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

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.

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

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Things To Know About the Reinforcement Learning with MATLAB!

With the advent of technology, human has started to function by largely depending on machines and technology. To make things far easier, automated technology has been introduced in various aspects of life. Reinforcement learning is also a segment of machine learning that is based on the premise of automation.

What is Reinforcement Learning with MATLAB

Reinforcement learning is a kind of machine learning which enables a computer to function on its own by interacting with the dynamic environment repeatedly. The main aim of this approach is to reduce human intervention in machine learning and automation as much as possible so that a state of one hundred percent automatic technology can be attained.

Under reinforcement learning, the computer is not properly coded or programmed to perform the tasks but is made to act accordingly with the trial-and-error method. The environment or the outside conditions are made dynamic for the computer to explore as much as possible.

Applications like MATLAB enable this kind of function to run smoothly by providing schematic and organized results and outcomes. MATLAB is a professional tool that is fully documented and properly tested for carrying out functions like these.

With the help of MATLAB, reinforcement learning is done to get the best outcome suitable for a particular outside condition. All these functions are undertaken by a piece of software called the agent. The agent interacts with the outside conditions to produce various outcomes.

Understanding the Reinforcement Learning Workflow

To train the agent or a computer, the following steps are deployed:

  1. Creating the environment

The first step is to provide a suitable environment for the agent. The environment can be either a real-life condition or a simulation model. For technical and machine-based reinforcement learning, having a simulation model is preferred for smooth and safe functioning.

  1. Setting up a reward

A specific reward in the form of a numeric number has to be set up so that the agent can function accordingly. A reward is sometimes achieved by the agent after constant trials. Once the reward has been met, the optimal way to achieve the reward can also be found.

  1. Creating the agent

The agent for reinforcement learning can be created either by defining the policy representation or through the configuration of the learning algorithm of the agent.

  1. Training and validating the agent

For this, all the training options for the agent are set and training is started for the agent to tune the policy. In case an ideal validation of the agent has to be done, simulation turns out to be the option.

  1. Deployment of the policy

In the end, the policy representation is deployed using coding languages like MATLAB, etc.

A real-life example of reinforcement learning with MATLAB

Automated driving is the best example of machine learning, outcomes of which can be the result of reinforcement learning. The agent in the car uses various sensors to drive the car automatically without any human intervention. These sensors and video cameras give commands to the steering, gears, clutch, and brakes to take suitable action.

After a rigorous session of trial-and-error of various outcomes, the best way to automatically drive a car can be known. Reinforcement learning uses almost the same sort of applications while parking or reversing the car.

A shortcoming of reinforcement learning

Apart from its various benefits, reinforcement learning takes a lot of time and tries to achieve the optimal outcome.

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Activation Functions in Neural Networks: An Overview!

The formation of neural networks is similar to the neurons of our brain. Here, the product inputs of say X1 and X2 with weights say W1 and W2, are added with bias or “b” and acted upon an activation function of ‘f’ to get the result as “y”.

The actuation work is the main factor in neural network training, which chose whether or not a neuron will act and move to the following layer. This implies that it will determine whether the neuron’s contribution to the network is pertinent or not during the prediction process.

It is also why the activation work is called the transformation or threshold for all the neurons that result in network convergence.

The activation work is also useful for normalizing output ranges such as -1 to 1 or 0 to 1. Moreover, it is helpful during backpropagation. Now, the most important reason for such utility is the fact that neurons possess some differential property.

Besides, during the journey of backpropagation, there is an update of the loss function takes place. Moreover, the activation function leads to the gradient descent arches and curves to reach what we call their local minima.

Further, in the post, you will better understand the activation functions that are part of a whole neural network.

What are the different types of activation functions?

Here is a compact list of the vast varieties of activation functions that form part of a neural network.

