All You Need to Know About Skills Needed to Endorse a Career as DATA SCIENTIST!

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Data works as a new-age catalyst and is the reason why organizations function. In recent years, data has enjoyed prominence in every possible industry. There no doubt that a job in Data Science is a dream for many. But what is it that you need to do to land there?

This Blog typically talks about what Data Science is and the skills you need to be a Data Scientist. Keep Reading!

What is Data Science?

Data Science is a complex blend of various algorithms, tools, and machine learning principles. The aim is to discover hidden patterns from raw data. In other words, data science filters the data to extract information and draw meaningful insights from it. This takes into account both structured and unstructured data.

Data Science is deployed to make decisions and predictions using predictive causal analytics, prescriptive analytics, and machine learning.

What data scientists do?

Data scientists crack complex data problems through their expertise in specific scientific disciplines. They work with elements related to statistics, mathematics, computer science, etc. Data scientists use technology to find solutions and reach conclusions and suggestions for an organization’s growth and development. Data Scientists are the most necessary assets in new-age organizations.

What are the requirements to become a data analyst?

  • Programming Languages (R/SAS): Proficiency in one language and working knowledge of others is a must.
  • Creative and Analytical Thinking: A good analyst should be curious and creative. Having a critical and analytical approach is another attribute.
  • Strong and Effective Communication: Must be capable of clearly communicate findings.
  • Data Warehousing: Data analysts must know connecting databases from multiple sources to create a data warehouse and manage it.
  • SQL Databases: Data analysts must possess the knowledge to manage relational databases like SQL databases with other structured data.
  • Data Mining, Cleaning, and Munging: Data analysts must be proficient in using tools to gather unstructured data, clean and process it through programming.
  • Machine Learning: Machine learning skills for data analysts are incredibly valuable to possess.

How to become a data analyst?

If you wish to make a career in Data Science, here are the steps you must consider following:

Earn a bachelor’s degree in any discipline with an emphasis on statistical and analytical skills.

Learn essential data analytics skills (listed above).

Opt for Certification in data science courses.

Secure first entry-level data analyst job.

Earn a PG degree/Equivalent Program in data analytics.

Grow You Data Science Career with Imarticus Learning:

Imarticus offers some best data science courses in India, ideal for fresh graduates and professionals. If you plan to fast-track your Data Science career with guaranteed job interviews opportunities, Imarticus is the place you need to head for right away! 

Data Science course with placement in IndiaIndustry experts design the Certification programs in data science courses and PG programs to help you learn real-world data science applications from scratch and build robust models to generate valuable business insights and predictions.

The rigorous exercises, hands-on projects, boot camps, hackathon, and a personalized Capstone project, throughout the program curriculum prepare you to start a career in Data Analytics at A-list firms and start-ups.

The benefits of business analytics courses and data science programs at Imarticus:

  • 360-degree learning
  • Industry-Endorsed Curriculum
  • Experiential Learning
  • Tech-enabled learning
  • Learning Management System 
  • Lecture Recording
  • Career services
  • Assured Placements
  • Placement Support
  • Industry connects

Ready to commence a Transformative journey in the field of Data Science with Imarticus Learning? Send an inquiry now through the Live Chat Support System and request virtual guidance!

Complete Guide: Object Oriented Features of Java!

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What is OOPs?

OOPs, the concept brings this data and behavior in a single place called “class” and we can create any number of objects to represent the different states for each object.

Object-oriented programming training (OOPs) is a programming paradigm based on the concept of “objects” that contain data and methods. The primary purpose of object-oriented programming is to increase the flexibility and maintainability of programs.

Object-oriented programming brings together data and its behavior (methods) in a single location(object) makes it easier to understand how a program works. We will cover each and every feature of OOPs in detail so that you won’t face any difficultly understanding OOPs Concepts.

Object-Oriented Features in Java

  1. Classes
  2. Objects
  3. Data Abstraction
  4. Encapsulation
  5. Inheritance
  6. Polymorphism

What is Class?

The class represents a real-world entity that acts as a blueprint for all the objects.

We can create as many objects as we need using Class.

Example:
We create a class for “ Student ” entity as below

Student.java

Class Student{
String id;
int age;
String course;
void enroll(){
System.out.println(“Student enrolled”);
}
}

The above definition of the class contains 3 fields id, age, and course, and also it contains behavior or a method called “ enroll ”.

