Usually, knowing what values you should use for the hyperparameters of a specific algorithm on a given dataset is challenging. That’s why you need to explore various strategies to tune hyperparameter values.

With hyperparameter tuning, you can determine the right mix of hyperparameters that would maximize the performance of your model.

Hyperparameter tuning

The two best strategies in use for hyperparameter tuning are:

1. GridSearch

It involves creating a grid of probable values for hyperparameters. Every iteration tries a set of hyperparameters in a particular order from the grid of probable hyperparameter values. The GridSearch strategy will build several versions of the model with all probable combinations of hyperparameters, and return the one with the best performance.

Since GridSearch goes through all the intermediate sets of hyperparameters, it’s an extremely expensive strategy computationally.

2. RandomizedSearch

It also involves building a grid of probable values for hyperparameters but here, every iteration tries a random set of hyperparameters from the grid, documents the performance, and finally, returns the set of hyperparameters that provided the best performance.

As RandomizedSearch moves through a fixed number of hyperparameter settings, it decreases unnecessary computations and the associated costs, thus offering a solution to overcome the drawbacks of GridSearch.

Selecting the hyperparameters to tune

The more hyperparameters of an algorithm you want to tune, the slower would be the tuning process. This makes it important to choose a minimum subset of hyperparameters to search or tune. But not all hyperparameters are equally important. Also, you’ll find little universal advice on how to select the hyperparameters that you should tune.

Having experience with the machine learning technique you’re using could give you useful insights into the behavior of its hyperparameters, which could make your choice a bit easier. You may even turn to machine learning communities to seek advice. But whatever your choice is, you should realize the implications.

Each hyperparameter that you select to tune will have the possibility of increasing the number of trials necessary for completing the tuning task successfully. And when you use an AI Platform Training to train your model, you’ll be charged for the task’s duration, which means choosing the hyperparameters to tune carefully would decrease both the time and training cost of your model.

Final words

For a good start with hyperparameter tuning models, you can go with scikit-learn though there are better options too for hyperparameter tuning and optimization, such as Hyperopt, Optuna, Scikit-Optimize, and Ray-Tune, to name a few.

.  .  .
To learn more about variance and bias, click here and read our another article.

The post How To Tune The Hyperparameters first appeared on Magnimind Academy.

Before we dive deeper to understand and decode the mystery surrounding data science salary, let’s take a look at what makes you a good professional in the field. Remember – if you don’t feel attracted to or excited about data; miss the skills, patience or experience of pondering over piles of data to find useful insights; or can’t act as a data sleuth to get information from seemingly innocuous data where others can’t see or find any, you should reconsider stepping into this field.

After all, the mere lure of high levels of data science salary won’t carry you much farther in the field. You can go to a certain distance for sure, but if you aren’t motivated by the beauty of the field itself, your drive will die soon and you may reach nowhere after having wasted a significant amount of time, effort and money on a subject that you didn’t love or feel excited about.  

1- What makes you a good professional in the field of data science?

Though the field of data science offers various positions at different levels, let’s stick to data scientists and find the three key skills one should have to excel in it.

1.1- Analytical mindset

Data preparation takes almost 70% of a data scientist’s time. From data cleaning and munging to preparing data so that it becomes fit for machine learning algorithms to be applied to it, a data scientist needs to and should be comfortable in handling huge data sets. So, possessing an analytical mindset with a strong statistical background is a must. Additionally, you should be strong in specific programming languages like R or Python, and have good knowledge of data structures as well as machine learning algorithms.

1.2- Sound domain knowledge

Before you even start dreaming of high data science salary, you should ensure to have sound domain knowledge. From the skills to understand business problems and select the most apt data science model to address them, to having an eye for the detail, and the ability to interpret the findings and arrive at the final result, you need to excel in many aspects. Apart from sound domain knowledge, you also need to have good communication skills to convey your findings in a lucid language to the wider audience.

