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Tag: data science career

Top Data Science Datasets Project Ideas for Beginners!

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is Data Science?

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

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

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

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

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

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

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

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

  1. Automated Chatbox Project

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

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

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

  1. Automated Caption Inserter Project

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

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

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

  1. Song Recommendation Project

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

Overview

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

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

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

Optimization In Data Science Using Multiprocessing and Multithreading!

Posted on by Imarticus Learning
Reading Time: 3 minutes

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

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

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

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

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

Key differences between Multiprocessing and Multithreading

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

Advantages of Multiprocessing

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

Advantages of Multithreading

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

Optimization in Data Science

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

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

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

Related Article:

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

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

Posted on by Imarticus Learning
Reading Time: 4 minutes

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

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

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

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

R Programming Interview Questions

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

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

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

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

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

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

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

SQL Interview Questions

SQL online Training

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

  1. Point out the difference between MySQL and SQL?

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

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

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

There are two types of DBMS:

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

 Tableau Interview Questions

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

  1. Briefly explain Tableau.

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

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

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

  1. What are Measures and Dimensions in Tableau?

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

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

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

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

Related Article:

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

Become A Data Scientist And Start Your Career With A-list Firms !

Posted on by Imarticus Learning
Reading Time: 2 minutes

Become A Data Scientist And Start Your Career With A-list Firms !

If you’re wondering why data science courses have become so popular recently, then it’s because the demand for data scientists is very high among A-list firms.

In the following points, we’ll discuss some of the biggest companies that hire data scientists and how you can start a career in one of them by taking an online data science course in India

Top companies that hire data scientists

Microsoft

If you’re using a PC, then you’ve probably heard of Microsoft already. Microsoft is a software development company and powers millions of computers throughout the world through its Windows operating system. The average pay of a data scientist at Microsoft is $136,000 per year.

Uber

Uber is an online cab-hailing service that has become widely popular due to its innovative solution. It uses data science to improve its operations, optimize route selection for drivers, calculate better fares, and a plethora of other tasks. The average pay of a data scientist at Uber is $139,000 per annum.

Pinterest

Pinterest is a social media platform for sharing and finding images. Social media platforms require the expertise of data scientists to help them enhance their algorithms and offer a better customer experience to the users. The average pay of a data scientist at Pinterest is $212,000 per year.

Google

Google is probably the most popular tech company in the world. It is also among the best employers for data scientists. The average pay of a data scientist at Google is $138,000 per year. Being a simple search engine, Google has expanded into a large tech enterprise with various subsections.

How to become a data scientist

As you can see, becoming a data scientist can help you bag lucrative jobs in some of the world’s top companies. Starting a career in data science is quite easy as well.

You will need to join a data science course in India to learn the necessary skills for this role. Online data science courses have become increasingly popular among students and professionals alike who want to start a career in this field.

You should look for data science courses in India that teach you the latest in-demand skills such as R, statistics, predictive analysis, Hadoop, and Spark. It would be best to get a data science course with placement support. When you join a data science course with placement support, you get the opportunity to start your career right after completing the online data science course in India.

Be sure to check the relevant data science course details (such as data science course fees and eligibility requirements) while looking for data science courses in India.

Conclusion

The companies we talked about aren’t the only ones that hire data scientists. Data science professionals work in numerous sectors as well, including finance, education, healthcare, and manufacturing.

Online Data Science Courses in India

Now, you know that you can start a career in this field by taking a data science course in India, you should head to our site where you’ll find more data science course details, including the data science course fees, requirements, and curriculum.

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

Posted on by Imarticus Learning
Reading Time: 3 minutes

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.

Data Science course

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.

Data Science CareerThere 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.

Breaking the Data Science Myths For a Better Career!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Data Science is a scientific discipline that employs algorithms, statistics, processes, and analysis to gain insights and understand in-depth unstructured data. Data Science is a very useful branch of science which is becoming widely popular among organizations.