What is an Object?

Object-Oriented Programming System(OOPS) is designed based on the concept of “Object”. It contains both variables (used for holding the data) and methods(used for defining the behaviors).

We can create any number of objects using this class and all those objects will get the same fields and behavior.

Student s1 = new Student();

Now we have created 3 objects s1,s2, and s3 for the same class “ Student ”.We can create as many objects as required in the same way.

We can set the value for each field of an object as below,

s1.id=123;
s2.age=18;
s3.course=”computers”;

What is Abstraction?

Abstraction is a process where you show only “relevant” data and “hide” unnecessary details of an object from the user.

For example, when you log in to your bank account online, you enter your user_id and password and press login, what happens when you press login, how the input data sent to the server, how it gets verified is all abstracted away from you.

We can achieve “ abstraction ” in Java using 2 ways

1. Abstract class

2. Interface

1. Abstract Class

  • Abstract class in Java can be created using the “ abstract ” keyword.
  • If we make any class abstract then it can’t be instantiated which means we are not able to create the object of an abstract class.
  • Inside Abstract class, we can declare abstract methods as well as concrete methods.
  • So using abstract class, we can achieve 0 to 100 % abstraction.

Example:
Abstract class Phone{
void receive all();
Abstract void sendMessage();
}
Anyone who needs to access this functionality has to call the method using the Phone object pointing to its subclass.

2. Interface

  • The interface is used to achieve pure or complete abstraction.
  • We will have all the methods declared inside Interface as abstract only.
  • So, we call interface 100% abstraction.

Example:
We can define interface for Car functionality abstraction as below
Interface Car{
public void changeGear( int gearNumber);
public void applyBrakes();
}

Now, these functionalities like changing gear and applying brake are abstracted using this interface.

What is Encapsulation?

  • Encapsulation is the process of binding object state(fields) and behaviors(methods) together in a single entity called “Class”.
  • Since it wraps both fields and methods in a class, it will be secured from outside access.
  • We can restrict access to the members of a class using access modifiers such as private, protected, and public keywords.
  • When we create a class in Java, it means we are doing encapsulation.
  • Encapsulation helps us to achieve the re-usability of code without compromising security.

Example:
class EmployeeCount
{
private int numOfEmployees = 0;
public void setNoOfEmployees (int count)
{
numOfEmployees = count;
}
public double getNoOfEmployees ()
{
return numOfEmployees;
}
}
public class EncapsulationExample
{
public static void main(String args[])
{
EmployeeCount obj = new EmployeeCount ();
obj.setNoOfEmployees(5613);
System.out.println(“No Of Employees: “+(int)obj.getNoOfEmployees());
}
}

 What is the benefit of encapsulation in java programming
Well, at some point in time, if you want to change the implementation details of the class EmployeeCount, you can freely do so without affecting the classes that are using it. For more information learn,

Start Learning Java Programming

What is Inheritance?

  • One class inherits or acquires the properties of another class.
  • Inheritance provides the idea of reusability of code and each sub-class defines only those features that are unique to it ghostwriter diplomarbeit, the rest of the features can be inherited from the parent class.
  1. Inheritance is the process of defining a new class based on an existing class by extending its common data members and methods.
  2. It allows us to reuse code ghostwriter bachelorarbeit, it improves reusability in your java application.
  3. The parent class is called the base class or superclass. The child class that extends the base class is called the derived class or subclass or child class.

To inherit a class we use extends keyword. Here class A is child class and class B is parent class.

class A extends B
{
}

Types Of Inheritance:
Single Inheritance: refers to a child and parent class relationship where a class extends another class.

Multilevel inheritance:  a child and parent class relationship where a class extends the child class Ghostwriter. For example, class A extends class B and class B extends class C.

Hierarchical inheritance:  where more than one class extends the same class. For example, class B extends class A and class C extends class A.

What is Polymorphism?

  • It is the concept where an object behaves differently in different situations.
  • Since the object takes multiple forms ghostwriter agentur, it is called Polymorphism.
  • In java, we can achieve it using method overloading and method overriding.
  • There are 2 types of Polymorphism available in Java,

Method overloading

In this case, which method to call will be decided at the compile time itself based on the number or type of the parameters ghostwriter deutschland. Static/Compile Time polymorphism is an example of method overloading.

