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Tag: data science courses

Unleash a wave of lucrative New Age career opportunities for skilled Data Scientists and Analysts

Posted on by Imarticus Learning
Reading Time: 3 minutes

Big data has revolutionized nearly every industry. Whether it’s social media or FMCG, every industry has started utilizing big data; and hence, the demand for data scientists and analysts is sky-rocketing.

In the following points, we’ll explore the scope of data science and analytics careers to find out why it’s worth pursuing a career in these fields.

Fintech CareerJobs of the future – scope of data science and analytics

Data scientists and analysts are big data professionals, which is becoming one of the fastest-growing sectors globally. Experts predict that the global big data market size will grow from USD 138.9 billion in 2020 to USD 229.4 billion by 2025.

Big data market size will grow at a Compound Annual Growth Rate (CAGR) of 10.6% during this period, a substantially high speed when you compare it with other industries.

Data scientists and analysts help companies in tackling modern business problems by gathering insights from large amounts of data. From social media to finance, companies of various industries rely on these experts to utilize big data effectively.

Many experts predict that over 14,000 jobs will become obsolete by the end of this decade. Most of these jobs will be lost to automation and changing industry trends. Hence, it’s vital for you to pursue a career in a field that keeps up with the industry’s demands and offers long-term job security.

Data science is quite a new sector. Joining data science courses and starting a career early in this field would help you greatly in advancing your career. If you check the data science course details of most programs, then you’d find that the average data science course fees are very reasonable.

Jobs with great pay – data science and analytics attract lucrative salaries

There’s a lot of demand for data science professionals. So, companies pay lucrative salaries to eligible professionals for these roles. Some of the highest-paying jobs you can get after completing the data science course in India include Data Analyst, Business Analyst, and Data Scientist.

For example, the average salary of a business analyst ranges from INR 2.73 lakh per annum to INR 10 lakh per annum in India. A proper data science course with placement support can help you start your career right away with such roles.

How to pursue a career in data science and analytics

Pursuing a career in data science and analytics can help you tremendously. You can open multiple doors of opportunity for yourself by developing the required skills for this industry.

The best way to learn about data science and analytics is by joining a data science course in India. The best data science courses in India will provide you with dedicated guidance and mentorship, along with a structured curriculum to study from.

Most of the attractive data science courses have quite reasonable fees. Some of the important skills you can learn in an online data science course in India are predictive analysis, machine learning, R, statistical analysis, and plenty of other data science skills.

Joining an online data science course in India would be perfect right now as it allows you to study from your home without going anywhere.

Conclusion

We hope that you found the above article on the opportunities of data science and analytics useful. You can easily start a career in these fields by joining a data science course with placement support.

Visit our site to get additional data science course details and learn more about the best data science courses in India.

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

Posted on by Imarticus Learning
Reading Time: 3 minutes

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

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

Data Science CoursesWhat is Data Science?

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

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

What is Artificial Intelligence?

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

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

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

What is a Prodegree from Imarticus?

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

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

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

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

Conclusion

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

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.

Related Articles:

Analytics & Data Science Jobs in India 2022 — By AIM & Imarticus Learning

The Rise Of Data Science In India: Jobs, Salary & Career Paths In 2022

Pursue a Career in Data Science: Why Is This The Perfect Time (COVID – Pandemic)?

Posted on by Imarticus Learning
Reading Time: 2 minutes

In a recent article published by LinkedIn, the organization reported a 25% increase in the number of data science professionals in India alone. On a global scale, this number is close to 37%. If you have been wanting to pursue a career in data science, then now is the right time to chase that dream, and in today’s article, we will tell you why?

Let’s get started.

Why Should You Pursue a Career in Data Science in 2020?

Post the COVID-19 crisis, the world has shifted to a completely remote work environment, and as predicted, the amount of data that is available now for collection has increased rapidly. As companies keep collecting a variety of different data sets, the need for expert data scientists are swiftly on the rise.

Career in Data Science in COVID 19 PandemicThe key concept behind this rise being, companies, need experts to analyze the data that is being collected and conclude decisions which not only contribute to short term gains but also long term business advances for the business.

Along with this, since the demand for such roles is on the rise, companies are willing to spend more to hire the best talent in the market, thus increasing the overall pay of the profession.

 

Some of the most common designations you can explore in this field include the following:

  1. Data Engineer
  2. Data Analyst
  3. AI Product Manager
  4. Data and Analytics Manager
  5. Database Administrator
  6. Business Analyst

How to Get Started With a Career in Data Science?

