Earned a data science certification? What next?

Posted on January 15, 2023April 5, 2024 by Imarticus Learning

A Data Science Certification course can be an excellent way for students to gain the skills and knowledge needed to pursue a career in data science. The course will provide an in-depth overview of data science concepts, techniques and technologies essential for success in this field. Through the certification program, students will develop the ability to manage and analyze large datasets effectively, create visualizations from their findings, build predictive models, and confidently use machine learning algorithms.

By completing a Data Science Certification program, students can access job opportunities at established tech companies and cutting-edge startups that require skilled professionals familiar with data science tools and best practices. Furthermore, they will acquire the technical expertise necessary to tackle real-world problems and develop innovative solutions. With the acquired knowledge, students can pursue various roles, from data analyst to data engineer to data scientist.

By having a Data Science Certification, employers will recognize that you have the skill set needed to succeed in the ever-changing world of Big Data and Analytics. Your certification will also demonstrate that you have taken the initiative to acquire more specific skills related to this field and be committed to learning more.

How can PG in Data Analytics after data science certification help?

A PG in Data Analytics can help students build upon the skills and knowledge gained through completing a Data Science Certification program. This program offers hands-on projects, allowing students to further develop their expertise in data science tools while applying them to real-world scenarios. Students will learn advanced techniques such as natural language processing, deep learning, and image recognition that cannot be acquired through a certification program alone. Additionally, some postgraduate programs guide how to create a portfolio that successfully showcases your capabilities as a data scientist. Finally, the postgraduate program may offer mentorship from industry professionals or networking opportunities with other experts in the field, both of which are invaluable for career growth. Combining a Data Science Certification with a Post Graduate Program in Data Analytics gives students a strong foundation for developing a successful career in this field.

Thus, it is clear that with the help of a data science course with placement and a postgraduate program in data analytics, students can transform their careers and get desirable growth opportunities. With the acquired knowledge and skillset, they will be prepared to take on challenging roles in data science and remain competitive in this rapidly changing field.

How does Learning Hadoop help with Data Science and Data Analytics?

Learning Hadoop can be beneficial for those interested in data science and analytics. Hadoop is an open-source software framework that stores, processes, and analyses large datasets quickly and efficiently. It enables developers to write programs that process massive amounts of data across distributed clusters of computers. By learning Hadoop, students will gain the skills and expertise necessary to build more complex data systems and manage large-scale datasets. Furthermore, when you learn Hadoop online, it allows you to understand the different parts of a data science project, from building an ETL pipeline to producing meaningful insights. Therefore, adding Hadoop to your data science skill set can open up new possibilities for career development.

Making a Career Transition with Imarticus Learning:

Are you looking for an edge in the highly sought-after data science and analytics field? Imarticus Learning has just what you need! Their personalized Postgraduate Program In Data Science And Analytics is designed to give recent graduates and professionals a head start on their careers. You will gain industry knowledge and the practical skills necessary to succeed in this ultra-competitive job market – all while benefiting from guaranteed job assurance upon completion of your program! With 25 real-world projects crafted by experts geared explicitly towards today’s businesses, get ready to make waves as one of tomorrow’s tech superstars starting now with Imarticus Learning.

Course USPs:

Build a strong foundation of Excel for data analysis
Summarise data with pivot tables and charts
Build a solid foundation for programming
Practice coding skills with 20+ coding questions
Build a strong SQL foundation for data querying
Create datasets for data analysis
Run data analysis process using Python libraries

Transitioning to Data Science: How To Get There?

Posted on September 21, 2022September 21, 2022 by Imarticus Learning

Transitioning to Data Science: How To Get There?

The emergence of big data has prompted businesses to rely on data to make informed decisions. But how do you make sense of all that data? That’s where the data scientist enters the scene.

A data scientist collects, analyses, and interprets data to make business recommendations. They do this by using AI, Statistics, and Computer science. They can apply these skills to a wide range of industries such as healthcare, finance, and retail.

The Data science career is an excellent choice for those interested in working with data. And it’s pretty rewarding with the right skills. Whether you want to advance in your career in data science or start, many options are available. One of the most effective ways is through data science courses.

These courses enable students with the right skills and knowledge to succeed. Let’s explore how you can transition with the help of proper data science online training.

