What is the Random Forest?

What is Random Forest?

Random Forest is a versatile and powerful supervised machine learning algorithm. Basically, you can think of it as an ensemble model of decision trees. Random Forest can be used for both regression and classification problems in Python and R.

Before delving deeper into Random Forest, you need to understand the associated concepts first. So, here’s a snapshot of the related concepts.

Supervised machine learning

Machine learning is divided into three key categories namely supervised learning, unsupervised learning, and reinforced learning. In supervised machine learning, a training dataset is used to create the algorithm. Many different examples of inputs and outputs are used to train the algorithm so that it can learn how to classify fresh input data and predict future outcomes. 

Regression and classification

Both regression and classification are parts of supervised machine learning. We use regression algorithms to predict the output, which is a continuous or real value such as age, salary, price, etc. Classification algorithms are used to classify or predict the categorical output. For instance, an email spam filter is able to categorize every email into either of two classes – spam or not spam.

Decision trees

A decision tree is the most effective machine learning modeling technique widely used for classification and regression problems. When it comes to finding solutions, decision trees make hierarchical, sequential about the outcomes variable depending on the predictor data. There are three building blocks of a decision tree – a root node, branches, and leaves. To make it simple to understand, you can think of a decision tree as a flowchart, which draws an understandable pathway to an outcome or decision. It starts at one point and branches off into multiple directions. Each branch of a decision tree provides different outcomes possible. Basically, it’s a set of rules that we use to predict future data.

Basically, a Random Forest is only a bunch of decision trees grouped together. However, it’s important to understand that in a Random Forest all the trees are randomly mixed together. Therefore, when you use a single decision tree, it’ll come up with a set of rules based on the inputted training dataset. But when the same training dataset is inputted in a Random Forest algorithm, it’ll select features and observations randomly to build multiple decision trees and average the results of each of them.

How to create a Random Forest using decision trees?

How to create a Random Forest using decision trees?

To do this, you need to understand how to build a decision tree first. Since the key objective of a decision tree is to make the most favorable choice at each node’s end, it’s important to avoid impurity and reach maximum purity. To develop a better understanding of how a decision tree is built, you need to learn about its building blocks. 

Let’s try to understand the following fundamental concepts.

Entropy

We use entropy to measure the disorder, information, or purity of a dataset. If a dataset has mixed classes, it has more disorder and its entropy value will be higher. Therefore the potential for extracting information from it is also higher. On the contrary, a dataset with only one class doesn’t have much potential for information extraction.

Information Gain

We calculate the difference in entropy before and after we split the dataset to assess how ideal a feature is for splitting. Information gain refers to the amount by which a dataset’s entropy reduces due to the split. We use information gain when training decision trees as it helps to reduce uncertainty in them. A high information gain refers to the removal of a significant degree of uncertainty.

Gini Index

The Gini index is another measure for purity, which’s used by the Classification and Regression Tree (CART) algorithm. It is based on Gini impurity that measures how frequently a randomly selected element is incorrectly labeled in case it was labeled randomly according to distribution. If a dataset has two classes, the Gini index’s range remains between 0 and 0.5. In case the dataset is pure, it is 0, and if two classes are equally distributed it is 0.5. Therefore, we should try to have it reach 0 because that’s where it’ll be maximally pure and minimally impure.

Now that you’ve got an overview of the key mathematical concepts that are used in training decision trees, it’s time to take a look at Random Forest.

As we’ve already touched upon, the major difference between the Random Forest algorithm and the decision tree algorithm is that in the former, segregating nodes and establishing root nodes are randomly done. 

Bagging

In Random Forests, the usual technique of bagging is applied by the training algorithm to tree learners. In the bagging technique, different samples of training data are used instead of only one sample. Here, decision trees are trained on a subset of the actual training dataset. 

Bootstrapping of the training dataset

The subsets are obtained through random feature sampling and row sampling from the dataset – a method called bootstrapping. The sample datasets are reduced into summary statistics depending on the observation and combined by aggregation. We can Bootstrap Aggregation to reduce high variance algorithms’ variance.

Variance

Variance refers to an error that occurs from sensitivity to slight fluctuations in the training dataset. A high variance will lead to model noise or irrelevant data in the dataset rather than signal or the intended outputs – a problem called overfitting. Note that in training, an overfitted model can perform well but in an actual test, it won’t be able to differentiate the signal from the noise.