Linear function

Let us start with the most fundamental function that defines being proportional to a particular unit. If you consider the equation, Y= az, you will realize its similarity with a typical equation of the straight line. Moreover, you get an activation range that starts from -inf and ends at +inf. Therefore, a linear function is the most suitable when you are solving a regression problem. For example, calculation or prediction of housing prices is a regression problem.

ReLU

The Rectified Linear Unit or ReLU is the most popular among all other activation functions. Besides, you will only find this function under the deeper layers of any learning model. Moreover, the formula, in this case, is straightforward. If an input implies a positive figure, then the same value comes back as ‘0.’ Therefore, the derivative concept here is straightforward.

ELU

An ELU or Exponential Linear Unit helps in overcoming a dying ReLU problem. Everything in this function is almost the same as a ReLU apart from the negative value concept. Here, the process gets back to the exact value if it is positive, or else the result is alpha(exp(x)-1). In this equation, for positive value, ‘alpha’ and ‘1’ is the constant and derivative, respectively. Moreover, the equation focus is ‘0’.

LeakyReLU

However, a little different from ReLU, the LeakyReLU function gives out the same output against positive inputs. In the case of different values, a fixed 0.01 is the output. The LeakyReLU function is mainly important when you want to solve any dying ReLU equation.

PReLU

Parameterized Rectified Linear Unit or PReLU is another variety of ReLU and LeakyReLU and negative qualities registered as an alpha*input. Unlike a Leaky ReLU here, the alpha is not is 0.01. So, in this case, the PReLU alpha value will come out through backpropagation.

Sigmoid

Sigmoid is one of the non-linear actuation functions. Otherwise called the Logistic work, it is constant and monotonic. Moreover, the yield is standardized in the reach 0 to 1. Also, it is highly differentiable and results in a gradient blend. Sigmoid is generally utilized before the result layers in binary order.

Tanh

A Hyperbolic tangent activation or Tanh value goes from – 1 and ends at 1. Moreover, the subordinate qualities range between 0 and 1. Besides, a tanh function is zero driven. Furthermore, it performs in a way that is better than the sigmoid function. Ultimately, they are utilized in binary order for hidden layers.

Softmax

This activation work returns possibilities of the contributions as the results. The possibilities will be utilized to discover the objective class. The Last consequence will be only the one with the most elevated probability.

Swish

It is a sort of ReLU work. It is one of the self-ground works where the only requirement is the info. Moreover, there are no extra parameters in this case. Equation y = x * sigmoid(x) is generally utilized in LSTMs. A Swish function is Zero driven and takes care of a dead activation issue.

Softplus

It is next to impossible to find 0’s derivative using numeric functions. Most of the neuronic functions have fizzled eventually because of this issue. The softplus actuation work is the only solution in this case. the formula of y = ln(1 + exp(x)) is like ReLU. However, this function is easier and goes from 0 and ends in infinity.

Here is a list of all the general activation functions that form a part of a complete neural network process.

Complete Guide: Object Oriented Features of Java!

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.

The Impacts of Robots in Regular Life!

Robots are used in many areas of our life, including 10 possible uses of robots in our daily life: Automated transport (autonomous robot) Automated transport (autonomous robot) The first major spread of visibility The use of mobile robots is seen through autonomous cars.

The advances in the development of automated autonomous vehicles over the past 10 or 15 years have been astonishing. New cars without robotics are like computers on wheels. But with robotics, they’re more efficient and dangerous. Autonomous robots are not robots that can drive cars. What it really means is that cars are built like robots and artificial intelligence is fed into those cars.

In a modern world like many countries in Europe and America, the automated autonomous vehicle is available like buses, trams and trains are automated, but vehicles like cars that circulate on the streets are not very general, but recently Audi, Mercedes, Google, they are presenting autonomous cars. The day is not far off when human drivers are not needed to drive vehicles.

As a result, accidents may not happen as many as there are today. Security, Defense and SurveillanceSecurity, Defense and Surveillance The work of security, defense, and surveillance robot is normal: it inspects the desired area. Immediately notify the owner if there has been any kind of malfunction. This type of robot is used in the military. This type of robot can also be used in everyday human life.