Transitioning to Data Science via Applied Learning Courses

The easiest way to transition to the data science space is to learn the fundamentals of the subject. Consider enrolling in a data science online training program that incorporates applied learning.

This learning method is convenient since students are not restricted to physical classrooms. Also, it offers students the flexibility to learn at their own pace, anywhere, and at any time.

The learning model in an online environment is flexible and includes various resources. These include videos, lectures, articles, books, and one-on-one interactions with the instructor. Next, let’s talk about what you’ll need to learn in your data science course.

Data Science Online Training Curriculum

To transition into data science, you’ll need to learn the fundamental concepts, including:

Statistics
Probability Theory
Probability Distributions
Hypothesis Testing
Statistical Tests

These topics form the core of any data science course. Moreover, an understanding of Machine learning and Predictive Modelling is also required. Apart from these topics, you’ll also learn about some essential tools and languages. These include:

Python
Tableau
SQL
NumPy
Pandas

Combining these tools with fundamental concepts will help you in your data role.

What Makes a Good Data Science Online Training?

So, we’ve considered the curriculum requisites for transitioning into a data science career. Now, let’s examine what makes a great data science course based on applied learning: Faculty from top institutes such as IIT/IIM and other industry experts.

Live instructor-led online sessions with access to the session recordings and notes.

Online discussion with course faculty and peers to clear doubts and brainstorm.

Real-world case studies and live projects to provide insight. These concepts can predict property value, heart disease, taxi fare, etc.

Regular assignment assessments to track progress.

A Capstone project to collate and present the course learnings.

Career support activities, including resume-building workshops and mock interviews for job placements.

Startup mentorship and funding for those looking to start their ventures.

Certification of completion.

Access to an alumni network and a chance for an on-campus immersion program.

A course providing these things will ensure that you learn data science skills and make data-driven decisions. One such data science online training based on the concept of applied learning is offered by Imaticus Learning.

About Imaticus Learning

Imarticus Learning, in collaboration with iHUB DivyaSampark @ IIT Roorkee and the Department of Science & Technology (DST), offers one of the best certification programs in Data Science and Machine Learning.

Since the program is made especially for the early and mid-level professionals, you’ll understand the various aspects of data science and learn how to implement and apply these concepts to real-world problems.

Imaticus Learning covers all the points that make for excellent data science online training program, as mentioned earlier. Check out the success stories of those who transitioned into the data science field. Some alumni work with big brands such as Accenture, Capgemini, KPMG, etc.

You can visit the Imarticus Learning website for live chat support. You can also drive to one of their training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Conclusion

As data becomes central to decision-making in organizations, those with the skills to analyze and interpret it will be in high demand. Starting or transitioning to a data science career requires a strong foundation. And to help you with that, you can look for result-oriented data science courses.

Attending these courses helps you develop the skills needed to become a top Data scientist, Data Analyst, Business analyst, Data manager, Business Intelligence expert, etc.

This blog discussed what constitutes a top data science online training program and explored one such institute in Imaticus Learning. Feel free to check it out. Also, let us know your opinion on the necessary things for transitioning into the data science career.

The Growing Importance Of Data Science Certifications

Posted on June 9, 2022September 7, 2022 by Imarticus Learning

The Growing Importance Of Data Science Certifications

Data Science is a popular rising topic with several employment opportunities. According to a LinkedIn research, Data Science is the fastest-growing new employment category, with a whopping 37 percent hiring increase over the last three years. Data Science is reshaping practically every business and growing in popularity. But, what is the significance of Data Science? This post will provide you with an answer to that question.

What is the history of Data Science?

It has been an essential component of numerous sectors, including agriculture, marketing optimization, risk management, fraud detection, marketing analytics, and public policy.

It attempts to handle numerous challenges within individual sectors and the economy by utilizing data preparation, statistics, predictive modelling, and machine learning.

It promotes the usage of general methods without varying their application, regardless of domain. This approach differs from traditional statistics, focusing on offering solutions relevant to specific industries or topics.

Why is Data Science important?

Data is pointless until it gets transformed into useful information. Data science is about mining massive datasets, including structured and unstructured data, and uncovering hidden patterns to derive relevant insights. The significance of Data Science rests in its numerous applications, which span from everyday activities like asking Siri or Alexa for suggestions to more complicated applications such as running a self-driving car.

Data Science includes Computer Science, Statistics, Inference, Machine Learning techniques, Predictive Analysis, and new technologies.