Method overriding

In this case, which method to call will be decided at the run time based on what object is actually pointed to by the reference variable.

Top Data Science Datasets Project Ideas for Beginners!

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What is Data Science?

Every company receives too much information about something at a moment which becomes tough to be processed at the same pace. Here is when Data Science comes into the picture.

Data Science is a field of study which deals with gathering massive information about a particular field from various sources and then converting that Big Data into a meaningful output. This data is combined with Machine learning and Artificial Intelligence which all together act as a base for scientific research to take place.

Data Scientists are hired to convert that Big Data into useful conclusions which further assists in lucid Decision Making.

With the advent of technology, everyone is pretty much connected which is the main reason how all the information related to a topic can be made available through the internet. A data science career can open the gate to multiple possibilities.

Data Science Course with Placement in IndiaData Science Datasets Project Ideas for Beginners.

According to a survey, it has been found that by the end of 2020, the demand for Data Scientists will increase by 28%. This is because of the current scenario where everything has shifted to online mode.

Data Scientists can lay their hands on various new topics and elements on the internet which can be the basis for their researches.

Some of the Data Science Projects that can help beginners to build a stronger resume are:

  1. Automated Chatbox Project

Considering the current situation, everything has become internet-based. Renowned companies are also switching to the Chat mode in their Customer Care Departments rather than taking up the calls. Chatting has become way more convenient than any other mode of communication. As far as formal or official communication is concerned, chatting sounds the best.

For a beginner, research on an Automated Chat Box can be really promising and fresh. There can be modifications in the classic chatting pattern in terms of official and formal chatting. For instance: When a company receives so many messages from their customers about certain queries, the automatic chatbox can answer some of the repetitive questions by itself.

This lessens the burden on the employees leading to a better focus on the queries rather than a formal salutation.

  1. Automated Caption Inserter Project

Talking about the current trend, where everyone wants to upload their pictures and photographs on Social Media Platforms, they want their captions to be suitable and trendy.

For a beginner who is aspirant of researching Data Science, this can be something new and likable.

When a picture alongside a river is posted on any Social Media Platform, this feature can give suggestions to the users regarding specific captions revolving around rivers or water bodies. This can save a lot of time and effort for the users leading to a great monopoly on the internet.

  1. Song Recommendation Project

Various music and song applications have been designed throughout the world. There can be research in the field of automated song recommendations to the users based on their current playlist or already downloaded songs on the application. This can be a  practical and helpful solution for users who are searching for songs that they may like.

Overview

Data Science, on the whole, is a massive field that can be explored with no limits and boundaries. One can keep carrying out amazing researches in several areas.

Investment Banking Courses with Placement in IndiaAll beginners must take up the Data Science Course if they wish to pursue a bright Data Science Career.

This is a field of study that is always going to be engaging and creative no matter how much work and research gets done.

Optimization In Data Science Using Multiprocessing and Multithreading!

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Every day there is a large chunk of data produced, transferred, stored, and processed. Data science programmers have to work on a huge amount of data sets.

This comes as a challenge for professionals in the data science career. To deal with this, these programmers need algorithm speed-enhancing techniques. There are various ways to increase the speed of the algorithm. Parallelization is one such technique that distributes the data across different CPUs to ease the burden and boost the speed.

Python optimizes this whole process through its two built-in libraries. These are known as Multiprocessing and Multithreading.

Multiprocessing – Multiprocessing, as the name suggests, is a system that has more than two processors. These CPUs help increase computational speed. Each of these CPUs is separate and works in parallel, meaning they do not share resources and memories.

Multithreading – The multithreading technique is made up of threads. These threads are multiple code segments of a single process. These threads run in sequence with context to the process. In multithreading, the memory is shared between the different CPU cores.

Key differences between Multiprocessing and Multithreading

  1. Multiprocessing is about using multiple processors while multithreading is about using multiple code segments to solve the problem.
  2. Multiprocessing increases the computational speed of the system while multithreading produces computing threads.
  3. Multiprocessing is slow and specific to available resources while multithreading makes the uses the resources and time economically.
  4. Multiprocessing makes the system reliable while multithreading runs thread parallelly.
  5. Multiprocessing depends on the pickling objects to send to other processes, while multithreading does not use the pickling technique.

