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Tag: Data Science Training

A Step-By-Step Guide For A Smooth Career Transition To Data Science!

Posted on by Imarticus Learning
Reading Time: 2 minutes

Are you an electronics engineer looking to move to a data science career for a better-paying job? Or are you a commerce student who has recently taken to data science? This simple data science career guide will help you transition to the field and become a data scientist.

Step-By-Step Guide to Becoming a Data Scientist

Follow these four steps to move from any field to a data science career.

Step 1 – What field are you in right now?

The first and the most important step in this process is to understand your current academic and professional standing.

Are you still a student? Or are you working at a job in another field right now?

Step 2 – Gain Data Science Qualifications – Study or Work

If you are still studying, you have two options. Either wait and complete your existing degree (especially if only a few months are left) or immediately switch to a data science course. The latter can be a difficult situation, but when you look at the number of years you will further waste in that degree, it will make sense to you.

As a student, your goal should be to gain at least a degree, a certificate course, or a diploma in the field. Without an educational qualification, becoming a data scientist will be challenging. Then you will need a lot of hands-on experience and projects in your CV to prove that you are an able data scientist.

If you are working, the transition will take a bit longer for you. As noted, gaining some education in the field should be your higher priority. In case it is not possible due to financial or any other reasons, you can look at online courses.

Whatever you do, make sure you have some experience – academic or professional – before you move to step 3. In an ideal scenario, a little bit of both will improve your chances considerably.

Step 3 – Create a Solid Data Science CV

Making a data science career for yourself should start with you making a CV of yourself. Once you are satisfied with your own academic and professional qualifications, a CV is your pathway to reach out to potential employers.

Here is a list of activities that you need to do one after other:

  • Create a detailed CV that is no longer than two pages
  • Update and build your LinkedIn and Naukri.com profiles (among other job websites)
  • Create a web portfolio that lists our your academic and/or professional projects in the data science field

Once you have any or all of these three, move to the next step.

Step 4 – Apply for a Dozen Jobs Daily

The fastest way to build a data science career is to aggressively apply for jobs. You need to send out at least a dozen applications – via LinkedIn, Naukri.com, company career pages – every day to even get a response.

The Covid-19 situation can be a challenge here but staying aggressive is the only solution. Since a data science career entails working on a computer, employers are still looking for talent.

Following these four critical steps can pave your way to the goal of becoming a data scientist. It is not as easy as it looks and will require a lot of hard work on your part. Nor is it a quick way to get into a data science career.

On average, you can expect to spend at least 2 to 4 years in this process if you are looking for a successful transition regardless of your current professional situation.

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.

Best Data Science Institutes in India!

Posted on by Imarticus Learning
Reading Time: 4 minutes

According to Glassdoor, Data Science was the highest-paid field to get into! The demand for data science is very high, while the supply is too low.

Have Some Questions? Explore more here!

How long does it take to get Tableau Certification?

With data becoming one of the core values of many organizations in the world, having tools that work best with data is the key. One such tool is Tableau. It is the fastest and powerful software used for data visualization. It simplifies raw data into a comprehensible format.

With a skill shortage in the field of Data Analytics, Tableau can help build a workforce of talented individuals who can contribute to the industry. For this, we need to understand how long it takes to get a Tableau certification.

There are three types of tableau certifications –

Tableau Desktop Certification, Tableau Server Certification, and Delta Exams.

Tableau Desktop Certification

In this, the certification levels are Tableau Desktop 10 Qualified Associate (2 hours), Tableau Desktop 10 Certified Professional (3 hours), and Tableau Desktop 10 Delta Exam (1 hour).

E-learning and Distance LearningTableau Server Certification

In this, there are certification levels which are Tableau Server 10 Qualified Associate (1.5 hours), Tableau Server 10 Certified Professional (7 hours), and Tableau Server 10 Delta Exam (1 hour).

The time taken depends on the certification level based on the qualification and experience in using Tableau, which may vary from 1 hour to 7 hours.

Which Institute Is The Best For Data Science In Mumbai?

Data science is the latest trend in many organizations that work with data every day, especially for analytics in order to boost their sales and recognize loopholes in their operations.

There are several renowned institutes that offer a Data science course in Mumbai. Some of them are NMIMS School of Business Management, ISME School of management and entrepreneurship, Imarticus Learning Private Limited, Aegis School of Business, Tata Institute of Social Sciences, and SP Jain Institute of Management and Research.

Imarticus Learning is a professional educational institute that focuses on bridging the gap between industries and academics. It builds powerful models and generates useful business insights and predictions for businesses.

The data science course at Imarticus helps the learners to gain job-relevant skills like R, Python, SQL, and Tableau, gain industry certification, experience 360-degree learning which comes with turbo-charged curriculum,  and hands-on experience showing real-life problems in the business world, and video case studies. The course involves expert inputs and certification endorsed by KPMG, a global leader in Artificial Intelligence and Data Science consultancy.

Which institute is the best for data science in Pune?

Data science is the latest trend in many organizations who work with data every day, especially for analytics in order to boost their sales and recognize loopholes in their operations.