The majority rule

For the same input vector, slightly different predictions will be made by slightly differently trained trees. Generally, we apply the majority rule to decide on the final output. It means the prediction made by the majority of the trees is considered as the final output.

The key advantages and disadvantages of Random Forest

The key advantages and disadvantages of Random Forest

Random Forests offer a multitude of advantages – from relative ease of use to efficiency and accuracy. In addition, if you’re a data scientist and planning to utilize it in Python, you can use an efficient and simply random forest classifier library in scikit-learn. Let’s take a look at the pros and cons of Random Forests.

Advantages

• A Random Forest is significantly more efficient compared to a single decision tree when you’re performing analysis on a bigger database.
• Random Forests have the default ability to correct for the habit of overfitting of decision trees to their training datasets. You already know that overfitting results in inaccurate outcomes. You can almost entirely resolve the issue of overfitting when executing random forest algorithms with the help of random feature selection and bagging method.
• A neural network, which mimics the way of thinking of a human brain to disclose the underlying relationship present in a dataset, is more efficient than a Random Forest. However, a neural network is much more complicated than a Random Forest. Since it requires less expertise and time to build a Random Forest, the method often proves to be more useful for less experienced data science professionals.

Disadvantages

• Like any other tool, Random Forests also come with a few downsides. Since a Random Forest comprises several decision trees, it may require lots of memory, especially for larger projects. It may make it slower compared to some other algorithms.
• Decision trees are the building blocks of a Random Forest and decision trees frequently experience the issue of overfitting, which may affect the entire forest. However, generally, Random Forests prevent this problem by default. This is because they utilize features’ random subsets and use them to build smaller trees. The processing speed might get reduced due to it but accuracy will increase.

When you shouldn’t use Random Forests

There are some situations where Random Forest algorithms may not be the ideal option. These include the following.

• If you’re working with very sparse data, Random Forests may not generate good results. Here, an invariant space will be produced by the bootstrapped sample and the features’ subset, which may result in unproductive splits affecting the outcome.
• When it comes to performing extrapolation, random forest regression isn’t the best choice. This is the reason the majority of random forest applications are related to classification.

Parting thoughts

Once you’ve developed a good understanding of these fundamentals, you should join a machine learning course to master these concepts. These courses from a reputable institute will help you learn a wide range of other skills along with the Random Forest algorithm, even if you’re completely new to machine learning.

The post Creating A Forest From A Tree: A Brief Introduction To Random Forest first appeared on Magnimind Academy.

Today, big data is being heavily used almost everywhere – from the concept of self-driving cars to healthcare industries to tech giants, just to name a few. All these requirements raise the demand for efficient data scientists to a great extent. In addition, the role of data scientists, which has been proclaimed to be the 21^st century’s hottest job, will only continue to get bigger and better in the upcoming years.

1- Things that attract people to become a data scientist

There’re enough facts that indicate a clear lack of competent data scientists for filling the increasing market demand. So, it’s no surprise that people from different fields are trying hard to step into the field and to become a data scientist. If you too belong to this league, you may be wondering what would one gain from becoming a data scientist, especially when he or she needs to spend thousands of dollars together with a significant amount of effort and time?

Let’s explore seven major advantages of becoming a data scientist.

1.1- You’ll get to enjoy more employability

In today’s data-driven world, almost every industry needs to take advantage of data in order to sustain and grow. So, knowledgeable and proficient data scientists are required by employers in every industry – from tech to healthcare to finance to automobile, and many more.

There’re thousands of vacant data scientist positions available for competent people, who can demonstrate that they’ve the credentials and skills to fill them. It’s important to note that many of these positions are high-paying, coveted jobs with giant companies. So, for candidates who’re looking to take their career to the next level, becoming a data scientist is most likely the best way to enter an extremely rewarding field. Also, for employees who’re stalled in their present position, becoming a data scientist can greatly help them move up the ladder quickly in the field where they’re already working.

1.2- You get to become non-negotiable for companies and businesses

This is one of the biggest reasons why people are striving to become a data scientist. Put simply, success in today’s business landscape heavily depends on comprehending and making the right decisions based on insights derived from data captured through different sources. Companies or businesses that don’t accomplish this at the right time will fail to stay in the competition for long and may have their business prospects severely damaged.