In the army, this type of robot does different types of work. . They are used to arm and defuse bombs. You will be sent to the desired area to monitor enemy activity, which is definitely a dangerous job for soldiers.

Robotics trainingFor use in everyday human life, this type of robot monitors your house. This robotics training helps people to monitor the sky, the ground, and the water from a remote location.

You can control this kind of robot from another location to send them to the desired location to monitor the activities of that location. Your home and property will not be damaged if you are not around to monitor them.

Cooking with Robots Cooking with Robots After spending a full day at the office, it becomes annoying to motivate yourself at home to cook a delicious meal for yourself.

The abbreviation is sometimes not healthy and tasty enough. But what if you had a robotic kitchen assistant to help you cook the food to your liking? There are many programmable robots that can prepare the food of your choice. All you have to do is set the number of food ingredients. The robot does the rest. A lot of robots are now being introduced that you can copy. You only need to cook in front of the robot once.

The movement of your body is registered by the camera, from then on the robot will copy your actions to prepare this meal for you, this type of robot kitchen helpers are being introduced in many hotels and homes, some companies are making these types of robots, including, Moley Robotics, Shadow Robot companies are quite famous.MedicalMedicineThe influence of robotics is undeniable in the medical field. Recently, engineers have successfully discovered surgical robots.

This success has resulted in a large financial investment in robots in medicine. Recently, Google and Johnson & Johnson worked together to develop a next-generation medical robotic system. While robots were only used as assistants in the clinical system in the recent past, they are now being introduced as an integral part of the clinical system.

Although not yet possible, it is not far from replacing the surgeon with robots in operations. The robotic system has established itself in clinics around the world. Therefore, engineers work hard to successfully invent micro and nanorobots.

Doing things that require precise and accurate performance in a way that a human cannot. For the drug delivery system, these robots can concentrate the therapeutic payload locally around the pathological sites so that they can reduce the dose of the drug delivery and the side effects they cause.

Education roboticsEducation Robotics is now known as an all-purpose technology. This means that it has the potential to change societies through its effects on economic and social structures.

So it is now natural to start discussing robotics in education. Many students suffer from different types of illnesses on a daily basis.

Therefore, they cannot physically attend classes. Because of this, the lessons are lost. Engineers have developed robots that can help students attend their classes remotely. The robot acts as a person in the classroom that is controlled by the person himself. His cameras are his eyes and the body is used for interaction.

Complete Overview on – Computer Science And Engineering(CSE) Projects!

Computer science is a branch of engineering that deals with the logical investigation of computers and their use like calculation, information preparing, frameworks control, advanced algorithmic properties, and man-made reasoning.

The skills of computer science incorporate programming, outline, examination, and hypothesis. Computer science engineering includes outlining and advancement of different application-based programming. Computer science venture points can be executed by various instruments, for example, C, C++, Java, Python, .NET, Oracle, and so on.

Mini Projects

A mini project is a bit of code that can be produced by a group or a person. Small-scale projects are utilized as a part of the Student field. A mini project is a source code with enhanced capacities it can even be taken as the last year venture.

Computer vision coursesLast year Mini undertakings, which they may need to make as a part of their instructive educational programs. These projects can be created in JAVA, VB .NET, ASP .NET, C, C++, PHP, C#, JSP, J2EE, ASPCloud Computing Networking, Big Data, Data Mining and that’s just the beginning.

 

You can get online courses at Imarticus with guaranteed internships over different languages C, C++, Java, Python, etc..

Topics

The topics for mini Projects in Computer Science and Engineering are as follows:

 

IEEE Java Mini Projects

Java is the world’s most popular language and it controls billions of gadgets and frameworks around the world. An assortment of recommended understudy term ventures is including java. Here are some IEEE java venture lists utilizing the most recent methods.