There are several renowned institutes that offer Data Science training in Pune including Imarticus Learning Private Limited. Imarticus Learning is a professional educational institute that focuses on bridging the gap between industries and academics. It builds powerful models and generates useful business insights and predictions for businesses.

The data science course at Imarticus offers a broad exposure to key concepts and helps the learners to gain job-relevant skills like R, Python, SQL, and Tableau, gain industry certification, experience a 360-degree learning which comes with turbo-charged curriculum,  and hands-on experience showing real-life problems in business world, and video case studies.

The course involves expert inputs and certification endorsed by KPMG, a global leader in Artificial Intelligence and Data Science consultancy. The instructors and trainers will guide you from the beginning till the end of the course, and you can stay in touch with them and continue to follow up with useful guidance even after completion of the course.

Which is the best training institute for Data Science coaching in Ahmedabad?

Data science is the latest buzz in the organizations who deal with data on a daily basis, especially for data analytics or data science to boost their sales and recognize loopholes in their operations.

There are several renowned institutes that offer Data Science Classes in Ahmedabad including Imarticus Learning Private Limited.

Key to Inclusive Leadership

Imarticus Learning is a professional educational institute that focuses on bridging the gap between industries and academics. It builds powerful models and generates useful business insights and predictions for businesses.

The data science classes at Imarticus help the learners to gain job-relevant skills like R, Python, SQL, and Tableau, gain industry certification, experience a 360-degree learning which comes with turbo-charged curriculum,  and hands-on experience showing real-life problems in business world, and video case studies. The course involves expert inputs and certification endorsed by KPMG, a global leader in Artificial Intelligence and data science consultancy.

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 Are The Steps To Become a Data Scientist From a Non-Technical Field?

Posted on by Imarticus Learning
Reading Time: 2 minutes

A data scientist is a professional who is in the authority of collecting, analyzing and understanding large amounts of data. This job deals with advanced analytics technologies like machine learning and predictive modeling. Some of the basic responsibilities of this role include collecting and analyzing data, using different types of reporting tools to detect the trends, patterns, and relationships in various data sets. In today’s scenario, a data scientist is one of the best professions to pick as a career.

Scope of a non-technical person as a data scientist

To understand the scope, firstly, the term non-technical person should be defined. It refers to a person from a non-engineering background. Basically, the person may be from any educational background but should have the right approach. He or she should be ready to put in a lot of time and effort. Self-motivation is a must to mentally prepare oneself to learn whatever is essential to become a successful data scientist.

Try enrolling in a good data science course to give shape to the career. Eventually, you will realize the time invested in learning the matter will prove to be one of the best long term investments in your career.

Steps to become a data scientist from a non-technical field

Coming from a non-technical background, to become a successful data scientist following this six-step guide can prove to be really helpful:

  • Broaden the skill level with the help of a planned course – For those who are a fresher in the area of data science, they should enroll in a well-structured course. An ideal syllabus should cover the basics of the programming languages R and Java, Big Data handling, deep learning a part of machine learning, data visualization, probability, and statistics.
  • Get in touch with some mentors in this field – When venturing into any new field, a mentor’s guidance plays an important role in guiding one through the best path. Getting in touch with an experienced person helps in building a network and getting valuable lessons.
  • Try to attend every event held in the town or online – Attending such events are a great way to gather information from industry experts in this In-person events are better as that has a scope of open conversations.
  • Appear for mock interviews – Only preparations are not enough. When one is looking for a job opportunity in data science, having a basic idea of what the hiring managers are seeking, is very important. Attending mock interviews is the best way to measure one’s expertise level.
  • Never compromise with the basics – If one is serious about a career in data science, then familiarity with any one of the basic areas is really important. This helps in tuning the intellectual capacities of analyzing and interpreting data.
  • Stay open to learning new things – In the field of data science, remember there is no end to learning. So, don’t hesitate to learn new things from your peers or seniors as you move ahead in your career path. But remember there should be a methodical approach in whatever you do. Keep strengthening your basic knowledge and read books related to data science.

Conclusion

Following these simple steps will make one’s transition from a non-technical field to the domain of data science not only interesting but also hassle-free. To get the real feel of this process of shifting, it is recommended that one checks out the online videos of some real-life examples of people who made it possible and are successful data scientists today.

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.

Introduction To Python Set and Frozen-set Methods

Posted on by Imarticus Learning
Reading Time: 3 minutes

Introduction To Python Set and Frozen-set Methods

Python is a widely-used programming language. No doubt that learning Python will bring you better job prospects. The advantage of Python is that it is generally smaller than other languages like Java. As a programmer, you will need to type only fewer indentations, which makes them highly readable highly. Many global market leaders like Google, Facebook, Amazon, Instagram, etc. are using Python. Learning Python will help you find a career in machine learning as well. A program widely used in GUI applications, image processing, multimedia, text processing and many more, Learning Python is highly advisable if you are looking at a career in IT.

Sets in Python

Sets in Python represent a collection of data. This unordered collection is mutable and iterable. It doesn’t have duplicate elements. The Python set is similar to the mathematical set. Using a set is recommended over a list because the set has several advantages. Based on the hash table, it is very easy to check if a particular element is included in the set. This is not possible if you use a list.