1.3- Programming and statistical skills

Apart from being adept in programming languages like Python and R, you should also have sound knowledge of mathematics, statistics, and algorithms. But just having these skills isn’t enough. You should ideally apply your knowledge to solve real-world problems. This would need you to be not only strong with the basic concepts, but also have the ability to use the technology tools to your advantage to create great models.

The key is to learn the basics right and then build on them by applying the acquired knowledge to practical problems while understanding and overcoming the practical difficulties that you’ve to face.

Now that you have a fair share of knowledge about what makes one a good data scientist, let’s take a look at what probably interests you the most – high levels of data science salary. We’ve considered three different regions across the world (the US, the UK, and India) to find where they stand with respect to data science salary. We’ve used information from Glassdoor, PayScale, Indeed, and some additional resources for the purpose.

1.3.1- The scenario in the US

According to Glassdoor, the average base pay for a data scientist in United States stands at $117,345, but PayScale puts the figure at $92,521. Glassdoor also reveals the additional cash compensation (that includes commission, cash bonus, tips, and profit sharing) to be $11,530 on an average, while the range of such compensation is between $3,933 and $26,784.

In case you feel interested in knowing which companies occupy the top positions with respect to lucrative data science salary they pay to their data scientists, the following (the average base salary in USD) will give you an idea of what’s like to work for the reputed companies:

• Facebook – pays $135,000 per year
• Twitter – pays $134,061 per year
• Apple – pays $130,000 per year
• Airbnb – pays $130,000 per year
• Google – pays $127,000 per year
• Uber – pays $121,500 per year
• Microsoft – pays $120,000 per year
• Amazon – pays $119,979 per year
• LinkedIn – pays $119,800 per year
• Oracle – pays $110,000 per year

If you want to know more about the salaries for some related job titles, here’s what you need to know (these are the average base pay that the following jobs offer):

• Senior Data Scientist: $137K per year
• Quantitative Analyst: $116K per year
• Data Analyst – $67K per year

1.3.2- The scenario in the UK

The average base pay for a data scientist in the United Kingdom stands at £45,000 according to Glassdoor. PayScale puts the figure at £40,547, while Indeed says it’s £62,143 per year in London, which is 12% above the national average. According to Adzuna, an average data scientist earns £66,441 per year in London. Adzuna further adds that thus figure is 16.4% over the average national salary for data scientist jobs. When compared to the average salary across London, the average data science salary in London that a data scientist gets is 59.3% more.

Glassdoor didn’t reveal anything about additional cash compensation but according to it, here are some companies that pay a lucrative data science salary to their data scientists (the figures below show the average base salary in GBP):

• Barclays – pays £52,150 per year
• Capgemini – pays £50,000 per year
• Dunnhumby – pays £44,000 per year

According to PayScale, the top five companies for hiring data scientists are

• McKinsey & Company
• PwC
• Deliveroo
• Deloitte
• Bloomberg

According to Indeed, here’s what the top companies pay in terms of data science salary to their data scientists:

• Braintree Limited – pays £99,517 per year
• OHO Group– pays £68,445 per year
• Harnham – pays £65,355 per year
• Kortx Ltd – pays £120,000 per year
• Client Server – pays £62,555 per year
• X4 Group – pays £70,356 per year
• Networking People (UK) Limited – pays £72,565 per year

If you want to know more about the salaries for some related job titles, here’s what you need to know (these are the average base pay that the following jobs offer):

• Data Analyst: £30K per year
• Quantitative Analyst – £65K per year

1.3.3- The scenario in India

The average base pay for a data scientist in India stands at ₹650,000 according to Glassdoor. PayScale puts the average data science salary (for a data scientist, IT) at ₹634,645, while Indeed says it’s ₹708,076 per year.