Most recent Java points, Latest java Concepts, Java venture focuses with astounding Training and improvement, Latest J2EE Projects with ongoing Technology. Here is a rundown of undertaking thoughts for Software ideas. Some of the project ideas involving the concepts of java are as follows:

  • Classroom scheduling service for smart class
  • Privacy-preserving location proximity for mobile apps
  • Mobile attendance using near-field communication.
  • LPG booking online system by smartphone

Projects on Cloud Computing

Cloud computing is the conveyance of on-request figuring assets over the internet, huge development in the recent software technologies which is associated with the remote servers through a systems administration connection between the customer and the server.

The information can be uploaded and it can be anchored by giving diverse sorts of security. Systems for securing information respectability, message validation codes (MACs), and advanced marks, require clients confirmation to download the majority of the records from the cloud server, We have the best in the class foundation, lab set up, Training offices, And experienced innovative workgroup for both instructive and corporate areas. The project topics for cloud computing are as follows:

  • An efficient privacy-preserving ranked keyword search method.
  • Vehicular Cloud data collection for Intelligent transportation system.
  • A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data.
  • Live data analysis with Cloud processing in wireless Iot networks.

Projects on Big data/Hadoop

Big Data is having a huge development in the application industry and in addition to the development of Real-time applications and advances, Big Data can be utilized with programmed and self-loader from numerous points of view, for example, for gigantic information with the Encryption and decoding Techniques and executing the charges.

Big Data examination has been an exceptionally hot dynamic amid recent years and holds the potential up ’til now to a great extent undiscovered to enable chiefs to track improvement advance. Most recent Big Data themes, Latest Big Data Concepts regions take after:

  • An online social network based Question Answer System using Big data
  • Efficient processing of skyline queries using Big data
  • User-Centric similarity search
  • Secure Big data storage and sharing scheme for cloud tenants.

Don’t miss reading Software Every Engineer Should Know About.

Projects in Networking

Networking works with all the directing conventions, for example, exchanging the information from a place to another which takes the assistance of numerous conditions like filaments and so on, Adhoc systems are utilized for exchanging information from a portable system to a web application. Some of the networking based projects are:

  • Cost minimization algorithms for data center management
  • Detecting malicious Facebook applications
  • Software-defined networking system for secure vehicular clouds

Data Mining Projects

Data mining is the mining of information from data, Involving techniques at the crossing point of machine learning, insights, and database frameworks. It’s the intense new innovation with awesome potential to enable organizations to center around the most critical data in their information stockroom.

We have the best-in-class foundation, lab set up, Training offices, and experienced innovative workgroups for both instructive and corporate parts. The projects topics on data mining are as follows:

●Link Analysis links between individuals rather than characterizing the whole
●Predictive Modelling (supervised learning) use observations to learn to predict
●Database Segmentation (unsupervised learning) partition data into similar groups

Learn Cloud Computing, Big Data, Data Mining, and many other courses at Imarticus with guaranteed internships.

Some more computer science-based project topics are:

  1. Data  Warehousing and Data Mining Dictionary
  2. Fuzzy Keyword Search in Cloud Computing over Encrypted Data
  3. Web-Based Online Blood Donation System
  4. Web-Based Graphical Password Authentication System
  5. Identification and Matching of Robust-Face Name Graph for Movie Character
  6. Controlling of Topology in Ad hoc Networks by Using Cooperative Communications
  7. An SSL Back End Forwarding Scheme of Clusters Based On Web Servers
  8. Motion Extraction Techniques Based Identifying the Level of Perception Power from Video
  9. Approximate and Efficient Processing of Query in Peer-to-Peer Networks
  10. Web-Based Bus Ticket Reservation System

15 Reliable Sources to Master Data Science

15 Reliable Sources to Master Data Science

Data Science is growing at a rapid pace and businesses have been dynamically benefitting from this. A lot of Data Science Courses are available at the Imarticus Learning Data Science Training Center. No doubt, the insights and knowledge of data science have helped business emerge a winner with better knowledge and insights available at their fingertips. Have a look at these 15 important blog resources with the highest number of followers if you are willing to understand and learn data science. These blogs have rich data science resources and won’t let you miss you anything in the world of data science.