It enables businesses to understand their customers better and become more powerful better. Customers are the heart and soul of every company, and they play a significant part in its success or failure. With data science, companies can interact with their customers in a more customized way, resulting in increased brand power and engagement.

One of the reasons it is gaining popularity is that it helps marketers tell their stories engaging and impactful. When brands and businesses use this data effectively, they can convey their narrative to their target audience, resulting in stronger brand connections. After all, nothing connects with customers like a compelling tale that evokes all human emotions.

The Future Of Data Science

Today, companies have access to massive databases due to documenting every element of client engagement. Data science plays a vital role in assessing and developing machine learning models based on this data. This is because these datasets get utilized to provide valuable insights. As a result, it’s logical to expect that as analysis and machine learning increase, the demand for data scientists will increase.

More positions should become available as the area advances. Individuals interested in pursuing a profession in data science may look forward to a bright future. Data Science has broad use across all sectors.

Discover data science certification course with Imarticus Learning

The Data Science career helps students get started in data science and machine learning. This curriculum, created in collaboration with iHUB DivyaSampark @IIT Roorkee, will teach them the principles and elements of data science and machine learning and arm them with the necessary skills to apply these ideas to real-world issues.

Course Benefits For Learners:

This 5-month curriculum, created by renowned IIT faculty members, will teach students how to utilize Python to understand data mining and machine learning techniques.
Learners will be able to acquire a strong foundation in data science and specialize in machine learning using Python for data-driven decision-making with this data science curriculum.
The data science online training will be live via online sessions with India’s best teachers.

Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon.

So You Want A Data Science Career? Start By Doing This

Posted on June 2, 2022September 2, 2022 by Imarticus Learning

So You Want A Data Science Career? Start By Doing This

If you are searching for how to start building a robust data science career, then you must have come across hard skill terms like machine learning (ML), analysis, and statistics. Apart from the hard skills, you should also possess soft skills like persuasive communication and critical thinking if you want to grow up in this role. The industry of data science is vast with endless opportunities so you just need to have the required skills and education to land your dream job in the future.

What are the different career options in data science?

Here are some of the most common job titles you can be entitled to if you want a career in data science:

Data Mining Engineer

A data mining engineer not only examines the data of their own business but information from third-party as well. Apart from analyzing data, a data mining engineer also develops complex algorithms for analyzing the data.

Business Intelligence Analyst

These analysts figure out business and market trends by using data. They analyze data and develop a clear picture of the company’s position.

Data Scientist

Data scientists can translate any business case into an analytical agenda, creating hypotheses, interpreting data, and discovering patterns to measure the impact they create on businesses. They can also select relevant algorithms to help in analyzing the data further. Their role is not just limited to explaining what impact will be created by data on the company but also to bring out solutions that will be helpful for the company in dealing with these effects in the future.

Data Architect

A data architect works in close association with system designers, developers, and users for creating blueprints that are used by data management systems to integrate, centralize, maintain and safeguard the sources of data.

Senior Data Scientist

A senior data scientist has the potential to anticipate the future needs of a business. They collect data and also analyze it thoroughly to get through very complex business problems smoothly. Using their prior experience, they can design new standards and also drive their development. They also find out ways in which they can use the statistical data and create tools for analysis.

An overview of Imarticus’ certificate programme in data science and machine learning

Our certificate program in data science and machine learning has been curated to help you know the initials of data science and machine learning. In collaboration with iHUB DivyaSampark @ IIT Roorkee, we have designed this programme to help individuals learn the fundamentals along with various aspects of data science and machine learning.

In the process, you will be also equipped with the knowledge required to implement these concepts to real-life problems. You will also know about data mining and machine learning tools using Python. One of the most remarkable aspects of this 5-month programme designed by eminent IIT faculty members is that it teaches you how to use data-based insights to create a positive impact on organizational growth.

This particular data science programme will also help individuals build a very strong base in databases and gain specialization in machine learning with Python as it will lead to data-oriented decision-making. This programme is an attempt to raise professionals who can render meaningful data insights and help businesses follow the right path to achieve their growth objectives.

Why choose Imarticus’ certificate programme in data science and machine learning?