Advantages of Multiprocessing

  1. It gets a large amount of work done in less time.
  2. It uses the power of multiple CPU cores.
  3. It helps remove GIL limitations.
  4. Its code is pretty direct and clear.
  5. It saves money compared to a single processor system.
  6. It produces high-speed results while processing a huge volume of data.
  7. It avoids synchronization when memory is not shared.

Advantages of Multithreading

  1. It provides easy access to the memory state of a different context.
  2. Its threads share the same address.
  3. It has a low cost of communication.
  4. It helps make responsive UIs.
  5. It is faster than multiprocessing for task initiating and switching.
  6. It takes less time to create another thread in the same process.
  7. Its threads have low memory footprints and are lightweight.

Optimization in Data Science

Using the Python program with a traditional approach can consume a lot of time to solve a problem. Multiprocessing and multithreading techniques optimize the process by reducing the training time of big data sets. In a data science course, you can do a practical experiment with the normal approach as well as with the multiprocessing and multithreading approach.

Data Science Courses with placement in IndiaThe difference between these techniques can be calculated by running a simple task on Python. For instance, if a task takes 18.01 secs using the traditional approach in Python, the computational time reduces to 10.04 secs using the pool technique. The multithreading process can reduce the time taken to mere 0.013 secs. Both multiprocessing and multithreading have great computational speed.

The parallelism techniques have a lot of benefits as they address the problems efficiently within very little time. This makes them way more important than the usual traditional solutions. The trend of multiprocessing and multithreading is rising. And keeping in mind the advantages they come up with, it looks like they will continue to remain popular in the data science field for a long time.

Related Article:

https://imarticus.org/what-is-the-difference-between-data-science-and-data-analytics-blog/

Top R programming, SQL and Tableau Interview Questions & Answers!

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Whether you are a fresher or an experienced data professional looking for better opportunities, attending an interview is inevitably the first step towards your dream career. Many of you might already have done a sneak peek into the world of data analytics through self-taught skills.

Data Science Course with Placement in IndiaHaving a good grip on the subject matter will give you an edge over other candidates. Data Science Courses and certifications add more weightage to your profile.

Interviewers might ask situation-based questions to test your knowledge and crisis management skills. So, make sure that you answer these questions wisely and showcase your knowledge wherever possible, without going overboard.

Listed below are some important R programming, SQL, and Tableau interview questions and answers. Check them out!

R Programming Interview Questions

A handy programming language used in data science, R finds application in various use cases from statistical analysis to predictive modeling, data visualization, and data manipulation. Many big names such as Facebook, Twitter, and Google use R to process the huge amount of data they collect.

  1. Which are the R packages used for data imputation?

Answer: Missing data could be a challenging problem to deal with. In such cases, you can impute the lost values with plausible values. imputeR, Amelia, Hmisc, missForest, MICE, and Mi are the data imputation packages used by R.

  1. Define clustering? Explain how hierarchical clustering is different from K-means clustering?

Cluster, just like the literal meaning of the word, is a group of similar objects. During the process, the abstract objects are classified into ‘classes’ based on their similarities. The center of a cluster is called a centroid, which could be either a real location or an imaginary one. K denotes the number of centroids needed in a data set.

While performing data mining, k selects random centroids and then optimizes the positions through iterative calculations. The optimization process stops when the desired number of repetitive calculations have been taken place or when the centroids stabilize after successful clustering.

The hierarchical clustering starts by considering every single observation in the data as a cluster. Then it works to discover two closely placed clusters and merges them. This process continues until all the clusters merge to form just a single cluster. Eventually, it gives a dendrogram that denotes the hierarchical connection between the clusters.

SQL Interview Questions

SQL online Training

If you have completed your SQL training, the following questions would give you a taste of the technical questions you may face during the interview.

  1. Point out the difference between MySQL and SQL?

Answer: Standard Query Language (SQL) is an English-based query language, while MySQL is used for database management.

  1. What is DBMS and How many types of DBMS are there?

Answer: DBMS or the Database Management System is a software set that interacts with the user and the database to analyze the available data. Thus, it allows the user to access the data presented in different forms – image, string, or numbers – modify them, retrieve them and even delete them.

