech

Google Voice, Siri, Cortana, and other speech recognition products are some of the best examples. Even if you are unable to compose a message, your life will not come to a halt if you use the speech-recognition option. Simply say the message out loud, and it will be transformed to text. However, you will notice that voice recognition does not always work correctly.

Route planning for airlines

The airline industry has been known to suffer significant losses all over the world. Companies are fighting to retain their occupancy ratios and operational earnings, with the exception of a few aviation service providers. The issue has worsened due to the huge rise in air-fuel prices and the requirement to give significant discounts to clients. It wasn't long before airlines began to employ data science to find strategic areas for development. Airlines can now, thanks to data science, do the following:

Gaming

Gaming games are being created with machine learning algorithms that grow and enhance as the user progresses through the levels. In motion gaming, your opponent (computer) also studies your previous moves and adjusts its game accordingly. EA Sports, Zynga, Sony, Nintendo, and Activision-Blizzard have all used data science to take gaming to the next level.

Because a VR headset incorporates computer expertise, algorithms, and data to provide you with the optimal viewing experience, augmented reality data science and virtual reality do have a relationship. The popular game Pokemon GO is a little step in the right direction. The ability to wander around and look at Pokemon on walls, streets, and other non-existent objects. To pick the locations of the Pokemon and gyms, the game's makers used data from Ingress, the company's previous software. Pokemon GO is a game that you may play on your phone.

Important Topics to Learn & Master in Data Science

Introduction to Data Science

• Introduction to Big Data
• Roles played by a Data Scientist
• Analyzing Big Data using Hadoop and R
• Methodologies used for analysis
• The Architecture and Methodologies used to solve the Big Data problems

Basic Data Manipulation using R

• Understanding vectors in R
• Reading Data, Combining Data
• Subsetting data
• Sorting data and some basic data generation functions

Machine Learning Techniques Using R

• Machine Learning Overview
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning
• Techniques, Clustering
• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models

Machine Learning Techniques Using R

• Understanding K-Means Clustering
• Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
• Implementing Association rule mining in R

Machine Learning Techniques Using R

• Understanding Process flow of Supervised Learning Techniques
• Decision Tree Classifier
• How to build Decision trees
• Random Forest Classifier
• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Naive Bayes Classifier

Introduction to Hadoop Architecture

• Understanding K-Means Clustering
• Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
• Implementing Association rule mining in R

Integrating R with Hadoop

• Integrating R with Hadoop using RHadoop and RMR package
• Exploring RHIPE (R Hadoop Integrated Programming Environment)
• Writing MapReduce Jobs in R and executing them on Hadoop

Mahout Introduction and Algorithm Implementation

• Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout

Additional Mahout Algorithms and Parallel Processing using R

• Implementation of different Mahout algorithms
• Random Forest Classifier with parallel processing Library in R

Future of Data Science

Data Science has a wide range of applications that aren't confined to a single field. Its applications are used in a variety of industries. Let's have a look at a few of the most important upcoming advancements in Data Science:

Automobile Manufacturing: The automobile industry has seen a significant transformation in recent years and is still in the early stages of development. Self-driving cars, autonomous flying cars, fixed-destination cabs, automated public transportation, and a variety of other uses are among the possibilities. All of this is feasible in the near future. However, such advancements necessitate a big group of dedicated individuals who can not only write code but also consider the additional benefits that Data Science can provide that were previously unavailable.

IT: Most individuals conflate data science with information technology (IT) and related services. However, Data Science is the use of pure mathematical capabilities paired with the miracles of software engineering to create what we now refer to as Machine Learning. In terms of global GDP, the IT sector has seen tremendous expansion. However, when it comes to the IT industry as a whole, Data Science is quickly becoming a critical component of any successful data-driven business.

Healthcare: Healthcare is the most important application or miracle of Data Science. We may use large datasets of patients to construct a Data Science strategy to diagnose diseases at an early stage thanks to the availability of massive datasets of patients. Healthcare is one of the most promising industries for professionals who can combine their medical knowledge with Data Science to provide rapid assistance to individuals in need.

Weapons and the Army: Every nation has grown stronger as a result of increased army strength. It is said by a wise man that power should not be utilized to enslave humans. It must be used to liberate humanity from all dangers. Justifying the notion that Data Science can assist in the development of numerous automated methods to detect and halt attacks at an early stage.

Energy and Power: The need for energy has risen dramatically in tandem with the rapid growth of the human population. This necessitates the management of nuclear energy to the point where, without depleting current natural resources, we are able to meet energy demands. Data science can aid in the prediction of nuclear power's impacts.

Banking and financial services: We always think of the bank when we talk about the safety of our money. However, as online transactions became more common, fraud rose as well. Banking and financial data, as well as security, necessitate stable systems to detect fraud activities before they cause harm.

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