Glassdoor didn’t reveal anything about additional cash compensation but it lists a handful of companies that pay a lucrative data science salary to their data scientists (the figures below show the average base salary in INR):

• Mu Sigma – pays ₹497,500 per year
• Tata Consultancy Services – pays ₹757,500 per year
• IBM – pays ₹1,350,000 per year

According to Indeed, here’s what the top companies pay in terms of data science salary to their data scientists:

• Future Focus Infotech– pays ₹14,63,789 per year
• 2COMS Consulting Pvt. Ltd – pays ₹13,50,526 per year
• Crescendo Global – pays ₹12,71,853 per year
• Okda Solutions – pays ₹14,32,256 per year

In case you’re interested in knowing more about the salaries offered by some related job titles, here’s a glimpse for you to ponder upon (these are the average base pay that the following jobs offer, as per Glassdoor):

• Data Analyst: ₹405K per year
• Quantitative Analyst – ₹708K per year

Final thoughts

Perhaps you now have a better idea about the data science salary structure that’s prevalent in companies across the world. You should remember that apart from your qualifications and experience, how much value you bring to the company too would play a big role in the level of data science salary you’re offered.

So, step into this field if its elements really interest you and not just because you’re chasing a fat pay packet. As we’ve said before, a high level of data science salary alone won’t take you a long way ahead unless you’re driven from the inside by pure love and interest in the field.

.  .  .

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

The post Here Is The Answer To The Most Carious Question-Data Science Salary first appeared on Magnimind Academy.

No wonder why many IT professionals as well as those with math/statistics background are joining and even planning to step into the field of data science. Even freshers are eyeing the field with great interest and weighing their options that could help them give an edge over their competitors.

Irrespective of which category you belong to, if you’re thinking about all the different routes that can help you become a data scientist, perhaps the thought of attending data science bootcamps have crossed your mind. But you may be skeptical of joining one as there’s a lingering question at the back of your mind – would the effort, time, and money you invest in one of these data science bootcamps be equivalent to the information you get? Before trying to find an answer to your question, let’s consider a few things.

1- Three key elements of good data science bootcamps

Before you wonder what criteria effective data science bootcamps should have, you need to have a clear understanding of what these bootcamps are all about. You can call them a “fast track” way that lead to well-paying jobs in the field of data science.

Thanks to their practical learning curriculum, the bootcamps pack in just what you need to know while steering clear of unnecessary elements. When you factor in other advantages like shorter class times, lower tuition costs, the flexibility to pick courses that interest you, they may appear to be the ideal vehicle for fast-track, effective learning.

Though data science bootcamps are often praised for generating interest in the field and increasing access to it, whether they are really worth it is a big question. After considering what people, who’ve attended such bootcamps say about whether their investments have paid off or not, we’ve found that you should ideally break data science bootcamps down into three key elements – learning style, cost, and outcomes. Evaluating these areas would let you decide if a data science bootcamp offers useful information equivalent to the money you would pay, and the time and effort you would have invested into it. And armed with such information, you’ll be in a better position to make an informed decision.

Let’s evaluate each of the three key elements and find out what you should look for when taking your pick from several data science bootcamps:

1.1- Learning style

Data science bootcamps are mostly known for being extremely targeted, hands-on, and fast-paced. If you think you’ll pay up, join a bootcamp, and then magically absorb whatever information is being shared over the next couple of weeks, you may be in for a rude shock. So, before you start your search for data science bootcamps, you should get a clear idea of which learning style your prefer – self-paced, fully instructor-led (one-on-one or in-class instructions followed by open discussions, group work with peers, and exercises with guidance from instructors or teaching assistants etc.).

You should remember that most data science bootcamps aim to help student’s master specific skills within a pre-professional setting, where you’ll be surrounded by high-achieving peers. If you have the zeal to work in a fast-paced environment and learn from and collaborate with peers, data science bootcamps could be worth attending for you.

Before you get ready for data science bootcamps, you should also take stock of your background and skills you already have. It’s important to keep in mind that usually, data science bootcamps are designed for students with some expertise in mathematics/statistics and coding. So, if you’re from a math/statistics background but don’t have any programming skills, it would be better to pick up a few relevant one like Python, R etc. before you dive into a bootcamp to learn data science.

