Why Should Engineers Learn Data Science Differently?

Posted on October 30, 2021July 11, 2022 by Imarticus Learning

Why Should Engineers Learn Data Science Differently?

Data science and engineers have a lot in common. They both need to know how to collect, store, analyse and visualize data. Engineers are taught these skills as part of their curriculum; however, they may not learn them as they would if they were learning Data Science from the start. The following is an overview of why engineers should learn Data Science differently than other disciplines.

A blog post intro paragraph engages professionals about why engineers should learn data science differs from other disciplines. Engineers are taught these skills as part of their curriculum but may not understand them simultaneously or efficiently without exposure to them earlier in life.

Why is Data Science important for Engineers?

Engineers always like to think about their work in processes and systems, also known as Systems Thinking. It is what enables them to build more efficient products by efficiently running those processes. By thinking of the world in this way, engineers can quickly solve data-related problems because they see all sides of an issue that deals with data.

It’s important to remember that engineering can be applied in any industry, including Data Science. As a data scientist, it’s often necessary to run specific processes and analyze the results. Engineers excel as they can take these processes and incorporate them into the current system that the company may already have set up, saving time and money in some cases.

Benefits of Learning Data Science for engineers.

Therefore it is necessary to run specific processes and analyze results where engineers excel in taking these processes and incorporating them into current systems that a company may already have set up.

Learning Data Science is important because of the benefits that engineers will gain. Engineers overall will be able to learn more efficiently about their field and how it fits into the bigger picture. By taking this information, they will be able to make smarter decisions in data-related situations.

Engineers should learn Data Science differently from other disciplines because it will make them understand better and more thoughtful about their field and how it fits into the bigger picture, enabling them to make smarter decisions in data-related situations.

Why Enrol in the Data Science program at Imarticus learning

Industry specialists created this postgraduate program to help students understand real-world Data Science applications from the ground up and build robust models to deliver business insights and predictions. The Data Science program is for recent graduates and early-career professionals (with 0-5 years of experience) who want to pursue Data Science and Analytics, one of the most in-demand fields.

Twenty-five in-class real-world projects and case studies from industry partners will help students become masters in data scientist careers. Exams, hackathons, capstone projects, and practice interviews will help students prepare for placements.

Some course USP:

The course lets the students learn job-relevant skills that prepare them for an exciting Data Scientist career.
Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the practical implementation of real industry projects and assignments.

Contact us through the live chat support system or schedule a visit to training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, and Gurgaon.

Here Is How You Can Become A Future-Ready Data Analytics Professional!

Posted on August 29, 2021April 2, 2024 by Imarticus Learning

Employers of all kinds are fast recognizing data science as one of the most in-demand skills. Businesses, government agencies, medical institutions, and charitable groups are all hiring data scientists at a rapid pace.

Do you want to enter the workforce or improve your data science skills? Now that data scientists are in high demand; there are plenty of opportunities to study the craft.

Enroll In Any Imarticus Online Data Science Courses

Online learning is the most cost-effective and trustworthy method of instruction. Before you enroll in a course, verify that it covers the topics you’re interested in and that it is appropriate for your ability level.

Industry specialists together created the Imarticus postgraduate data analytics certification program to help you understand real-world Data Science applications from the ground up and build powerful models to deliver relevant business insights and predictions.

This program is for recent graduates and early-career professionals interested in pursuing a data science and analytics career, the most in-demand job skill. With this data science course with a job assurance guarantee, you may take a considerable step forward in your career.

How Do I Register For The Data Analytics Course?

This data analytics certification is just what you need if you’re an inquisitive spirit who wants to dominate the digital business by studying the latest and most in-demand data science abilities.

Once you have completed your counseling session and understood this course is right for you; you can then proceed to apply for the data science course in three easy steps:

Enquire
Enlist in a counseling session
Enroll

What Is the Program’s Duration?

The PG Program in Data Analytics is a full-time program that lasts six months (on weekdays). Each week, you will have 4-hour-long lessons from Monday through Friday.

What Is a Hackathon? How Does It Assist?

While enrolled in an Imarticus course, you will be assigned a Course Guide/Mentor who will assist you academically and advise you on the best career route for you.

Your Course Guide will inspire you to finish assignments, attend classes, and get the most out of their PG program in data science with placement.

Moreover, your Course Guide or Mentor will also be playing a role in the following activities:

Keep an eye on your grades and academic progress.
Provide you with insider information to help you land the job of your dreams.
Encourage you to do better on projects and homework in class.
Make lasting friendships that will help you get through the course and beyond.