There are plenty of data science courses available but the programme that we offer stands apart from the rest in many aspects, such as:

Get LIVE training sessions by IIT faculty and industry experts

The programme will be delivered to you live through online classes taken by top Indian faculty belonging to IIT Delhi, IIT Roorkee, and other distinguished institutes. Industry experts also have intense knowledge about data science and machine learning as they have been working on relevant projects in several organizations.

Startup mentorship and funding programme

Those who are enthusiastic about start-ups can also pitch ideas and seek funding support on this platform created by iHUB DivyaSampark @IIT Roorkee under the national scheme of Digital India.

Recognition from iHUB DivyaSampark @ IIT Roorkee and the Department of Science and Technology

Once you complete the programme, you will receive a certificate issued by iHUB DivyaSampark @ IIT Roorkee, Department of Science and Technology in partnership with Imarticus Learning under NM-ICPS. The certificate will create a huge impact on your profile and will give a boost to your data science career.

Conclusion

So, if you have already decided on starting a career in data science and machine learning, what are you even waiting for? Go ahead and get registered for this certificate programme on our website. You can also call us to chat with us to know more details about this certificate programme that is a magic wand for all data science aspirants.

Does Machine Learning Excite You? Check Out Our Data Analytics Course!

Posted on February 27, 2022March 3, 2022 by Imarticus Learning

Machine learning (ML) is truly a blessing to modern computing and technology, possessing the ability to endow systems and machines, the ability to think for themselves and tackle tasks on their own without any supervision of humans. Machine learning is able to do this by creating artificial neural networks which simulate how human brains work. Machine learning is assisted by data science and supports its applications in various fields.

Even though machine learning was initially invested upon with the primary focus on Artificial Intelligence, it was later recognized as a separate field and started being heavily invested upon from the 1990s and is one of the most valuable fields of computing that has one of the highest industry requirements of skilled professionals and freshers holding expertise in various skills and tools which assist in machine learning.

In this article, we will learn more about machine learning and how a well-planned data analytics course can help you progress in your career if you are already in this field or how it can help freshers get exposed to ML.

What is machine learning?

Machine learning first came into existence due to the interest of having systems and computers learn from data on their own. “Machine learning” was first termed by Arthur Samuel in 1959, who was working in IBM at that time. During his tenure there, he was responsible for various important projects related to computer gaming and AI. It all started when Mr. Samuel took the initiative to teach computers how to play games through the game of Checkers on IBM’s first commercially available computer, the IBM 701.

Eventually, machine learning started being used for various purposes and borrowed many models and approaches from statistics and probability theory. AI uses predictive analytics along with machine learning to execute the various responses or trigger actions. All of this is acquired from the training data set which helps the machine in learning and equips it with the information.

Machine learning is an important branch of computing and data science that creates autonomous systems which learn from data on their own. A machine trained with clean processed data eventually identifies trends and patterns to respond to situations without human supervision.

Machine learning also promotes the automatic improvement and development of algorithms or data models which improve on their own. Machine learning is an important part of Artificial Intelligence which uses data mining, predictive analytics, and various tools to assist machines in learning more extensively with methods like deep learning to allow them to execute functions that emulate the responses of a human, just much more accurate and fast.

Machine learning is also not biased unless specifically asked to do so, hence promoting unbiased AI-supported systems that make fewer errors. Data mining is also a very relevant field and quite valuable to machine learning as it helps systems come to conclusions without having some bits of data or having unknown bits of information. Machine learning is a type of predictive analytics which is backed by data and is exploratory in nature.

Perks of a Data Science Prodegree from Imarticus

The Data Science Prodegree is a great data science course that students and working professionals can choose to gain more exposure and skills in the fields of machine learning, business analytics, and AI.

Acquire skills and learn how to use required tools and algorithms

Gain valuable industry and course certifications

Get placement support and opportunities from the best companies

Advanced live classroom learning supported by technology and real-life projects

Imarticus’s Data Science course with Placement is a great choice if you wish to advance in your career and learn about machine learning, AI, business analytics, or data analysis which will help you become more effective as a data scientist and pursue your dream career in this respectable field.

How to Excel in Data Science?

Posted on December 7, 2021May 23, 2024 by Imarticus Learning

Data science has been growing and has infiltrated everyday life, even if sometimes we are not aware of it. To excel in this discipline that is becoming so popular there are several things you can do, and you should know that the first of these is not necessarily to learn data science although it is of course on the list. It is very common that when you go to a website you will be recommended products that might also be of interest to you.