Though you may also come across some data science bootcamps that take in coding novices, it would be much better to have the basic knowledge at first as that would make you fit to absorb the fast-paced, high-intensity curriculum that most bootcamps have.  

So, you should know what your skill level is and the type of learning that suits your schedule and preference the best before enrolling into one of the several data science bootcamps that are available in the market these days.

1.2- Cost

If affordability is the sole driving factor (though it should not be) while choosing from a handful of data science bootcamps, you’ll have many affordable options. Depending on the course curriculum on offer, the level of expertise of the instructors, the reputation of the institute that’s organizing the bootcamp and its collaboration with famous institutes/companies for offering the certification, the cost of attending the bootcamp would vary.  Yet, they would mostly be substantially much more affordable than getting a full-time master’s degree or Ph.D. from universities. And when you consider how fast you would be ready for the job market (in 14 to 24 weeks rather than spending two years to get your master’s degree), data science bootcamps at a fraction of the cost of your usual university degree program would seem to be a far lucrative option.

If you’re a beginner in the field of data science, you would surely like to start earning money as a data scientist as soon as possible. In case you’re planning a career change or want to improve your chances of bagging that covered data science job, doing it fast without burning a big hole in your pocket would surely seem like the best option. And that’s where data science bootcamps beat their traditional counterparts by a wide margin.

1.3- Outcomes

Many bootcamp attendees think it all ends when they finish the curriculum. But did you know what happens after any bootcamp is actually what matters a lot? Yes, that’s true. After all, it you can’t get the pay rise you have been eyeing for long, or fail to get hired for a coveted data science job, or find that you dislike your job, you may be forced to think if investing your time, money, and effort on the bootcamp was really worth it.

Ideally, you should short-list data science bootcamps that offer you support even after you’ve finished the course. It could be in the form of updates about recent vacant posts and industry trends, the chance to network with your peers, use discussion forums to find answers to questions or problems where you get stuck, get help from the instructors etc.

2- Money invested vs. information acquired – are they equivalent?

Most often, people jump into the very first bootcamp they can find and then complain they couldn’t learn much, or that it didn’t match their skillset or wasn’t equivalent to the money they paid. Like everything else in life, before choosing one from the many data science bootcamps, you need to weigh them on certain parameters as discussed earlier. If you don’t have some background in coding and math, and yet join a bootcamp that’s designed for advanced users, simply wishing to learn it all within a few weeks, you’ll be setting yourself up for failure. So, it won’t be long before you find the curriculum too tough and stressful to understand.

At this point, you may even feel cheated but the responsibility of it all would be on you before you jumped in prior to understanding your expertise level and your readiness for the bootcamp. But this doesn’t mean you need to feel discouraged.

Even if you have no coding background, you can still find some pretty useful data science bootcamps that will get you ready as a programmer within a few weeks. And once you’ve got the basics of the relevant programming languages like Python, R etc. right, and even tacked a few projects as well as real-world problems, you can take the next step forward by attending some more extensive data science bootcamps that let you dive deeper into the complex world.

And with a bit of careful planning and the will to see it through till the end (even when the going gets tough and stressful, which might happen for many), you’ll find that the money you pay for your data science bootcamps is indeed equivalent to the information you get or often, much more.

Final words

Data science bootcamps may not be fit for everyone. But if you prefer shorter data science programs that are available at lower prices and let you learn fast from experts in addition to giving you unmatched hands-on experience, data science bootcamps run by industry experts and reliable institutes could be exactly what you need.

.  .  .

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

The post Is It Worth Investing In Data Science Bootcamps? first appeared on Magnimind Academy.

The popular applications of deep learning

What are some of the popular applications of deep learning?