  1. Reddit – It’s an American social news aggregation, web content rating and discussion website for everyone who loves to share content and satisfy their curiosity. The registered members at Reddit can submit content such as text posts or direct links and get opinions on the same. It’s a hugely popular website where everyone can participate because it’s simple and easy.

FrequencyAbout 84 posts per week

Facebook Fans: 1,108,745

Twitter Followers: 511K

2. Google News – Comprehensive and most dynamic up-to-date news coverage, aggregated from all over the world by google news. It’s a popular medium throughout the world since Google has become a most reliable name everywhere. It’s a reliable source of Data Science information where everything related to it will be at your fingertips.

FrequencyAbout 21 posts per week

Facebook Fans: n/a

Twitter Followers: 214K

3. Data Science Central – Now this is a platform where every kind of information is available in one place. It wouldn’t be wrong if we say that it’s the industry’s online resource for big data practitioners. And it’s damn popular among the practitioners. From analytics to data integration to visualisation, data science centre provides a community experience.

FrequencyAbout 24 posts per week

Facebook Fans: 1,013

Twitter Followers: 100K

4. KDnuggets I Data Science, Business Analytics, Big Data and Data Mining – Now, if you are looking for the most interesting and updated blogs on day to day evolution of the Big Data, then this is the place to be. Here, one can find the most interesting stuff on analytics, big data, data science, data mining and machine learning, not necessarily in that order.

FrequencyAbout 34 posts per week

Facebook Fans: 21,860

Twitter Followers: 96K

  • Kaggle I Data Science News – No Free Hunch – A competitive platform where companies and researchers post data while statisticians and data miners compete with each other to produce the best models for predicting and describing the data. It’s a popular platform where professionals compete with each other to come up with the best ideas that they have.

FrequencyAbout one post per month

Facebook Fans: 35,137

Twitter Followers: 89.1K

    • Revolution Analytics – An exclusive blog dedicated to the news and information of interest to the members of the community, who are deeply interested in analytics and relation disciplines. The blog is updated every US workday, with contributions from various authors.

FrequencyAbout six posts per week

Facebook Fans: n/a

Twitter Followers: 25.9K

  •  Data Science for Social Good – This social good data science does the work of training data scientists to handle the problems that matter. It effectively trains the data scientists to work on data mining, machine learning and big data.

FrequencyAbout one post per month

Facebook Fans: n/a

Twitter Followers: 20.5K

  • Data Camp – You can learn to be a data scientist from the comfort of your home through your browser with Data Camp’s data science blog. It’s a comfortable way where total information is available in one place, and you can pick up the topics that you want to master.

FrequencyAbout seven post per month

Facebook Fans: 340,109

Twitter Followers: 16.2K

9. Codementor – This blog tells you about the latest trends in data science. Here you can read tutorials, posts and insights from top data science experts and developers. This will eventually help you gain knowledge from experienced experts.

Frequency -About one post per month

Facebook Fans: 12,587

Twitter Followers: 22,1K

10. Dataversity – Data Science News, Articles & Education – Here, learn about the latest business intelligence news and get a thorough business intelligence education. This blog is focused more on the business side and understanding it is necessary from the business point of view.

Frequency – About one post per week

Facebook Fans: 6,312

Twitter Followers: 17.4K

11. Data science @ Berkeley I Online Learning Blog – If you are interested in an online course called professional Master of Information and Data Science (MIDS) from UC Berkeley School of Information.

Frequency — About one post per month

Facebook Fans: 14,804

Twitter Followers: 10.2K

12. Data Plus Science – This blog helps people find real answers in data science, quickly and effectively. So it’s a swift means of knowledge generation.