Or when you search about something, the search engine completes the sentence for you or makes a suggestion. All this is driven by data science, but do you know what data science is? Do you understand what it means? Do you know where data science is applied? Understanding this is the first step to becoming an excellent data scientist followed of course by a good data science certification course.

What Is Data Science?

Data science is first and foremost the discipline of making data useful. Above all data science has become a new approach to problem-solving and strategizing. Although the computing power of today’s computers and data centers is an element without which Data Science would not have much scope, we are dealing with a discipline where other areas of knowledge converge that cannot always be clearly defined.

Several subfields include mining large amounts of information, making decisions based on limited information, and using patterns to automate tasks. Each subfield encompasses a science or technology, and it is important to understand the differences,

Analytics

Analytics allows for the analysis of all types of data in real-time, historical, unstructured, etc. Above all, it is the process of examining data sets to find trends, hidden patterns, correlations, and conclude the information extracted. It is now possible to analyze data and get answers almost immediately, which is not possible with traditional solutions.

Statistics

Statistics is mainly concerned with putting data in order and analyzing it to obtain predictions and forecasts about specific phenomena. It is made up of methods, procedures, and formulas that enable relevant conclusions to be drawn. Its main objective is to improve the understanding of information.

Artificial Intelligence

Thanks to machine learning, artificial intelligence can process massive amounts of data, which we as humans could never do. It also refines models through algorithms and predictive analytics, allowing machines to perform activities that we can consider intelligent on their own.

In short, data science employs a variety of technologies and methods to process and analyze data. The important thing to become excellent at data science is to find a good data science certification course. By finding a course that fully exploits your capabilities and develops your skills you can become an excellent data scientist.

Why Study Data Science?

If you learn data science you will make you part of the changing world. You will develop skills in computer science, programming, statistics and learn how to analyze and use the information to solve problems and develop strategies. There are many sources of learning but not all of them will suit your needs and those of the market. Our data science courses are designed by industry experts so you will learn real-world applications to generate useful solutions.

The program Post Graduate Program In Data Analytics & Machine Learning is designed for those looking to build their career in data science especially for recent graduates and early career professionals. The Data Science courses will go a long way to ensuring that you become an architect of your future. Seeing the increasing demand for the application of this discipline, it is logical to expect an increase in the demand for data science professionals.

How to become a data scientist by optimizing your career

Posted on December 4, 2021January 22, 2024 by Imarticus Learning

In 2021, many businesses have transformed themselves digitally. Businesses are interacting with customers on digital channels. The digital channels are also helping businesses to provide services round-the-clock to customers. Business organizations have never cared about their IT infrastructure more than now. With accelerating digital transformation, the amount of data produced by business organizations is also increasing.

There is a need for skilled data scientists in the industry that can perform high-end data analysis and extract insights. It is why many young aspirants want to learn data science and secure their careers. Read on to the best way to learn data science and secure your career.

Roles of data scientists in 2021

Before you go on a job hunt in the industry, you should know about the roles of a data scientist. Some of the tasks performed by a data scientist in 2021 are as follows:

Data scientists must collect data from various sources to perform analysis. In starting, data scientists collect unstructured data that makes no sense. However, the unstructured data contains many meaningful insights that will be uncovered after analysis.
Once data is collected from different sources, it needs to be cleaned and classified. Redundant data points or outliers need to be removed for high-end data analysis. At present, data scientists use many analytics platforms for faster data cleaning and classification.
A data scientist also performs exploratory data analysis to identify the main characteristics of a data set. Via exploratory data analysis, data sources are manipulated to get the answers needed.
Data scientists are also involved in developing better analytics models. Analytics models run on algorithms are performing data analysis without any manual support. Data scientists also use new-age technologies to develop better analytics models.
Data scientists often collaborate with other IT teams to find loopholes in the IT infrastructure. With analytics results, data scientists try to implement methods that could drive business performance.
Data scientists also make data accessible for everyone in a business organization. They represent complex data via several visualization techniques. It makes data easier to understand by every employee of a business organization.
Business organizations rely on data scientists to find trends and patterns among data sets. Those patterns are then used to prepare market forecasts and demand forecasts.