Today, deep learning is used in a wide range of fields, the most popular ones among which are:

• Computer Vision
• Sentiment Analysis
• Virtual Assistants
• Image Recognition and Processing
• News Aggregation
• Automatic Text Generation
• Natural Language Processing
• Object Detection
• Robotics

CNN (Convolutional Neural Network)

What’s a CNN (Convolutional Neural Network)?

In the domain of deep learning, a CNN refers to a category of deep neural nets that are most commonly applied for analyzing visual imagery. In other words, a CNN takes an input image and allocates importance (learnable biases and weights) to the different objects/aspects in the image to become capable of distinguishing one from the other.

Central to the CNN is the convolutional layer, which executes a process called a “convolution”. This is a linear operation where a set of weights is multiplied with the input, similar to a traditional neural network. Since the technique was planned for 2D inputs, the multiplication is executed between a set of input data and a 2D group of weights, called a kernel or a filter.

Apart from the input and output layers, CNNs typically have multiple pairs of convolutional and pooling layers, which are followed by several consecutive convolutional layers (also called fully connected layers), and lastly, a regression layer or a softmax layer to produce the desired outputs.

Deep learning platform and a deep learning library

What’s a deep learning platform and a deep learning library? 

A deep learning platform offers a set of tools together with an interface for constructing custom deep nets. As a user, you will get to choose from a selection of deep nets on a deep learning platform along with the ability to combine data from various sources, manipulate it, and manage models via the user interface. If you need to train a net with a massive dataset to improve performance, you can use some of these platforms designed for the task. 

A deep learning library refers to a group of modules and functions that you can call through your own programs to carry out certain tasks. By using deep net libraries, you can enjoy a high degree of flexibility with hyperparameter configuration and net selection.

Name some of the deep learning frameworks or tools that you have used?

I have worked with the following deep learning frameworks and tools (make sure to pick the ones from the list below that you have experience with):

• Keras
• TensorFlow
• PyTorch
• Caffe2

Wrapping up

While these questions may not be that difficult to answer, you need to make sure your answers convince the interviewer of your clear idea about the fundamental concepts of deep learning. 

The post What Are The Frequently Asked Interview Questions With Answers: A Third Post Of A Series Of Three first appeared on Magnimind Academy.

What is deep learning and how’s it different from other machine learning algorithms?

Deep learning is a subset of machine learning that uses a layered structure of algorithms, known as artificial neural network (NN), the design of which is inspired by the human brain’s biological neural network. Thus, a deep learning model can learn and make smart decisions by constantly analyzing data with a logic structure on its own, similar to what humans do.

In contrast, machine learning algorithms can parse data, learn from it, and make informed decisions based on such learning. Though machine learning models do become better in their targeted functions with time, they’ll still require some guidance. For instance, if an erroneous prediction is given by an AI algorithm, an engineer will have to intervene and make the necessary adjustments. But in a deep learning model, the algorithm can use its own neural network (NN) to decide on its own if a prediction is correct or not.

How many network layers will any deep neural network (NN) consist of?

Any neural network (NN) consists of at least three network layers, namely an output layer, a hidden layer, and an input layer. The hidden layer positioned between the algorithm’s input and output layers is the most vital layer as feature extraction occurs here, and adjustments are made for better functioning and faster training.  When there are more than three layers with multiple hidden layers, this neural network is considered a deep neural network. 

How can ensemble learning improve the performance of neural network (NN) models?

Since neural networks (NNs) have a high variance, developing a final model that can be used for making predictions could be frustrating. An effective solution is to reduce the variance of NN models by training multiple models (or ensemble models) instead of a solitary one and combining the predictions from all these models. This is ensemble learning, which not only decreases the variance of predictions but can even deliver better predictions than what a single model would do by averaging out biases and having lower chances of overfitting.

What do you mean by overfitting and underfitting?

Overfitting is a machine learning algorithm or statistical model that captures the noise of the data (which refers to data that’s irrelevant and redundant). 

Underfitting is a machine learning algorithm or statistical model that doesn’t fit the data adequately well. This happens when the algorithm or model displays high bias but low variance.