Properties of a Python set()

There are no parameters to create an empty set. If you create a dictionary using a set, the values will not remain after conversion. However, the keys will remain.

Sets have many methods:

  1. Method of addition (x): This method is used to add one or more items to a set if currently it is not included in the set.
  2. Method of union (s): This method is used to unite two sets. The operator use is ‘|’, which is similar to set1.union(set2)
  3. Method of intersection (s): This method is used for the intersection between two sets. You can also use the ‘&’ sign as the operator here.
  4. Method of difference (s): This method is used to return a set that contains replicates the elements present in the invoking set, but not present in the second set. The operator used here is ‘-‘.
  5. Method of the clear set (): This method is used to empty the whole set.

Operators for sets and frozen sets

Operator Function
key in s containment check
key not in s non-containment check
s1 == s2 s1 is equal to s2
s1 != s2  S1 is not equal to s2
s1 <= s2 s1is subset of s2
s1 < s2 s1 is a proper subset of s2
s1 >= s2 s1is superset of s2
s1 > s2 s1 is a proper superset of s2
s1 | s2 the union of s1 and s2
s1 & s2 the intersection of s1 and s2
s1 – s2 the set of elements in s1 but not s2
s1 ˆ s2 the set of elements in precisely one of s1 or s2

Frozen sets

Frozen sets, unlike the set, are immutable. Once created, the frozen set remains the same. It cannot be changed. This is the reason we use a frozen set as a key in Dictionary. It accepts an iterable object as its input and makes them unchangeable. However, it is not guaranteed that the order of the element will be preserved. In a frozenset, if no parameters are passed to the () function, an empty frozen set is returned. If you change the frozenset object by mistake, you will get an error saying that the item assignment is not supported by frozenset object.

Conclusion

Equivalent to the data set in mathematics, Python set is a data structure similar to the mathematical sets. It consists of several elements, but the order in which the elements should be deployed in the set is undefined.  You can perform addition, deletion, iterate, union, intersection, and differences on the sets.

How To Use Data Science For Predictive Maintenance?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How To Use Data Science For Predictive Maintenance?

Most businesses constantly face an issue while analyzing whether their critical manufacturing systems are performing to their full capacity while ensuring a consistent reduction in the cost of maintenance. Causes of potential concerns need to be identified early to help organizations come up with more cost-effective plans.

This is where predictive analysis fits the bill. Predictive analysis is used to predict if an in-house machine will malfunction or work correctly. Predictive analysis also helps to plan maintenance in advance, predict failures, classify failure types and recommend necessary actions to be taken after a system fails. The scope of data science is vast and predictive analysis only helps in proving that further.

Factors influencing the success of predictive maintenance

There are three factors that influence if a predictive model is going to be successful or not:

Having the right data

One of the most crucial factors influencing predictive maintenance is having enough data that helps analyze factors that may lead to failure. Additional system features like operating conditions, technical properties also need to be taken into consideration. Additionally, it is also important to make an inventory that will help note the kinds of failure that can occur, and which are the ones that can be predicted.

If at all there is a failure, what the failure process might look like. Having the right data for predictive maintenance also helps understand which parts of the system may have failed and how improvement in terms of performance can be brought about. A system has a vast life span of over a couple of years, which means data collection needs to be done over a couple of years to ensure correct statistics are taken into consideration. A basic data science course will teach you everything about data collection methods.

Framing a predictive maintenance model

The next step is to decide the best modeling strategy for the collected data and how it can lead to the desired output. While there are always multiple modeling strategies to choose from, a predictive maintenance framing strategy should keep a couple of things in mind:

Desired output for the model

Quantity of data collected

Measurements required to predict is a system will succeed or fail

Advance time to predict before a system fails

Setting performance targets for the model such as accuracy, precision and more

Evaluating predictions in predictive maintenance model

A predictive maintenance model predicts whether a system will succeed or fail, what are the conditions under which it might fail and how to ensure that it runs smoothly amongst others. After a predictive model is built, it gets highly essential to analyze how accurate the predictions have been, under what circumstances has the model been able to predict a certain failure or success conditions, and what can be done to combat the same.

Usage of data science in predictive maintenance

Using data science in building predictive maintenance models goes a long way and has its own set of advantages. Here is a lowdown of the ways in which data science has proved to be beneficial for the same:

Minimizing the cost of maintenance

Data science helps understand when to repair a system or machine and prevents unnecessary expenditures by predicting how frequently maintenance should be done.

Root cause analysis

Data science digs deeper into the causes of high failures and understands why systems malfunction occasionally. It also helps suppliers deal with the potential supply of materials accordingly.

Reduce unnecessary downtime

Predictive maintenance is required to predict if an ad when systems might malfunction. A prior data science analysis only helps in lessening the risk of unforeseen disasters.

Efficient planning for smooth operations

Data science ensures that there is no time wasted in fixing systems that are not vital or replacing equipment that has no usage. This way it plans labor efficiently and also ensures that the operations of the business run smooth.

A course in predictive maintenance and building models is an interesting choice for professionals enthusiastic about pursuing a data science career.