Now that you know the why of why you should pursue a career in data science, along with a few of the designations you should pursue, let us explore how you can kick start your career.

21st century is one of the hottest times to pursue a career in data science since millions of job openings are being posted on the regular. While having a degree in science or engineering is a good foundation to pursue a career in data science, if you truly want to stand out, one of the best things to do is to get a professional certification from any of the top recognized companies.

While one of the most obvious advantages of having a certification in data science is the edge it gives you over thousands of applications; the underrated advantage is making it easy for recruiters to spot your talent and choose for the right role.

Conclusion

2020 is a cornerstone in shaping how big data analytics will be used in the future, and thus the decisions you make today on how to pursue and shape your career in data science will determine your success in the future. With technologies such as big data, machine learning and artificial intelligence being readily used by the small to medium scale businesses around the world to increase their capabilities, the need for skilled professionals, who can swiftly analyze this data and extract meaningful insights will be on a constant rise.

In 2020, if you choose to pursue a career in data science, it can easily be estimated that your future will be secure for the next generation.

We offer data science courses at our centers in Mumbai, Thane, Pune, Ahmedabad, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.

The Increase in Data Science Education in India, Explained!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Data science jobs and related roles are increasingly becoming some of the most coveted jobs across industries. This is partly due to how the data science field can cut across industries to be of value, but also thanks to its resilience in tough times and the needs it has responded to.

Data ScienceOver the past few months, colleges and academic institutions have seen a significant rise in enrollment in data science courses in India. The choice is wide– potential students can choose from full-time, part-time or short and snappy online courses to either fill a gap in their skillset or experiment outside their comfort zones.

Although the potential for online learning had been realised by many even a few years ago, certain situations contributed to its exponential rise in recent times.

WFH and Remote Learning During the Coronavirus Pandemic

As lockdowns and shelter-in-place restrictions were imposed on countries all over the world, schools and colleges also had to pull down the shutters. Learning was taken online; in many institutions, exams and lessons were replaced by the opportunity to take online courses that otherwise wouldn’t have been accessible. Whether as a result of this or to fuel this trend, online education providers also reduced or waived off subscription fees and made certain courses available to all regardless of budget or geographies.

As a result, there was a surge in remote and online learning, not just from universities that students were enrolled in but also from coveted universities on the other side of the world. With the demand for data scientists expected to increase, professionals see new opportunities for growth. This, in turn, fueled the desire for upskilling and even pivoting careers as the economy slowed down.

Exposure to Global Universities and Opportunities

Online learning has made courses available in virtually any country from international universities and institutions. By making education accessible globally, online learning significantly increases the scope of the curricula as well as the teaching standards. Another benefit of this exposure is also the ability of graduates and professionals to connect with industry experts in other countries.

Data Science

Enrolling for data science courses in India that are offered by global universities is also a fantastic learning opportunity.

It exposes students to data science landscapes in other countries as well as lays bare the scope and possibilities they have well within their reach.

Once countries open up and travel restarts, students might also consider physically enrolling in these universities to explore topics further. Having a certificate or two in your portfolio indicates to the interviewer or the recruiter that you are interested and have done preliminary research which has only served to whet your appetite further.

Completely Online Courses

Until very recently, full-fledged online courses weren’t popular or even encouraged by governmental departments in India. Indian universities and colleges have not been permitted to deliver over 20 per cent of a degree online for several years. However, in the first move of its kind, the government gave the green signal for fully online courses in order to democratize education and erase barriers to learning caused by transport, accommodation and overall access.

The approach to fully online degrees is still cautious and restricted to particular subject areas. That said, it is still a welcome shift, especially for those looking to find data science jobs but lacking the access to opportunities that a lot of metropolitan cities and countries enjoy.

Conclusion

Online learning has significantly cut down barriers to entry that involve finance and access. It is a welcome step towards democratizing knowledge and making certain domains of the job market accessible to virtually anyone with a smartphone and a stable internet connection.

Seeing as data science jobs are set to increase in number, now is the ideal time for this surge in data science education, so that students are well-prepared for roles of the future.

Do You Need a PhD to be a Data Scientist?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Today, data scientists are the emerging assets for companies, and it is imperative for candidates to possess the required skillsets to do wonders in this worthwhile career. But that being said, many people are in a dilemma whether they need a Ph.D. to become a data scientist.

In order to ensure efficient workflow in data science projects, companies are looking for Ph.D. candidates, turning a blind eye to the challenge of the skill gap.