As revealed by various reports, there was an acute paucity of data scientists in the U.S. alone. Such a paucity is a key barrier to innovation and can easily truncate the prospect of businesses. As a result, the role of data scientists has become non-negotiable for companies looking to rise above the competition.

1.3- You get to grab the top-paying job

The emergence and prospect of big data has generated a significant number of job titles that pay attractive salaries compared to other tech-related jobs. We’ve already discussed that data scientists are needed by almost every industry. Today, the requirement isn’t only limited to the IT domain, but it has spread across all the major industries.

So, it can be said that for a data scientist, sky is the limit. In addition, data scientists who hold a Master’s degree or a Ph.D. degree can earn more than their relatively lesser educated peers and with a couple of years of experience, the pay packet would become even fattier.

1.4- You get to work in a rapidly evolving field

The field of data science is developing quickly and the necessary skillsets are also changing fast over time. Skills that are necessary to become a data scientist today may not be of that much importance tomorrow. So, as a data scientist, your learning never becomes stagnant.

On a continuous basis, you get the chance to work with new technologies and tools that are developed and encouraged. This kind of an exciting work environment is another key factor that motivates many to become a data scientist.

1.5- You get to work with multiple high-end tools

There’re very few fields where professionals get to work with multiple, advanced tools. Once you’ve succeeded in your effort to become a data scientist, you hold the chance to gain a huge amount of proficiency over multiple tools by working with them during various stages of your job responsibilities. For example, you may use SQL to capture and fetch information, process it using Pandas, develop machine learning models with the help of Python’s learning packages, use D3.js to demonstrate the results, and many more.

You may also need to use big data tools like Spark and Hadoop for performing certain tasks. This ability to work with a diverse range of high-end tools is another major triggering factor that encourages lots of people to become a data scientist.

1.6- You get the chance to work with the giants and at different roles

You can apply for jobs in giant companies like Apple, Amazon, Uber etc as a data scientist. For example, Apple utilizes big data to decide on its product features, while data science is used by Amazon to sell products by strategically recommending them to consumers. The surge pricing of Uber is a great example of how the company uses data science. Also, as a data scientist, you become able to fit into different roles when it comes to solving real-world problems.

Every single day, a huge amount of data is generated by people as well as large and small companies. To help the companies get useful and actionable insights from the captured data, the expertise of data scientists is required at different stages. Not every job in today’s world offers this level of flexibility and such vast and exciting learning opportunities.

1.7- You get to build a safe career

We’ve seen that many new technologies come and go, and that’s exactly why many people think that every technology that shines in the tech domain comes with a somewhat fixed tenure of existence. But this isn’t the case with data science. As a data scientist, you should always try to put your best foot forward to learn new technologies and tools, as and when they arrive. It has become evident that different aspects of today’s tech landscape, including data science, have started becoming automated. What it actually means is that some areas will experience automated processes but the data science field will continue to grow and the need for data scientists will continue to increase as well. Data scientists with the right skill and mindset will continue to be in demand, probably in wider fields than today.

Apart from these seven reasons, there’re some other factors as well that attract people to become a data scientist. For example, data scientists need to work with different technologies and different programming languages, and use different tools to solve real-world business problems. During those processes, they achieve mastery of various aspects. When you’ve this amount of expertise together with the knowledge of operational methods of different industries, it becomes much easier to establish own business. There’re lots of people who always dream about developing their own businesses and working as a data scientist can immensely help them in accomplishing their endeavors.

2- Things to keep in mind when you’re trying to become a data scientist

If the above reasons have convinced you enough to become a data scientist and you’re ready to start your journey, there’re some things that you should keep in mind to succeed. First of all, there’re different avenues to become a data scientist. You can take the traditional route or attend a data science bootcamp or be a self-taught data scientist. Among all these, the second route i.e. a data science bootcamp is the most popular and probably the most effective one to become a data scientist. When you’re getting enrolled in a bootcamp, go through the curriculum offered meticulously, and compare the cost, the success rate, and the post-program guidance with other data science bootcamps. Though by attending such a bootcamp, you’ll be able to become a data scientist in a shorter period of time and at a lesser cost, it’ll still cost you thousands of dollars and a significant amount of time and effort. So, it’s strongly recommended to take your pick wisely.