It helps predict results and makes decisions in a variety of tasks. Data Science involves machine learning principles and analytics to understand patterns and find information.

Data Science Career

Data Science, as a field, evolved after the 90s. Today, it is a widely adopted and used AI platform. Data Science career is becoming a hugely in-demand profession globally.

And as with many other popular jobs, the job of a data scientist is also associated with a lot of myths. But myths are natural. Any attractive thing induce thoughts and beliefs in people’s minds and these can result in myths.

If you are looking to build a career in Data Science, you need to uncover the myths related to this profession as myths can impact your career choices. In this article, we will burst the common myths of Data Science.

No compulsory Ph.D. required
Yes, you read that right! A doctorate is not mandatory for the role of a data scientist. The data scientist profession is divided into two parts – Research and Applied data science. If you are looking to pursue a career as an applied data scientist, then all it requires is the knowledge of basic applications of techniques, the functioning of algorithms, and an in-depth understanding of this field.

However, if you want a research role, then it is good to have a Ph.D. as it will involve working on research papers, creating new algorithms, etc.

Online courses or Part-time degree are acceptable
Contrary to the popular belief, a person need not have a full-time data science degree to pursue a career in data science. There are many online data science courses, part-time or correspondence degrees available that equip you with the knowledge required to pursue this career. All you need is the right skill-set and passion for the field of data science.

Background in Specialized Subjects is not necessary
Data Science is a combination of different subjects like Programming, Communication, Computer Science, and Mathematics. It is important for data scientists to possess knowledge of all these subjects, as each of them plays a major role in a successful data scientist career. Programming is needed to understand data hierarchies and develop algorithms.

Communication is needed to reach out to people and convey them useful information in an easy-to-comprehend manner. Mathematics is needed to deal with structures, models, and designs. Computer Science is needed to incorporate different strategies and plans in the projects. However, one need not have a background in any of these subjects to become a data scientist. A good understanding of all these sectors is enough for a fruitful data science career.

Related Previous Work Experience is not required
Anyone with work experience in any technology related to the field of data science is enough to build a career as a data scientist. One can also step into this field without any relevant technological experience. However, in that case, you will start with the beginner level.

One must equip themselves with the domain knowledge and skills required for this role to become a successful data scientist.

Interesting Puzzles To Prepare For Data Science Interviews !

Posted on by Imarticus Learning
Reading Time: 3 minutes

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

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

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

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

Data Science Career Interview

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

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

Sol:

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

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

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

Sol:

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

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

Sol: 

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

X runs 5 kms

Runs 5 kms

Z runs 5 kms

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

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

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

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

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

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

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

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

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Why It Is Right Time To Pursue A Career in AI, ML and Data Science?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Introduction

The world is all set for a digital transformation. New technologies are disrupting how business is being conducted on a day-to-day basis. Among the most notable of these technologies are Artificial Intelligence (AI), Machine Learning (ML), and Data Science.

These technologies are constantly restructuring the landscape of different economies throughout the globe, as it provides tremendous career opportunities. Moreover, these technologies are also interrelated which gives an individual a chance to build a holistic, well-paying, and satisfying data science career.

Career In Data ScienceWhy now is the Right Time?

We are living in the age of the fourth industrial revolution where everything is expected to be data-driven. Moreover, the pace at which the volume of data is growing is simply astonishing.

According to an IBM survey, 90 % of the data available has been created in the last two years. Technological devices like smartphones, tablets, and laptops have revolutionized the way users interact with the internet, and this number of users is also increasing at an exponential speed.

Now, accumulating data is not enough. An analysis of data is required to produce insights that can help in the curation of actionable results. This is exactly where the tools of AI, ML, and Data Science become relevant. These tools leverage various techniques from mathematics, statistical modeling, data engineering, data visualization, computer programming, cloud computing, etc.

To extract the insights from data collected by an organization. Now, this insight forms the basis of strategic decision-making in any organization. It is used to create targeted ads, augment customer experiences on company websites, reduce costs, forecasting demands, and so on. Therefore, the application of predictive algorithms like AI, ML, and data sciences are pervasive throughout different functional domains.