Although Ph.D. candidates will gain an edge over other non-doctorate candidates with regards to knowledge and exposure, a Ph.D. degree in the field of data science will not ensure results due to the ever-evolving technology ecosystem.

Data science jobs ceased to exist a decade back, and anyone rarely opted for a Ph.D. degree in data science. As such, there were a limited number of data scientists who used to hold a doctorate in data science. Apart from that, until lately, several universities did not even provide courses to get trained in data science, which further triggered the scarcity of skilled candidates for the jobs.

Research reveals that there are over 4000 jobs for data science in the US, providing abundant opportunities for Ph.D. aspirants to pick out. As such, thinking about pursuing a Ph.D. degree in data science is not mandatory. Moreover, experts suggest that majority of challenges that companies confront do not require Ph.D. candidates.

Current Situation

Looking at the fast-paced changes in AI and data science spaces, novel technologies make headways, and numerous approaches get outdated, a Ph.D. in data science requires around 5-7 years.

For some people, TensorFlow, Jupyter Notebook, and PyTorch have only become mainstays in the past few years. Thus, these tools would not make into the data science courses in universities, and hence, myriads of candidates enrolled for Ph.D. in data science in 2014 or 2015, would not be skilled in new technologies that are comparatively commonplace now.

As a result, a Ph.D. degree in data science does not guarantee a skilled aspirant.

Data Science: Skills vs. Ph.D.

Skill gains more preference versus certificates in the data science landscape, which is witnessing cut-throat competition. Case in point, on Kaggle, wherein developers from across continents compete to win the challenges, loads of developers hold a firm grip sans having a Ph.D. degree in data science.

At present, there are several platforms that numerous companies can leverage to figure out the expertise of data scientists rather than looking for Ph.D. candidates. Data scientists, unlike other development roles, are tutored to resolve real-life problems on hackathons such as StackOverflow, Kaggle, and GitHub that are organized on a global level. As such, their performance on such platforms says a lot.

Apart from that, data science developers are using media platforms – LinkedIn, Medium, and Twitter – to display their proficiency in data science. Furthermore, a proven track record is what recruiters are giving preference to rather than a Ph.D. degree holder in data science

Online Courses Fill the Bill

Following the latest fad, the data science industry is seeing a colossal rise of e-learning channels and the students opting for them, reflecting the potential of bridging the skill gaps in the data science and AI landscape. As per the Analytics India Magazine, leading data scientists have gained expertise in data science careers following appropriate practices, mostly enrolling in online courses. They believe that one can reach new heights without a Ph.D. in data science.

Looking Forward

So do you need a Ph.D. to be a data scientist? Well, it depends. Research in the data science field is crucial as it brings new techniques on the table, streamlining the workflow. However, it is not a must as not all companies are much into research.

You can be a great data scientist if you apply conventional methods to solve challenges with data science. That is why it not quintessential for you to have a Ph.D. in data science.

A Day In The Life Of A Data Scientist!

Posted on by Imarticus Learning
Reading Time: 3 minutes

The data science field holds immense career potential, yet you must be thinking, what actually do data scientists do the entire day?

To provide you deep insights into data scientists’ usual tasks so you can imagine yourself in that role and decide if the time is ripe to get trained for it, we have gathered some insights for you.

No Such Typical Day

If you ask somebody working as a data scientist about their typical working day, he/she may burst into laughter after listening “typical”. For those who are adaptable and flexible, and love to do various responsibilities, then a typical day of data scientists should fit them just fine. While these workdays are subject to changes, some essence of the day stays as it is – working with people, working with data, and working to stay abreast of the field.

Data is Everywhere

Given the job role, it is no surprise that data scientists’ regular tasks hover around data. A major portion of their time is consumed in collecting data, analyzing data, processing data, yet in several ways and for several reasons. Data-centric responsibilities that data scientists may come across include:

  • Pulling, merging and assessing data
  • Searching for trends or patterns
  • Leveraging numerous tools such as Hadoop, R, MATLAB, Hive, PySpark, Python, Excel, and/or SQL
  • Developing predictive models
  • Striving to streamline data issues
  • Developing and testing new algorithms
  • Creating data visualizations
  • Gathering proofs of concepts
  • Noting down outcomes to share with colleagues

Interacting With a Broad Range of Shareholders

This may appear as if it has a minor role in data scientists’ day, yet the otherwise is true as eventually, your job is to ward off issues, not create models.

It is paramount to remember that even though data scientists are playing with data and figures, the reason for this is fueled by a business requirement. Having the ability to view the larger picture from a department’s perspective is vital. So is being able to comprehend the tactic behind the requirement, and to assist people comprehend the consequences of their decisions.