Conclusion

Finally, you should try to become a certified data scientist as a majority of the giant companies prefer certified candidates over non-certified ones. As a certified data scientist, it will not only become easier to get a coveted job but also greatly help you in gaining smooth promotions in the organization. Certification brings enhanced trust in a candidate as it’s believed that he or she will be able to handle the job responsibilities more efficiently than his/her non-certified peers.

.  .  .

To learn more about data science, click here and read our another article.

The post 7 Things That Convince People To Become A Data Scientist first appeared on Magnimind Academy.

It’s important to understand that though some people often use Bitcoin and blockchain interchangeably, in reality, they’re absolutely different. Put simply, Bitcoin is a form of virtual currency and blockchain is the underlying technology that makes it possible. If you’re interested in purchasing any cryptocurrency including Bitcoin, it’s important to have a good understanding of blockchain technology, why it’s being widely used, and what lies ahead in the future of it.

1- What is blockchain?

Put simply, blockchain refers to a virtual, public ledger that records everything in a transparent and secure manner. Here, all the data is held in an interconnected network of computers that’s owned and run by only the users themselves.

Although the technology was initially developed to work only in the financial arena, it has already started taking over other industries through its huge potential and slowly becoming an absolute game changer. According to some experts, blockchain technology is much more promising than the cryptocurrencies it was designed to support. With its implementation, more valuable usage opportunities are coming into existence almost regularly.

2- Major advantages of using blockchain

Here’re the major pros of blockchain for which the technology is being widely adopted across industries.

2.1- DECENTRALIZATION

World’s largest corporations use centralized data systems as well as servers. Even though these corporations implement and maintain best network practices, their data systems and servers act as attractive targets for hackers and the operations may get affected.

Decentralization offered by blockchain prevents this problem and this is why this technology is being adopted by several industries. Here, every node on the network holds the same data as the others. Thus, in the event of any node breaking down, it won’t affect the entire network.

2.2- TRANSPARENCY

One of the biggest pros of blockchain is that the technology is open source in nature. It means developers and other users have the ability to modify it as they deem fit. But what’s more important about blockchain being open source is that it makes modifying logged data incredibly difficult. At any given point of time, since there’re numerous eyes on the network, someone is most likely to see that the logged data has been modified.

2.3- SECURITY

As we’ve discussed earlier that no single actor can manipulate the blockchain network, it helps to take security to the next level. Here, whatever is performed on one node has to be replicated as well as agreed by other nodes before the action becomes active. This means a single actor like an employee’s hacked computer or a compromised employee cannot take the entire blockchain network down.

2.4- COST-EFFECTIVE

Reduced transaction cost is one of the other biggest pros of blockchain. The technology allows business-to-business and peer-to-peer transactions to be completed without any involvement of a third-party like a bank. This elimination of the involvement of middleman leads to reduced cost for the businesses or users over time.

2.5- QUICK TRANSACTION SETTLEMENTS

It’s widely known that transactions take days to settle completely when it comes to traditional banks. This delay mainly happens due to protocols in software, fixed business hours, and the difference in time zones, among others. On the other hand, blockchain technology works round the clock, which means transaction processes based on it are completed relatively more quickly.

2.6- ABILITY TO KEEP BOTH CURRENT AND HISTORICAL RECORDS

When working with regular databases, it’s quite difficult to check historical transactions. In blockchain, all historical and current transactions are recorded securely in one place. So, if you want, you can go back to check the first blocks to learn where it all started. Here, every single block of information is contained, from the very beginning of it until just a couple of minutes ago.

2.7- USER CONTROL

The reason why blockchain is particularly encouraged by cryptocurrency investors is its control aspect. Here, users and developers control all their transactions and information instead of having a third-party perform it. In other words, you can consider blockchain to be something that’s by the people and for the people.

So, you can see that blockchain technology offers a spectrum of revolutionary benefits. Altogether, they offer the most profound quality of this technology – it doesn’t require trust to interact and transact safely. With the help of blockchain technology, traditional systems can be transformed into dynamic systems where trust doesn’t matter as it’s no longer required. This facilitates establishing ownership of anything unquestionably, without the requirement of a central authority.