Frequency — About two posts per month

Facebook Fans: 2,932

Twitter Followers: 25.1K

13. NYC Data Science Academy Blog – A one-stop destination for in-depth development tutorials and new technology announcements created by students, faculty and community contributors in the NYC DCA network.

Frequency — About five posts per week

Facebook Fans: 2,136

Twitter Followers: 17,1K

14. Data Science 101 – A blog on how to become a data scientist.

Frequency — About five posts per week

Facebook Fans: 15,925

Twitter Followers: 2,365

15. Data Science Dojo – It’s a revolutionary shift in data science learning. The course offers short-duration, in-person, hands-on training that will get the aspiring data scientists started with practical data science in just a week!

Frequency — About one post per month

Facebook Fans: 12,009

Twitter Followers: 4,664

The Data Science Resources will help you keep updated and gain new knowledge and insights in the ever-evolving field of data science. The data science course at the Data Science Learning Center – Imarticus Learning will ensure updated knowledge to candidates.

Top Data Science Datasets Project Ideas for Beginners!

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.

How To Advance Your Business With Analytics & Build The Right Team?

In 2020, data is a goldmine of information, and if you can collect and analyze the right data sets, a lot can be achieved in a short period of time.

As companies around the world, start recognizing and collecting more data points from their customers, it is crucial than ever before to have a data analytics team, which can not only process and analyze the collected data but also emphasize sharing key insights which will assist you in advancing your business.

LinkedIn, the number one job search portal reported that 2020 saw a 25% increase in professionals who are seeking a Big Data Career in data science and analytics.

Bi Data CareerWhile this clearly indicates that the importance of data scientists is on a steady rise, it also indicates that companies need to better analyze the capabilities of each individual domain to choose the right man for the job.

How to Choose and Build the Right Data Analytics Team for Your Company?

One of the first and most crucial aspects to understand and embrace is the fact that in 2020, data scientists come with a variety of different skill sets, and thus it is essential to recognize each of the skills and categorize them into the functions best suited for.

While building an analytics team for your organization, you can follow either of two different approaches.

  1. The Direct Method of Segmentation
  2. The Indirect Method of Appreciation

The Direct Method of Segmentation

The concept of the direct method of segmentation is based on the ideology that each data scientist depending on their skill set can be grouped into either of three different designations and then hires can be made based on deciding which skill is required first.

  1. Data Engineers: Data Engineers are the crux of any data analytics team you want to design. The main skill sets you should look for in a data engineer include, ETL (Extraction, Transformation, and Load), Data Warehousing, data processing, and other similar roles.The fundamental job of a data engineer can be summarized as preparing the data for further analysis by data scientists and analysts, who form the rest of the team. They generally have a degree in Big Data Analytics Training.

    Big Data Career

  2. Data Analysts: Using the data prepared by data engineers, analysts extract critical information and decisions which are helpful in solving problems and contribute to advancing business decisions within the organization.
  3. Data Scientists: Data scientists form the last hierarchy of the team and are mainly responsible for crafting and perfecting algorithms using either Machine Learning or Artificial Intelligence to make compelling decisions from unstructured data sets. While a data scientist can easily be tasked with the responsibilities of both analysts and engineers, in big teams these designations are separated for better utilization of time and resources.

The Indirect Method of Appreciation

The indirect method of appreciation is based on the concept of recognizing people who have a broad range of skills, but also in-depth knowledge in a few key areas. This method of hiring can be understood using the “T-Shaped” skill concept, where the horizontal bar of the T represents the broader knowledge set of the hires, and the vertical bar represents the specialized knowledge in key areas.

The overall aim of this methodology is always to find the right set of people, who have the expertise and the knowledge to get the work done in a timely manner.

Conclusion

Building the right data analytics team for your business can not only contribute to its immediate success but also long-term growth. Thus always make it a point to invest the right amount of resources and figuring out which methodology of hiring works best for your business.

Optimization In Data Science Using Multiprocessing and Multithreading!

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/