There are two types of DBMS:

  • Relational: The data placed in some relations (tables).
  • Non-Relational: Random data that are not placed in any kind of relations or attributes.

 Tableau Interview Questions

Tableau is becoming popular among the leading business houses. If you have just completed your Tableau training, then the interview questions listed below could be good examples.

  1. Briefly explain Tableau.

Answer: Tableau is a business intelligence software that connects the user to the respective data. It also helps develop and visualize interactive dashboards and facilitates dashboard sharing.

  1. How is Tableau different from the traditional BI tools?

Answer: Traditional BI tools work on an old data architecture, which is supported by complex technologies. Additionally, they do not support in-memory, multi-core, and multi-thread computing. Tableau is fast and dynamic and is supported by advanced technology. It supports in-memory computing.

  1. What are Measures and Dimensions in Tableau?

Answer: ‘Measures’ denote the measurable values of data. These values are stores in specific tables and each dimension is associated with a specific key. This helps to associate one piece of data to multiple keys, allowing easy interpretation and organization of the data. For instance, the data related to sales can be linked to multiple keys such as customer, sales promotion, events, or a sold item.

Dimensions are the attributes that define the characteristics of data. For instance, a dimension table with a product key reference can be associated with different attributes such as product name, color, size, description, etc.

The questions given above are some examples to help you get a feel of the technical questions generally asked during the interviews. Keep them as a reference and prepare with more technically inclined questions.

Remember, your attitude and body language play an important role in making the right impression. So, prepare, and be confident. Most importantly, structure your answers in a way that they demonstrate your knowledge of the subject matter.

Related Article:

https://imarticus.org/20-latest-data-science-jobs-for-freshers/

Do You Know Data Science Professionals Been Hired The Most ?

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Data science courses have become increasingly popular in the past few years. That’s because the demand for data science professionals has risen substantially in various industries.

Companies in various sectors recognize the importance of big data and want to use it properly. In the following points, we’ll look at the sectors that hire the most data scientists:

Industries that hire the most data scientists

There are several industries involved in hiring data scientists:

Finance

The finance sector utilizes the expertise of data science professionals the most. It uses data science in determining the growth prospects of its investments, to calculate risk, perform predictive analysis and manage its operations.

Banks also rely on data science to detect and prevent credit card frauds. They use data science to track fraudulent behavior patterns in suspicious clients to identify potential credit card frauds.

When you join a data science course with placement, you’ll surely be working on finance-related projects.

Healthcare

Data scientists work in different avenues of the healthcare sector. Mostly, they work in the research aspect of healthcare and contribute to making trials and testing more efficient. Data science and artificial intelligence help companies in reducing errors and enhancing the efficiency of research processes.

Modern healthcare technologies also utilize the data science to provide better experiences to patients. Data science helps in improving the accuracy of diagnoses and delivers more precise prescriptions to patients.

Entertainment

OTT platforms have revolutionized the entertainment industry. Netflix, Amazon Prime, and Hotstar are now some of the biggest entertainment companies in the world. Netflix has been using data science since it launched its digital subscription service and has been a hot topic for case studies in data science courses in India. It relies on data science to attract more customers, create high-quality content and track its growth.

Data Science Course with Placement in IndiaHow to capitalize on this opportunity

As you can see, the demand for data scientists is constantly growing in multiple industries. Whether you want to enter the entertainment sector or the banking industry, becoming a data scientist will help you in your pursuit.

The best way to start your career in this field is by joining data science courses. While there are many data science courses in India, it’s vital to pick one that suits your requirements and aspirations. You should always check the data science course details, including the data science course fees to ensure they match your criteria.

Currently, it would be best to pick an online data science course in India because it would teach you all the required concepts and skills digitally.

Enrolling in a data science course in India would not only teach you the necessary skills, but it will also make you eligible for pursuing data science roles in various companies.

You can also look for a data science course with placement. It would help you kick-start your career as a data scientist easily and quickly.

Conclusion

Now, you have learned how data science helps numerous industries. You also found out how joining an online data science course in India can help you capitalize on this demand and become a sought-after professional.

Do check out our data science course details such as the data science course fees, if you’re interested in a career in this field.