3- Industries that widely use blockchain technology

Blockchain technology is being steadily adopted by some specific industries to enjoy the above benefits. Let’s have a look at them.

3.1- QUALITY ASSURANCE

If an irregularity is observed somewhere along the chain, a blockchain-enabled system can lead the user to its point of origin. This makes it much easier for users to carry out investigations and execute necessary actions. You can consider the food sector, for example, where tracking origination, batch information etc is crucial in order to maintain quality and safety.

3.2- SUPPLY CHAIN MANAGEMENT

In this industry, blockchain offers the advantages of cost-effectiveness and traceability. For example, businesses can use a blockchain to track the origin of goods, their movement, and quantity, among others. This brings an enhanced level of transparency while simplifying processes like production process assurance, ownership transfer, payments etc.

3.3- ACCOUNTING

Blockchain technology can be used by accountants to record transactions, minimize human error, and increase accuracy. Blockchain-based accounting systems are also able to protect sensitive data from fraudulent or illegal manipulations.

Here, an accounting change needs to be verified by the network of nodes. Put simply, accounting heavily encircles audit trails and blockchain helps to develop a secure audit trail.

3.4- GLOBAL  PAYMENTS

This is one of the most prominent industries that can benefit from the implementation of blockchain technology. There’re some services that can effectively transfer money across the globe. Some of the major issues with them are high fees, slow operation, absence in some countries etc. When it comes to blockchain-based currencies, the fees are significantly lower. In addition, a blockchain-based payment network also boosts of speed and security.

There’re lots of other industries that rely on blockchain to a great extent. However, there’s one thing facilitated by this technology that can benefit every type of business. You’re probably aware that time-consuming contractual transactions can lower the growth of any business, especially for organizations that process a huge amount of communication consistently. With smart contracts, agreements can be validated, signed, and enforced automatically through a blockchain construct, thus helping the business save both time and money.

4- Future of blockchain

Blockchain technology comes with immense power to impact almost every industry. Based on recent developments in the field, here’s what the future of blockchain would look like.

4.1- BLOCKCHAIN AS A SERVICE

Just like other platforms help organizations to manage operations, we can expect to see the growth of new platforms with decent features for implementing a blockchain network for businesses. Blockchain as a service or Baas is a cloud-based service that can be leveraged by companies without the need for setup and infrastructure.

4.2- HYBRID BLOCKCHAIN

A hybrid blockchain is the combination of both private and public blockchain. The major advantage of a hybrid blockchain is that specific parts of the network can be kept private, and specific parts public. This would be faster and cheaper than a public blockchain, while allowing access to the blockchain advantages of the same.

4.3- IMPLEMENTATION OF BLOCKCHAIN IN BIG DATA

With features like decentralization, immutability etc, blockchain can take care of two major challenges of working with big data – authenticity and restricted access to maintain quality of data and control. Data security and privacy are unquestionably two major concerns to businesses. Centralized data is insecure to a good extent. But with blockchain, data can be managed by the users generating the data, thus leading to more data security and privacy.

In addition, data generated through blockchain needs less scrutiny as the probability of manipulating such data is extremely low. It combines cryptography and hashing to develop data stored in a series of blocks that’s almost immutable. So, blockchain with big data is expected to become a powerful tool for both small and big businesses, redefining the way data is being handled.

Final takeaway

As with any technology, blockchain also comes with some disadvantages like complexity, lack of adequate resources, and the probability of human errors, among others. However, considering the fact that blockchain ticks all the right boxes of becoming the technology of the future, there’re lots of reasons to learn it and if possible, get certified in it.

Though the technology is still confined to a limited number of industries, there’re lots of possibilities for it to expand its reach to various other domains soon enough. Though it may take a couple more years, blockchain is certainly going to change the tech domain once again.

To learn about artificial intelligence, click here and read our another article.

The post Future and Pros of Blockchain first appeared on Magnimind Academy.

As technical skills are high in demand these days, boot campshave become one of the most sought after methods to obtain the additional skills required to cover the extra mile in your career. Over the last few years, boot camps have popped up all across the globe, which promise to consider non-tech candidates and transform them into tomorrow’s technical people, often in as little as a couple of weeks or months.

1- What is a technical boot camp?