Data scientists dedicate their time in meetings and replying to emails, just like most people do in the corporate sphere. Yet, communication skills may carry greater importance for data scientists. While attending those meetings and responding to those emails, as a data scientist, you should be able to elucidate the science behind the data in layman terms, as well as able to comprehend their issues as they view them, not from data scientists’ viewpoint.

Staying Updated with Changes

Both, working with data as well as with others will account for a notable portion of the day if you decide to pursue a career in the field of data science. The remaining of your day will be captured staying updated with the data science industry. New insights arrive on a daily basis as other data scientists craft a solution to fix an issue, and then extend their new finding.

Data scientists, thus, normally dedicate a portion of the day going through industry-centric articles, newsletters, blogs, and discussion boards. They may attend conferences or connect online with various data scientists. Moreover, occasionally, they may be the ones to extend new insights.

As data scientists, you do not wish to waste time starting from scratch. If anyone else has a better solution to fix an issue, you would like to know. Staying updated with changes is the sole way you will have the ability to do that.

Now the question arises, how to become a data scientist? Well, the good news is you do not have to worry much about it. There are loads of resources available at your doorstep in the form of online courses and e-books. So, if you want to pursue a career as a data scientist, grab these resources and get yourselves enlightened.

How To Get Into Data Science From a Non-technical Background?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Whatever you wanted to learn can change with time, and it seems like you have decided to dive into the field of data science. It is a vast field that is growing every day. In today’s modern era, every person can be defined with data, every person is data, data is strength.

Every day uncountable MBs of data are produced and so there is a demand of data scientists. If you have mathematics/statistics in your backdrop then it becomes easy to aspire for a data scientist. But, if you are from a non-technical background, more hard work would be required from you, and you can join the field of data science.

Without big data analysis firms wander into the world like a man in the woods. There is a demand for data analysts nowadays, so if you enhance your skills to the anticipated level, jobs would be hunting you. If you don’t have the pre-requisite of statistics or programming, the first and foremost thing is to enroll yourself in such courses. Udacity, KDnuggets, Dataquest, etc. are some platforms that can provide you online courses in data science.

They also provide certification which proves to be helpful when you toil for a job. But remember, education should also come. If you keep your focus maintained then data science is a very interesting field. Certification is secondary, if you have the knowledge, your value will automatically increase in the market. All the talks of big data and analysis, not many people understand it. It is a trendy field so many talk about it just for the sake of talking. Real knowledgeable people are valued when we are talking about data science.

Once you have enrolled yourself in a course, you can find new ways to brush up your knowledge. You can dive into real-life data analysis projects. There is much free data sets out there for various kinds of projects like criminal records, census reports, cause of death count, etc. They are available on the internet and you can use them for better interpretation and analysis.

Indulging yourself in a project will enhance your statistical skills and practical knowledge will help you when you will be seeking for a job. Also, you can join various data science communities and learn what is best suited for you. You can also follow data scientists on different social media sites to learn their perspectives on data science.

The field of data science is vast, but you have to gain knowledge. Without knowledge, nothing ever happened and nothing will. Since you are from a non-technical background, there is no substitute for hard work. If you have a mentor in this field, then it is even greater. Because sometimes, learning a new technology is not going to be easy.

Proper guidance will help you in investing your time for the right thing. You will also want to learn programming languages as an analysis of such large sets of data is done with the help of machines. Hadoop and R languages are widely used for data science and analysis.

They help in parallel usage of data at multi-points. Keep yourself updated with news and blogs so that you know which thing is in demand nowadays. The statistical approach to data science will also require a lot from you including real-time computation. And at last but the least, keep trying.

Once you have the knowledge and the skills, keep looking for the job until you find it, yes, it is going to be hectic but that’s how everyone starts! And who knows, one day you can provide jobs in data science if you keep learning and keeping your focus towards your goal.

What Is A Data Scientist’s Career Path?

Posted on by Imarticus Learning
Reading Time: 2 minutes

The Data Career trajectory is probably the hottest career option you can do right now. As Glassdoor’s latest report shows, the $ 108,000 base salary is not only attractive to job seekers, but the Data Science career also boasts 4.2 out of 5.

Data Science Pipeline

A data science project is a whole process. It is important to understand this fact to get out of the labyrinth of data science.

Data science is not magic!

Embarking on a series of steps systematically first, the project goals are reached. Have you identified attractive business issues or market opportunities? You need to clarify what your company is trying to help you gain a competitive edge.