Again, these tools are used across different organizations as well. Governments, Corporates, Brands all are leveraging the advancements in technology to create an entire automated, data-driven ecosystem. Therefore, naturally, there has been an upsurge in the demand for data science courses in India and data science jobs across industries and functions. It is estimated that in India close to half a lakh positions have opened up.

Data Science CareerFrom an Indian context only, a typical data scientist is expected to receive a salary of around INR 9 lakhs p.a. Similarly the salary figures for AI and ML engineers would lie at around INR 5.5 lakhs p.a. and INR 11 lakhs p.a. respectively. Therefore, a six-salary figure makes a career in these disruptive technologies even more attractive.

With the pandemic changing the operation models across industries and functions, it can be safely assumed that technology is going to become even more relevant. Data Science, AI, and ML have a steep learning curve more and more organizations are adopting newer and agile techniques.

From expensive platforms, SPSS, SAS, etc. and organizations are now moving to open resource platforms like python and R. Therefore, technology is no more the future anymore; it is here and those who are passionate about it can find a lucrative career opportunity in AI, ML and Data Science.

10 Data Science Careers That Are Shaping the Future!

Posted on by Imarticus Learning
Reading Time: 3 minutes

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

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

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

Data Scientist

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

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

Business Intelligence Analyst

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

Machine learning Engineer

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

Applications Architect

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

Statistics Analyst

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

Data Analyst

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

Infrastructure Architect

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

Data Architect

Data architects mainly focus on maintaining databases.

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

Enterprise Architect

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

Data Engineer

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

Conclusion

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

Data Science Job Opportunities Continue to Surge in 2022!

Posted on by Imarticus Learning
Reading Time: 2 minutes

Data science has revolutionized the functioning of almost all industries in the world today. The creation of data is the highest at the moment due to the widespread process of digitisation. Therefore data science tools and technological advancements are being deployed in order to push further productivity amidst all organizations.

With this, there is the provision of Big Data, Machine Learning, Data Analytics, Data Mining and Data Analysis thus creating large importance for this technological field.

All businesses and organizations require efficient and quick problem-solving methods. This is offered by data technology, having the ability to analyze and comprehend large sets of data in order to resolve a variety of problems in a fast-paced and accurate manner. This is a much more sought after a method as compared to the completely engineered solution.

The development of proficient machine language algorithms and a change of direction from analytics that were descriptive has resulted in driving progress. Predictive analytics and maintenance have slowly been gaining popularity amongst industries and this popularity only seems to be growing.

Data Science JobsThe demands for various data science services have been seeing a large surge all over the world as researchers for the market predict its magnification in the near future. Due to this increased demand, the path for various other talents and job aspirants is clearing. This would allow them to try their hand and work hard while in this genre of work. The vast number of technologies in relation to data are creating large opportunities for up and coming data professionals to seize.

With an estimated increase of over 1 lakh new job openings in the present year of 2020, which is a little more than a 60% increase from the previous year (2020), aspirants have a large number of openings to prove themselves with a data science career. Almost 70% out of these job opportunities are for budding professionals with experience less than or up to five years.

In a bid to remain in the fast-paced competition of today’s market and maintain relevancy, organizations, businesses and various other companies are taking up newly emerging technology. Due to a large amount of data that is being created, data technology and science is the answer to mining insights that are actionable for businesses.

There is thus a very large scope in this field for data science professionals set in the present year, 2022. This year has been the best year for Data science and furthering its opportunities.

Industries of energy, pharmacology, healthcare, media, retail, e-commerce, etc. are creating a large number of job opportunities in the field with average potential salaries going from 10 lakhs to even 14 lakhs per year.

The industry of data science had been previously (2022) facing a large shortage of skilled professionals which have increased in large numbers this year (2023).

By taking a data science course aspirants will be well equipped with all the necessary information in order to succeed in their future data science career.