Skillset required for becoming a data scientist in 2021

A data science online course can help you in acquiring the competencies required by a data scientist. The skills required by data scientists in 2021 are listed below:

Data scientists need to be fluent in coding languages like Python, Java, SQL, and MATLAB. A reliable data science online course can help learn the coding languages used by data scientists.
Data scientists need to have a sense of the current business landscape in which they are working. Many firms spend funds on data science training with business in focus for their employees.
Analytical and data visualization skills are necessary for a data scientist. Young aspirants can undergo data science training to learn data mining, munging, visualization, and reporting.

Which is the best course to learn the skills needed by a data scientist?

Imarticus provides the best data science certification courses for young aspirants. It also offers a PG Program in Data Analytics & ML for working data scientists. Its Data Science Prodegree is also popular among aspirants in India.

All the data science courses offered by Imarticus Learning follow an industry-endorsed curriculum. The industry-oriented curriculum focuses on teaching industry practices to job seekers. By learning techniques and practices used by data scientists, you will be job-ready. Start your data science certification course to become a successful data scientist!

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/

How Data Science Training Will Ensure Business Continuity In The Post-Covid World?

Posted on April 28, 2021March 26, 2024 by Imarticus Learning

Lockdown restrictions have pushed us into recession along with a health crisis we have never seen before. Businesses are struggling to make ends meet, and many have had to take tough decisions like layoffs, frozen hiring, salary cuts, and more. The year has also shown us how vital digitalization is and why data science is the driving force in the post-COVID era.

One of the most prominent examples is remote work and how people have adapted to such a form of work. Businesses are accepting digital tools to optimize their functionality, and this is where data science comes in.

Digital tools not only help businesses measure ROI but also determines every small or big aspect like regulating spends, analyzing the long-term impact, and more. The change is making more companies include AI, VR, AR, and cybersecurity to transform their businesses.

Why is data science essential for business continuity?

Here are some of the reasons why adopting a data science course is crucial for current and aspiring businesses leaders:

Analyzing and forecasting

The pandemic gave us clarity of how uncertain times can be. Businesses can go through drastic changes due to such conditions unexpectedly.

With the help of data analytics, we can predict our future better. It helps us analyze risks and develop strategies to mitigate them.

Companies can use historical data from the current situation to estimate t

he trends of the future. Data science training can also give us cues of future obstacles and how to handle them efficiently.

Moreover, historical data can help plan different outcomes of businesses during an unfortunate phase.

Assessing resources for maximum utilization

While analyzing risks and mitigating them fast can prevent disrupting business processes, knowing the way to put resources to use during such times is also crucial.

Every company needs to imply analytic practices to get through critical times and understand the state of their business.

One example of this would be setting up an analytics team for finance. The process will help a business find how changes in the economy can or are affecting their business. Data assists in allocating resources and promotes effective decision-making.

Identify Opportunities

When you adopt data science, you can identify new opportunities for business continuity. The process of analyzing available data resources helps catch loopholes early and find new possibilities to overcome them.

Currently, most companies are aggregating COVID data and combining the same with employee data. It helps one understand better ways to support employees in this crisis. For example, updating a dashboard to check geographical data helps offices take preventive measures or recommend closedown. HR’s are using such data to determine if offices are safe to open or not.

Necessity of cybersecurity

As soon as you step into the data science career, you will know how critical cybersecurity is. It is the most vulnerable risk that any form of data can pose.

Much of this has been at stake since people have been working remotely. There is also an increase in cloud services, which need constant technical tracking, maintenance, and recovery.

Even when companies work remotely, they have to imply IT security to safeguard data exposure and threat. Data science and AI give us solutions to cyber threats and monitor network traffics more effectively across VPNs. It quickly detects points of breaches and infringement in real-time.

Helps in quick changes of services

E-commerce and retail industries use data science and AI to attract customers; both online and offline. Companies now leverage AI-powered solutions to get insights into changing consumer demands. Such data helps optimize the supply chain and minimize disruption.

There is a high usage of AI chatbots to get quick solutions to external factors. These chatbots communicate with customers, answer their questions, and record their responses. It is assumed that the usage of chatbots will multiply by 50 times compared to what it was before COVID.

Final thoughts

Business continuity is critical and crucial. More and more companies need to incorporate data science to make businesses effective and reliable. While it has been vital for a long time already, it will become non-negotiable in the post-COVID era. The above-listed reasons give you a brief insight into the bigger picture and why data science is the future of your business goals.