Next, you need to know where to collect data, plan resources, and coordinate people to do their job. The third part is data preparation. You must clear the data and investigate it carefully. The association begins to appear and the sample and the variable are corrected. The next step is to create, validate, evaluate and improve the form.

Finally, you need to communicate your team experience in the data science process. The data must be compelling and compelling. In the final reporting stage, visualization is essential to telling the complete story.

What did you learn?

At Imarticus Learning, the role of the data science team is not exclusive technology. Programming and statistics are essential to the basic steps in the Data Science Training, but contextual skills are essential to the planning and reporting stages. 

A role in data science

In fact, the role of data scientists is a common part of many different fields. Data scientists are highly capable professionals who have a big picture and are a data programmer, statistician, and a good storyteller.

However, the data science team counts people with different roles, all of whom contribute in different ways. If your career path in the data world is your ultimate goal, there are many ways to reach it.

For example, as an analyst, your data science career will be involved in day-to-day tasks that focus on data collection, database structure, modeling and execution, trend analysis, recommendations, and storytelling. Business intelligence (BI) analysts, on the other hand, should be able to see the trend and get an overview and state of the business unit in the market.

BI analysts usually have experience in business, management, economics or similar fields. However, you should also “interact with data”. BI analysts process a great deal of information and spend most of their time analyzing and illustrating data collected from multiple sources.

Are you fascinated by marketing issues? Marketing analysts are a special kind of data analyst. However, their main competency is associated with analyzing customer activities data with the help of special programs and not involved in programming or machine learning.

Data Science at Work

Data science training equips you with the skills for suggesting smart solutions for performing machine learning for beer and food molecules. Preparing beer with the right molecules to match the most popular meal ingredients on the market will be fun and make money. Imagine the perfect mix of top-selling beers like burgers and tikka masala!

Understand The Random Forest Model in Data Science

Posted on by Imarticus Learning
Reading Time: 3 minutes

Data science is used for predicting smart solutions to modern problems nowadays by processing big data. But that processing is a very tedious job. The data produces in such cases are large, unstructured chunks of data often referred to as raw data.

This unstructured data has to be classified and separated into different clusters of data. Random forest model is a classification algorithm offered by Data Science Training that arranges data in a structured way using decision trees. It is ranked highly among other classification algorithms because of its high performance and efficiency. Let us know more about this algorithm and its working.

To dive deeper into this algorithm, we must have a pre-requisite about decision trees. A decision tree is a way of dividing a data set into different categories/classes by mapping the elements of the given data set in a tree based on decisions and at each level of the tree a question is asked which leads to the branching of the tree into several categories. For example, suppose we have a data set of 1s which are of two colors and are either underlined or not. Now, we have to make a decision tree and classify the given data set into various categories.

Given data set = ( 1 , 1 , 1 , 1 , 1 , 1 , 1 )

Decision tree –

1  1  1  1  1  1  1

1  1  1                         1  1  1  1

1  1                                   1

As you can see in the above figure that on the first level of the tree a question has been asked i.e. is 1 red? Based on this question the 1s are divided into two categories and then based on whether the 1 is underlined or not, the branching of nodes is done on level 2. So, it is a very simple yet powerful means of data classification and helps even more when the data set is huge.

When the data is large in volume then a lot of individual decision trees are made from the given data set. These decision trees have classified the data depending on their attributes and characteristics. Once the trees are made, they are brought together to form a forest of trees which has different sets of data. These trees act as a community and serve their purpose of data classification. Together, they perform very well and give far better results as compared to other models/algorithms.

These individual trees in the forest perform as an ensemble which is further used for predictive analysis and other data science operations. The outcome of this model is uncorrelated. Uncorrelated outcomes do not affect each other and as we have many trees, the accuracy of our prediction increases. There are ways to ensure that the trees don’t affect each other i.e. the trees should be uncorrelated to each other. It is done in two ways that are feature randomness and bagging.

In bagging, different trees are made by slightly changing the sample data set which is random, as the decision trees are very sensitive even to a slight change in the data set. This ensures that the trees are uncorrelated. In feature randomness, whenever we branch the decision tree, we use that property of the data set which results in the highest number of branches. If we have numerous possibilities then we predict with more accuracy using each and every value the given data set can possess.

Conclusion

The forest serves as a great deal to analysts and is widely used. Each decision tree in the forest is made by changing the data set. The change in the data set is done through random values that replace the original data set and thus create more possibilities and a way for better and accurate prediction. This article was all about the random forest model for data classification in data science.