Data Scientists Are in Great Demand And Are At The forefront of The AI revolution.

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Data scientists are in great demand due to the value they offer to Artificial Intelligence and Machine Learning. With the advent of automation and the increased focus on Artificial Intelligence, organizations and corporations are looking for skilled human assets who have expertise in this field.

In this article, we will cover how a Prodegree in Data Science from Imarticus can help a budding data scientist advance further in the field of AI and Machine Learning. Budding data scientists can utilize an extensive data science course like Prodegree by Imarticus to gain the necessary skills and knowledge that is needed to work with valuable projects and organizations.

Data Science CoursesWhat is Data Science?

Data science is a specialized field of computing and working with data, which promotes data-centric or data-backed business and IT solutions. Data science consists of fundamental methods, tools, and algorithms that use data analytics, data mining, sourcing data, creation of models to work with data, and the execution of IT processes or data models to provide business solutions or attain insights.

Data Science also powers analytical methods which allow individuals to use business analytics and predictive analytics to come to resolutions from the generated insights. Data scientists are also responsible for the process of importing data from various sources and cleaning the data to allow this data free of noise to be used in various applications.

What is Artificial Intelligence?

Artificial Intelligence is the ability of machines or systems, which allow them to take actions based on historical data and through learning on their own without any interference from humans. Artificial Intelligence uses Data Science and Machine Learning to create complex systems, which emulate how human intelligence works and responds to scenarios. Artificial Intelligence promotes automation and supports the idea of machines doing the work autonomously without any human intervention or biases.

This empowers a lot of platforms, machines, and services to provide automated services that save money for companies and allows us to give less effort by relying on rapid and efficient action taken by AI. 

For instance, AI is helping industries and factories by automating a lot of production and helping operations with AI-assisted analytics and suggestions. AI is highly appreciated even in the fields of marketing, advertising, finance, and business by making predictions to support companies in making data-backed business decisions. 

What is a Prodegree from Imarticus?

The AI and Machine Learning centric Data science Prodegree is designed by experts from this field to help future data scientists learn important data science concepts like Machine Learning and data mining, or algorithms and tools to assist in the process of building efficient models to gain valuable business insights and predictions backed by data.

The Data Science courses also offer various modules on business analytics and predictive analytics to provide analytical expertise to students. This kind of a planned data science course encourages individuals to get into this highly valuable field and learn the fundamentals required to build a great future centered on data science and AI.

A Prodegree from Imarticus helps budding developers and data scientists bag valuable job roles offered by reputed organizations like KPMG, Genpact, Infosys, and TCS.  

Data Science Certification CoursesThis course contains real projects, which will allow students to gain hands-on experience to tackle IT challenges and business problems. With this kind of well-planned course and study modules, one can truly get ahead in his or her career and discover new prospects. 

Conclusion

Working with AI is fun and interesting as well, and Imarticus is a great learning hub that promotes advanced data science and involves enrolled students in real-world AI projects. This further contributes to their skill development and exposure to this highly interesting field. AI has huge potential and a great future ahead, and this well-orchestrated course can certainly help in building your career in this field. 

Here Are Some Data Science Careers Which Are Enhancing Our Future!

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With the increasing reliance on data science by major corporations and the biggest brands, Data science is the prime focus of this decade. Data science is making our future better by enhancing our life in various ways and through various services.

There are different careers like data analysts, business analysts, and data scientists that one can pursue to contribute to this truly interesting field which has a huge effect on our lives and will affect our future in the years to come. Similarly, data science promotes and supports IT and the efficiency or effectiveness of businesses.

In this article, we will cover how data science is enhancing our future and the various respected careers with job roles that are highly in need of being filled. 

How Data Science is improving our future

There are a variety of ways that data science is improving our future; ranging from its applications in medical science to rapid accurate resolutions to troubled customers, data science is responsible for making our lives faster, safer, and smoother in general. For instance, data science is helping the health industry by allowing patients to be treated more effectively by analyzing historic data of patients.

It is also helping medical science by empowering chemical synthesization and simulating the effects of medication on affected individuals or allergies. Data science is increasing safety and cutting risk for us as well with applications in automated braking systems, AI in navigation, and automated cars, warning about industrial risks or any issue with the structural integrity of physical or digital units.