What makes boosting a more stable algorithm than other ensemble algorithms? 

The focus of boosting is on finding errors in earlier iterations until they become obsolete. But there’s no corrective loop in bagging. This is the reason that makes boosting a more stable algorithm than other ensemble algorithms.

Conclusion

Make the most of these interview questions to give your deep learning career the necessary assistance it needs.

The post What Are The Frequently Asked Interview Questions With Answers: A Second Post Of A Series Of Three? first appeared on Magnimind Academy.

Key job responsibilities

As a data analyst, what will be your key job responsibilities?

As a data analyst, some of my key job responsibilities will be

• Data cleaning where I’ll remove or fix incomplete, duplicate, corrupted, or erroneous data within a dataset.
• Data exploration and interpretation where I’ll explore massive data sets to find out initial attributes, patterns, and points of interest and analyze these results.
• To provide support for each phase of data analysis, and analyze complex datasets to identify the hidden patterns in them and extract insights for decision-making.
• To keep the databases secured.

Statistical methodologies used by data analysts

Which statistical methodologies are used by data analysts?

To perform data analysis, several statistical techniques can be used. However, some of the significant ones are:

• Cluster analysis
• Markov process
• Rank statistics
• Bayesian methodologies
• Imputation techniques

The best tools for data analysis

What are the best tools for data analysis? 

For data analysis, some of the most useful tools are:

• Tableau
• Google Search Operators
• RapidMiner
• Google Fusion Tables
• NodeXL
• KNIME
• OpenRefine
• Solver

The criteria that define a good data model

What are the criteria that define a good data model?

A data model is good if 

• it’s intuitive.
• data in it can be easily consumed.
• data changes in it are scalable.
• it’s responsive and adaptive to changes, which would make it capable of supporting new or growing business needs.

Feature engineering for data analytics

How can feature engineering make data analytics more powerful?

Data that I’m given or which I gather may not always be adequate for designing a good machine learning model. This is where feature engineering can help. It can prepare suitable input datasets that are compatible with the requirements of the machine learning algorithm, and help in boosting the machine learning models’ performance. The domain of feature engineering involves different tasks, such as: 

• Feature transformation where new features are built from existing features.
• Feature generation (or feature extraction) that involves creating new features via domain-specific or generic automatic feature generation methods; these new features aren’t usually the result of feature transformation.
• Feature selection, where a small set of features are chosen from an extremely big pool of features; with the decreased feature set size, it becomes computationally viable to use specific data analytic and machine learning algorithms.
• Automatic feature engineering, which is a generic method for automatically producing a huge number of features and choosing an effective subset of the produced features.
•  Feature analysis and evaluation where the efficacy of features and feature sets is assessed. 

Conclusion

Conclusion

These interview questions for data analysts are selected from a vast pool of probable and frequently asked questions. Thus, knowing their answers would surely help you a lot in landing your dream job where you can have fun with data cleaning, data exploration, and much more.

The post What Are The Frequently Asked Interview Questions With Answers: A First Post Of A Series Of Three first appeared on Magnimind Academy.

Must have technical skills and capabilities

Data Science Fundamentals along with Statistics

To become a successful data scientist,you need to have a solid knowledge of the basics of data science, artificial intelligence, and machine learning as a whole. Additionally, you should understand the relevant topics in statistics and mathematics like sample and population, standard deviation, probability distributions, skewness and kurtosis, CLT, relational algebra, matrices and linear algebra functions, database basics, binary search tree and hash functions, etc.

Programming

Programming gives you a way to communicate with machines. Though you don’t need to be the best in programming, you should surely be comfortable with it to progress in the field of data science. Python and R are two of the most used programming languages in data science. Python is a general-purpose language that has multiple data science libraries, while R is suited for data visualization and statistical analysis.