You can consider a technical boot camp similar to one of its non-technical peers, sans the physical effort. Here, someone won’t yell at you and order you to do pushups when you get an answer wrong. Put simply, a technical boot camp is a learning format designed in a manner to help participants focus intensely on acquiring new technical skills quickly. Like a non-technical boot camp, the primary principles here too are speed and high impact.

2- Key advantages of attending a boot camp

It’s important to note here that before you even plan to attend a boot camp, you should acquire a bit of self-knowledge. For example, what are you exactly looking to get out of the boot camp – an entirely new career, a better job, or new opportunities? It becomes even more crucial if you’ve never had any technical skills before. Many skilled professionals hold degrees in their fields while some others are self-taught. But the thing they all have in common is the passion for their career. If you aren’t sure whether you’ll actually want to work in your chosen field, it’s always recommended to start off with one class, get your feet wet, and then jump into a bigger program. You should understand that finding the right career path isn’t only about the pay or occupational outlook. A proper fit plays one of the most crucial roles as well.

Once you’ve done the soul-searching and determined that a tech field is perfect for you, a boot camp is perhaps the best way to delve deeper into your chosen field. Here’re the key advantages that a boot camp can provide you with.

2.1- GET INTO A NEW ROLE

It’s not the employees with a diverse range of technical skills on which tech organizations depend alone. If you’re already working for a tech employer but looking to get some help in stepping into a technical role from administrative or other creative roles, a boot camp can create the opportunity for you. All you need to do is use the connections you’ve at your workplace to determine the most suitable field to attain your goal and get enrolled in the suggested program.

2.2- OPPORTUNITY TO STEP INTO A NEW CAREER

Unquestionably, this is one of the major selling points of any boot camp. By attending a boot camp in your chosen field and acquiring the required technical skills, it’s possible to switch to an entirely new career. However, it’s important to note that the final outcome may vary based on the program, the coursework, and your personal background. What this means is that making the leap from a low-paying career to a high-paying one is indeed possible. But again, you’ve to pay careful attention to the practical aspects of attending a boot camp before making the decision.

2.3- OPPORTUNITY TO DEVELOP YOUR NETWORK

Another major benefit of attending a boot camp is you get the chance to make a significant number of connections in quite a short period of time. From your fellow participants to instructors to previous alumni – everyone can help you expand your network. Once you’ve the network developed, you can explore it for different career-related purposes such as getting industry insights and placement assistance, among others. Especially, enlarging of business area is crucial.

2.4- RELATIVELY AFFORDABLE ALTERNATIVE

For many people looking to learn technical skills in demand, a boot camp acts as an affordable alternative to regular college education. The main reason is that the price of attending a college for traditional education has skyrocketed. Also, there’re other factors like housing, books etc that don’t come cheap. You should even consider the fact that the cost of higher education is rising steadily. On the other hand, a boot camp offers a varied range of tuition – from free to part-time to full-time programs, thus giving you a lot of choices. Boot camp is indispensable piece for career planning.

2.5- EXTREMELY SHORT PERIOD OF TIME

If you want to learn technical skills and take the traditional college route, four years is the obvious tenure that you’ll have to invest. In addition, students sometimes take up to six years to complete their college education. From class overflow to taking time off to work to pay student loan debt – reasons may vary a lot. When you attend a boot camp, you can forget those years and go for an intensive training that will help you learn the skills in demand in as little as a couple of months or weeks. Courses at boot camps usually range between 4 and 30 weeks with a majority of them falling in the 9 to 12 weeks range. A boot camp seems like an obvious choice when it comes to learning the technical skills for your desired job faster as compared to college courses.

2.6- JOB PROSPECTS

We’ve already discussed the advantages of attending boot camps in terms of switching your career and switching roles. Apart from lesser time and costs, job prospect is one of the biggest benefits of these programs. You may ask – will you get hired when another applicant holds a traditional college degree? Surprisingly, many tech organizations are less bothered with a traditional degree. Technical skills and talent seem to be more important to them than a traditional degree, particularly considering that these skills can be self-taught.

Till now, we’ve been discussing the key advantages of attending a boot camp from the perspective of an individual. Tech organizations can reap a lot of benefits by helping their employees attend such a program.