Data science makes our lives smoother by providing assistance in machine learning of customer care or service platforms which in turn give us rapid and precise resolutions. Data science powers the recommendation engines during shopping, social media and search or media recommendations by learning our behavior and global trends and then using AI to provide us suggestions.

Highly regarded Data Science careers valued by companies and the beneficiaries 

A data science course helps individuals acquire the necessary skills to contribute to this highly reputed and valuable field that works with data. 

Data Science JobsData Scientist – A data scientist helps while sourcing the data and then processing the data. Data scientists are experts in data mining and are responsible for removing the noise from the data, handling the data, modeling the data, and storing the data.

 

  • Data Analyst – Data analysts also engage in data mining, data cleaning, and then working on the data with various tools. Data analysts then analyze the data and then use predictive analytics to gain insights from the data with various tools and simulations with the help of the acquired data.
  • Data Engineer – Data engineers work with scripts for injecting data from various sources, they are involved in the modification of data, creation of data models and they work on the data with various programming languages. They also troubleshoot data problems and assist IT or software development projects.2. Business Analyst – Business analytics is highly used by organizations to gain insights from data, and then with their help, companies make business decisions based on the visual or graphical representations and predictive analytics which is backed by data. Business analytics helps businesses a lot by helping them make the right decisions which helps them cut costs or maximize profit while minimizing risk. 

 

 

  • Marketing Analyst – A marketing analyst uses analytics to find our market patterns and the user or target behavior to help companies accurately target ads and marketing promotions. Marketing analysts depend on data to figure out trends and target the relevant audiences. 

 

It is due to data science that we are able to enjoy the various forms of technology and automated or AI-powered services that are backed and powered by data science. An expertly orchestrated data science course can help in acquiring various job roles that are in need to be delegated to human assets trained extensively in data science. 

Interesting Puzzles To Prepare For Data Science Interviews !

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A Data science career is a lucrative opportunity with many young professionals opting for it. With the easy accessibility to data science courses, the number of professionals pursuing it is rising. There is a huge demand for expertise in this area and it has been voted as the best career by Glassdoor in the United States.

Though there is a need for professionals in this field, it is often not easy to get into. Organizations look for problem-solving and analytical skills in their potential employees and judge them based on creative and logical reasoning ability.

Having a different approach towards a problem and solving it in a unique way can help one stand out from the crowd. It isn’t a cakewalk to master these abilities. One has to practice and try to improve their skills. Solving puzzles is a way to test the individual’s ability to think out of the ordinary and also puts to test problem-solving skills.

The interviewers while hiring fresher especially give them puzzles to solve during their interviews. Due to the pandemic, many companies now have a stricter policy when it comes to choosing the right candidate for the job. It is challenging and the chances of selection are less compared to earlier.

Data Science Career Interview

Some are even assessing the candidates based on their coding skills. To provide an insight into what is in store for the candidates, below mentioned are some of the commonly asked puzzles during a data science job interview.

  1. There are 4 boys A, B, C, and D who are supposed to cross a rope bridge. It is very dark and they have just one flashlight. It is difficult to cross the bridge without the flashlight and the rope bridge can only stand 2 people at once. The 4 boys take 1, 2, 5, and 8 minutes each. What is the minimum time required for the four boys to cross the rope bridge? 

Sol:

This is a question that is most repeated and has an easy solution. A and B are the fastest boys and can cross the rope bridge first. They take 2 minutes. B stands on one side and A returns with the flashlight in 1 minute. So the total time taken is 3 minutes. After that, C and D have to cross the rope bridge. They have taken 5 and 8 minutes each. The total time taken is 8 minutes.

When we add the time taken by all, it is 3+8 which equals 11 minutes. C and D stand on the other side and B takes 2 minutes to return. Hence the total time that is taken by all is 11+2 which equals 13 minutes. At last, A and B will cross the rope bridge and will take 2 minutes and that adds the total time to 13+2 which is 15 minutes. So the time required by all the 4 to cross is 15 minutes.

  1. A person is in a room with the lights turned off. There is a table. A total of 50 coins have been kept on the table. Out of the 50, 10 coins are in the head position while the other 40 are in the tails position. The person has to segregate the coins into 2 different sets in a way that both sets have equal numbers of coins that are in the tails position.