Data Analytics and Manipulation

Data analytics is all about getting the feel of data and making sense of it. For instance, it could involve figuring out which products are most frequently bought by customers, the weekly average sales, etc. Typically, you’ll analyze raw data to spot trends and get insights. For data analysis (a subset of data analytics), you’ll usually use Pandas in Python, SQL, or Excel. Before you perform data analysis, you’ll have to manipulate the data by cleaning and transforming it into a format that gets it ready for analysis.

Data Visualization

In the domain of machine learning, data visualization is an interesting part where you’ll need to construct a story from the visualizations. For this, you’ll have to be familiar with plots like bar charts, histogram, pie charts, and be adept in handling advanced charts like thermometer charts, waterfall charts, etc. In exploratory data analysis, such plots would be helpful while bivariate and univariate analysis would become much easier when you use colorful charts. 

Machine Learning

This is a core skill to possess if you want to excel in the field of data science. You can begin with a simple logistic and linear regression model and then proceed to advanced ensemble models like XGBoost, Random Forest, etc. Machine learning is a subset of artificial intelligence that contributes to data modeling. When you have to handle and operate on huge volumes of data and facilitate a decision-making process based on data, machine learning is a must-have skill.

Other skills

You’ll also need good and persuasive communication skills, curiosity, storytelling skills, and structured thinking to become a good data scientist.

.  .  .

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

The post What Are The Capabilities And Technical Skills You Need To Be A Good Data Scientist? first appeared on Magnimind Academy.

• Is this income from salary? Or from a business?
• How long has the individual been in service or running the business?
• Does the person have any criminal record?

Based on the answers, the next set of questions could involve finding out if the person has any existing loans, has defaulted on credit card payments, etc. Assuming the person draws a salary of $30,000, has no existing loans or criminal record, and makes his credit card payments on time, the bank may offer him the loan. You can call this a basic form of a decision tree.

What’s a decision tree?

What’s a decision tree?

It is an effective machine learning modeling technique for classification and regression problems. To find solutions or possible results of a series of related choices, a decision tree makes hierarchical, sequential, decisions about the variable outcomes based on the predictor data. Typically, a decision tree begins with a single node, or the root node, which branches into probable outcomes which are called the decision nodes or leaf/terminal nodes based on whether they have further sub-nodes or not. Each of those outcomes could give rise to additional nodes that branch off further to take into account other possibilities, as in the case of the bank loan example we had discussed at the start. This gives the entire structure a tree-like shape, and hence the name of the decision tree as it helps to identify a strategy that’s most likely to facilitate achieving the desired goal.

Decision Trees and Machine Learning

Decision Trees and Machine Learning

In machine learning, you have two categories of models, namely classification and regression. You can apply decision trees to both. Where you need to deal with distinct categorical target variables, you’ll need to use classification trees. Typical use cases could be predicting if a team would win the match or not, if an email is spam or not, if the temperature would be low or high, etc.

Regression trees are used when you have to handle continuous quantitative target variables. Typical use cases could be predicting marks, revenue, the salary of an employee, rainfall, etc.

Handling variance in decision tree models

Handling variance in decision tree models

Even a slight variation in the data could make the decision tree unstable by resulting in the creation of a tree that’s completely different from what was intended. This is called variance. You can reduce it with methods like bagging and boosting.

Bagging decision trees: You can use them to create multiple decision trees by continually resampling training data with replacement and getting the average of all the predictions from different trees, which would help deliver more robust results than a single decision tree.

Boosting decision trees: An iterative method is used to fit a weak tree to the data and continue fitting weak learners (trees) iteratively to analyze and correct the errors of the preceding model.

Parting thoughts

Parting thoughts

If you work with Python, there is a popular library called Scikit-Learn that you can use to implement decision tree algorithms. It has a great api that can get your model up and running with just a few lines of code. 

The post Decision Tree In A Nutshell first appeared on Magnimind Academy.