The hackathons at Imarticus Learning allow you to compete against a large number of students and stand out from the pack. They are regarded for instilling critical thinking skills in Data Analytics and pushing them to achieve at a higher level.

Take Away

It takes more than being able to evaluate large amounts of data to become a Data Analytics Professional.

You must also be familiar with the organization’s business processes and identify ways in which your participation will impact, which is now available with Imarticus online data science courses.

Optimization In Data Science Using Multiprocessing and Multithreading!

Posted on May 31, 2021March 26, 2024 by Imarticus Learning

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

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

Advantages of Multiprocessing

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

Advantages of Multithreading

It provides easy access to the memory state of a different context.
Its threads share the same address.
It has a low cost of communication.
It helps make responsive UIs.
It is faster than multiprocessing for task initiating and switching.
It takes less time to create another thread in the same process.
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.

The 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!

Posted on May 28, 2021March 21, 2024 by Imarticus Learning

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.

Having 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.

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.

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

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.

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.

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.

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.

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.

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/

Using Near-Miss Algorithm For Imbalanced Datasets!

Posted on May 27, 2021March 22, 2024 by Imarticus Learning

Data scientists are required to obtain, pre-process, and analyze data. Companies can use the insights gathered by data scientists for making important business decisions. While this task seems straightforward, there is a multitude of challenges witnessed by a career in data science.

All seems to be a tedious task, right from learning the fundamentals from data science courses to generating data science. But the major challenge lies in data cleaning for any data science operation. To be specific, 70 percent of the work of a data scientist consists of cleaning and preparing data.

An imbalanced dataset is a typical example of unbalanced data. Let us see how to use the Near-Miss Algorithm for imbalanced datasets.

What is an Imbalance Dataset?

For classification problems, imbalanced datasets are a special case where the distribution between classes is not uniform. They are usually composed of two classes: the majority or negative class and the minority class which is also known as the positive class.

Imagine, in your dataset, you have two categories to predict: Category-A and Category-B. You have a problem with imbalanced datasets when Category-A is higher than Category-B or vice versa.

So how could this be a problem?

Imagine that Category-A contains 90 records in a dataset of 100 rows and Category-B contains 10 records. You run a model for machine learning and end up with 90 percent precision. Then comes the certainty check and you get to realize that the results are not accurate. This is a common error caused by imbalanced datasets.

Near-Miss Algorithm

The Near-miss Algorithm is used to balance an imbalanced dataset and is considered as an algorithm for undersampling and is one of the most powerful ways to balance data.

The Near-Miss algorithm works by observing the class distribution, removing samples located in the higher class. Simply put, if the algorithm witnesses a case in which two near points that pertain to different classes occur, it simply excludes the one from the higher class and ensures that the balance is preserved.

Types of Near-Miss Algorithm

There are 3 main versions of the near-miss algorithm. They are listed as follows:

Type 1: In this type of Near-Miss Algorithm, unbalanced data is improvised by assessing the minimum distance (avg) between the large distribution and three farther small distribution.

Type 2: In this version, the balancing of data occurs by figuring out the distance between ‘n’ neighbors of the data points belonging to smaller classes. The largest distance obtained from this calculation is eliminated.

Type 3: This version involves the calculation of the minimum or shortest base distance between the larger distribution and three other smaller distributions close to it.

Using the Near-Miss Algorithm for an unbalanced dataset

To use the Near-Miss Algorithm for an unbalanced dataset, three major steps are followed. As a part of the first step, the distance between the points belonging to the larger class and the point belonging to the smaller class is considered.

This is done to ensure that the undersampling process is simplified. Moving to the second part, the instances belonging to the larger class are selected. While selecting these instances, it should be noted that only those who have the shortest distance are chosen. As a final step, the algorithm returns m*n instances from the larger class.

Conclusion

The choice for an appropriate method depends on the dataset and the approach as desired by the user. Near-Miss is a popular undersampling technique that is used to deal with imbalanced classes.

However, it is not the only one. Other methods of dealing with unbalanced data include random sampling, SMOTE, etc. Therefore, make sure you are thoroughly aware of the technique before proceeding with it.

Do You Know Data Science Professionals Been Hired The Most ?

Posted on May 11, 2021March 26, 2024 by Imarticus Learning

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.

How 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.

Tag: Data Science Courses with placement in India