Sol:

Segregate the coins into two groups, one with 10 coins and the other with 40 coins. Turnover the coins of the group that has 10 coins

  1. A bike has 2 tyres and a spare one. Each tyre can only cover a distance of 5 kilometers. What is the maximum distance the scooter will complete? 

Sol: 

To simplify the problem, we will name the tyres X, Y and Z respectively. 

X runs 5 kms

Runs 5 kms

Z runs 5 kms

Initially, the bike can cover a distance of 2.5 kms with tyres X and Y

X=2.5 kms, Y=2.5 km, and Z=5 kms

Take off tyre X and ride the bike with YZ another 2.5 kms

Remaining X= 2.5, Y=0 and Z=2.5

Take off tyre Y and ride the bike with XZ another 2.5 kms

Remaining X=0, Y=0 and Z=0.

Hence, the total distance covered by the bike is 2.5+2.5+2.5 = 7.5 kms

The more an individual practices such puzzles, the better the chances of landing a data science job.

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10 Data Science Careers That Are Shaping the Future!

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Data is wealth in modern days and data scientists will be in huge demand in the coming years. Firms require skilled professionals to analyze the generated data. Data analysis is also predicted to surge with the rise of new-age technologies like machine learning, artificial intelligence, etc.

According to reports, there is a shortage of expert data scientists in the market. One can opt for a post-graduate program in machine learning to gain the skills needed in the data science industry.

Let us see about ten data science careers that are shaping the future.

Data Scientist

Data Scientists have to organize the raw data and then analyze it to create better business strategies. Data is analyzed for predicting trends, forecasting, etc.

Data science careerData scientists are technical personals who are fluent in data analysis software and use them to predict market patterns. Firms will require more skilled data scientists in the future due to the need to process & analyze big data.

Business Intelligence Analyst

Business Intelligence (BI) analysts & developers are required to create better business models. They also help in making better business decisions. Policy formation and strategy development are key responsibilities of a BI analyst. Firms have to face market disruptions and need good business models/strategies to tackle them. BI analyst/developer will be in demand in the coming days.

Machine learning Engineer

Machine Learning (ML) Engineers are required for creating better data analysis algorithms. They have research about new data approaches that can be used in adaptive systems. ML engineers often use other technologies like deep learning, artificial intelligence, etc. to create automation in data analysis.

Applications Architect

Firms require good applications and user interfaces to run business processes smoothly. Applications architects choose or create the right application for their firms. Due to the rise in the complexity of data, firms will require better applications to manage it.

Statistics Analyst

A Statistics analyst or statistician is required to interpret the data and present it in an understandable way to non-technicians. They have to highlight the key insights in big data to stakeholders/fellow employees. Data analysis results are also used to make predictions and identify potential opportunities. You need to be good with numerology if you are thinking to become a statistician.

Data Analyst

They have to convert large data sets into a suitable format for data analysis. They also help in finding the data outliers which can affect the business. There is a lot of data generated every day as humans analyze less than 0.5 percent of data produced! Data analysts are already in huge demand in the data science industry.

Infrastructure Architect

Infrastructure architect in a firm makes sure that the applications, software(s), databases used by the firm are efficient. Infrastructure architects also help in cost optimization. They make sure that their firm has the necessary tools for analyzing big data.

Data Architect

Data architects mainly focus on maintaining databases.

Data Science CareerThey attempt to make the database framework better. With the rise of automation in data science, data architects are in huge demand to provide better solutions.

Enterprise Architect

Enterprise architects are IT experts and provide firms with better IT architecture models. They suggest stakeholders & senior managers in choosing the right IT applications for data analysis. Top companies like Microsoft, Cisco, etc. hire enterprise architects for maintaining their IT framework.

Data Engineer

Data engineers are required to create a good data ecosystem for their firms where the data pipelines are maintained. Data Engineers are required to choose better data analysis applications to provide real-time processing. They also help in making the data available to data scientists.

Conclusion

Data science is a growing field and there are a lot of job opportunities. You can learn Data Science Courses in India from a reliable source like Imarticus learning. One can also target any particular job role in the data science industry and should learn the necessary skills. Start your post-graduate program in machine learning now!