In today’s tech landscape with a high attrition rate, it has become quite difficult to retain top talents. Companies are trying their best to support their best employees. Getting your employees certified can provide you with a multitude of benefits. Let’s have a quick look at them.

• It’s usually cheaper to get the existing staff certified than hiring certified staff from outside
• It improves workplace methodologies and professionalism
• It improves employee retention, morale and acquisition
• Certified employees come with greater technical skills, which result in new client engagement

Here’re the major advantages of getting your employees certified through boot camps.

2.6.1- Up-to-date knowledge

The tech landscape is changing rapidly, and thus, it has become crucial for companies to ensure that their employees undergo quality training that enrich them with up-to-date knowledge, practical hands-on experience, and new technical skills within a limited period of time. In this scenario, a boot camp is probably the most feasible solution not only because of the time constraint faced by the companies, but since these programs offer a distraction-free learning environment too and are well-equipped with crucial software and hardware that add value to the experience. Data science boot camp in the Bay Area gives companies numerous advantages.

2.6.2- Regulation compliance

Sometimes, companies find it difficult to remain compliant to new regulations. Boot camp sessions run by adept professionals help introduce the participants to new regulations quickly. It helps the participants to obtain a better and clearer understanding of compliance regulations, and learn how to remain compliant without affecting business activities.

3- Things to keep in mind when searching for a boot camp

All boot camps aren’t created equal. So, it makes sense to have a clear idea about the factors that differentiate a great program from an average one. Here’re the things that you should be looking for in a boot camp.

3.1- JOB PLACEMENT RATES

A reputable boot camp is more likely to share its job placement rates with candidates, and offer information on the types of jobs one may get after the program. Remember to focus on the latter part as you’re presumably not investing your hard-earned money to get an opportunity that pays less than or same as your current job. Event area is expanding day by day.

3.2- CAREER SERVICES

f you’re going to enroll in a career-changing boot camp by paying top dollar, you’ve to make sure that it’ll offer you an excellent career service after the program gets over. Some boot camps offer mentoring programs, alumni networks, and even job guarantees after the programs end.

Final note

When it comes to developing technical skills, these are the major advantages of boot camps, both from the perspectives of an employee and an employer. There’s one important thing that you should always keep in mind – regardless of the duration and the field of the boot camp you’re planning to attend, you’ve to keep realistic expectations at the conclusion of the program. Contrary to how a program is marketed, they sometimes make a better fit for participants with good technical skills to master an advanced skill.

To learn more about data science, click here and read our another article.

The post Benefits of Bootcamps to Develop Technical Skills first appeared on Magnimind Academy.

What makes Magnimind a class apart?

Magnimind’s mission is to educate people in the direction toward leading innovation for the overall good of all. We help people by giving them the opportunity to create new things with the advanced knowledge they impart, or adjust to new innovations and changes. By helping rewire their mindset, Magnimind Academy helps the participants build their future, thus giving them a chance to transform the way they live for the better. And this open, encouraging approach is visible in Magnimind Academy’s data science course.

Who can take up data science education?

Many other data science courses in the market need that the candidates must have the fundamental knowledge of statistics and Python, or should come from adjacent fields (like IT, advanced mathematics or statistics etc). However, Magnimind Academy welcomes interested candidates from a variety of backgrounds. So, if you have worked with coding just a little bit, or come from adjacent fields like IT or design, you can get admitted to the data science tutorials. But even if you are from an unrelated field, and just want to achieve the complete skill set that’s required to start a career in data science, you are welcome.

No worry about statistics and programming skills

Whether you want to improve and hone your existing skills for a career change, or begin a new career on data science, expert instructors of Magnimind Academy are there to help. So, if you come from an unrelated field and have very little statistics and programming skills, there’s no need to worry. You will have to take the introductory-level curriculum where you will learn everything you need to know about the fundamentals of statistics and Python.

Getting started on your chosen path is easy as you will have to pick one of Magnimind’s prep options that you can easily review on their site’s Admissions Process page. You just have to remember one thing: much more than any particular skill set, you will have to show top-notch level of perseverance and determination to tackle new challenges.

So, start your journey of having a career in data science by enrolling into Magnimind Academy’s data science degree. Here’s wishing you good luck!

The post How Magnimind is different? first appeared on Magnimind Academy.