Your path to a career in data science without knowing everything

Whether you want to become a data scientist or land that coveted job in the field of data science, here are the steps to follow:

Learn and hone the relevant skills

For a successful data scientist career, you need to have certain skills in mathematics, statistics, and programming languages. While the transition to data science would be easier if you have a programming or quantitative background, it isn’t impossible even if you don’t have one. However, you should be ready to work hard in the latter case.

Your first step will be to learn and/or sharpen your mathematical skills and concepts like probability theory and statistics, which are crucial for the implementation of algorithms. Some math and statistics concepts to master could be:

• Linear algebra
• Probability theory/distributions 
• Statistical modeling and fitting 
• Data exploration
• Probability 
• Hypothesis testing
• Multivariable calculus
• Regression analysis
• Bayesian thinking and modeling

After you have built a robust foundation with the above, you should learn some of the key programming languages that are crucial to excel in your data scientist career, such as Python, R, and SQL, to name a few. 

The quickest and most effective way to polish up your math and statistics skills in addition to learning a few programming languages is to enroll in a reliable data science bootcamp in Silicon Valley.

Get real-world projects

Learning and mastering the relevant data science skills are absolutely mandatory, but to leverage them the best way, you need real-world practice. This means you should be able to apply your learning and skills to solve real-world problems as this would give the necessary confidence and help you get adequate experience. Both of these will make the path to your dream data science job much shorter. But how do you do this? The answer lies in joining a data science bootcamp in Silicon Valley.

Not only will such a bootcamp give you the chance to get trained by industry experts and ask them questions but even let you learn via real-time projects and assignments. Additionally, you’ll benefit from peer-to-peer interaction and brainstorming sessions. The opportunity of being mentored by industry experts and data science professionals, who have already mastered the domain, is an added advantage of such data science bootcamps in Silicon Valley.

Final words

Once you’re confident in your data science skills and abilities, you can begin your data scientist career, say with the job of a data analyst, and move up from there with continued education and growing experience.

The post How Can I Get A Job In Data Science Without Knowing Everything? first appeared on Magnimind Academy.

1. Improving your statistical inferences: Designed for intermediate level learners, this course offered by the Eindhoven University of Technology has a rating of 4.9 stars and will help you to derive better statistical inferences from empirical research. Since it’s backed by practical, hands-on assignments, you’ll learn to tackle various real-life situations. Be it simulating t-tests to learn which p-values to expect, calculate likelihood ratios, or ways to examine if the null hypothesis is true via Bayesian statistics and equivalence testing, you’ll be able to do all these and more with this online course.
2. Bayesian Statistics Mixture Models: Targeting advanced-level learners, this course with a 4.7-star rating offered by UC Santa Cruz is based on the premise that statistics is best learned by doing it. Thus, with this course divided into 5 modules, you can learn an important category of statistical models through their application and peer-reviewed assignments together with short quizzes, lecture videos, discussion prompts, and background reading.
3. MITx MicroMasters Program in Statistics and Data Science: With this course, you’ll master the basics of statistics along with data science and machine learning. With instructors from the Massachusetts Institute of Technology, you’ll learn to analyze big data, perform data-driven predictions, pull out meaningful information by deploying suitable modeling and methodologies, and a lot more.  
4. Become a Probability & Statistics Master: This extensive 163-lesson course will teach you everything about probability and statistics that you’ll need to become a data scientist. From visualizing and analyzing data to data distributions, Bayes’ theorem, handling discrete random variables (including Bernoulli, binomial, Poisson, etc.), hypothesis testing, and regression, you’ll learn them all using some real-life data.  

Conclusion

Get enrolled in any of these courses to jumpstart your data scientist career by learning and honing your skills in statistics. Since these courses let you learn and practice with real-life data and situations, you’ll not only gain useful insights and the practical application of the concepts learned but even give your confidence a big boost. Thus, these courses will get you ready to step into your chosen data science career.

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

The post What Are The Best Online Courses On How To Use Statistics On Real-life Data? first appeared on Magnimind Academy.