Sunday 19 June 2022

Data Preparation in WEKA

 https://lahirumadushankablog.wordpress.com/2018/02/11/data-preparation-in-weka/

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Monday 13 June 2022

How to Learn Math for Machine Learning: Step by Step Guide?

 This excerpt is directly copied from 

https://www.mltut.com/how-to-learn-math-for-machine-learning-step-by-step-guide/

for educational purposes


When it comes to learning math for machine learning, most of us are stuck and don’t know what to learn and from where to learn…Right?. That’s why I thought to write an article on this topic. In this article, I’ll discuss how to learn math for machine learning step by step. So read this article and clear your all confusion regarding math for machine learning.

So, without further ado, let’s get started-

How to Learn Math for Machine Learning?

Before learning math, you should know why math is important for machine learning and why you can’t avoid math. Alright…?. So let’s understand the importance of math in Machine Learning-

Importance of Math in Machine Learning

  • With the help of mathematics, you can select the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters, and number of features.
  • Mathematics helps you to identify under-fitting and over-fitting by understanding the Bias-Variance tradeoff.
  • You can choose parameter settings and validation strategies with the help of math.
  • Without knowledge of math, you can’t estimate the right confidence interval and uncertainty.

That’s why you should have mathematics knowledge to become a Data Scientist or Machine Learning Engineer.

Now you understood the importance of math, so let’s see how to learn math for machine learning and what’s the first step-

Step 1- Identify How Much Math is Needed for Machine Learning

The first step is identifying how much math is required for machine learning. So the minimum level of mathematics needed to be a Machine Learning Scientist/Data Scientist is-

  1. Linear Algebra
  2. Probability Theory and Statistics
  3. Multivariate Calculus
  4. Optimization Methods

1. Linear Algebra

Before discussing what topics to learn in Linear Algebra, I would like to tell you why you need to learn Linear Algebra for Machine Learning.

Why Linear Algebra?

In machine learning, most of the time we deal with scalars and vectors, and matrices. For example in logistic regression, we do vector-matrix multiplication. Sometimes we do clustering of input by using spectral clustering techniques, and for this, we need to know eigenvalues and eigenvectors.

Linear algebra is used in data preprocessing, data transformation, dimensionality reduction, and model evaluation.

I hope, now you understood why you need to learn Linear Algebra…Right?. Now let’s see what topics you need to learn in Linear Algebra-

Topics to Learn in Linear Algebra-
  • Basic properties of a matrix and vectors: scalar multiplication, linear transformation, transpose, conjugate, rank, and determinant.
  • Inner and outer products, matrix multiplication rule and various algorithms, and matrix inverse.
  • Matrix factorization concept/LU decomposition, Gaussian/Gauss-Jordan elimination, solving Ax=b linear system of an equation.
  • Eigenvalues, eigenvectors, diagonalization, and singular value decomposition.
  • Special matrices: square matrix, identity matrix, triangular matrix, the idea about sparse and dense matrix, unit vectors, symmetric matrix, Hermitian, skew-Hermitian and unitary matrices.
  • Vector space, basis, span, orthogonality, orthonormality, and linear least square.

2. Probability & Statistics

So let’s understand why probability and statistics is important in machine learning-

Why Probability & Statistics?

Probability helps you to manage uncertainty. Uncertainty means working with imperfect or incomplete information. And in Machine Learning, we build predictive models from uncertain data. But we can manage uncertainty using the tools of probability.

Whereas Statistics help you to count well, normalize well, obtain distributions, find out the mean of your input feature, and its standard deviation.

That’s why knowledge of Probability and Statistics is important for machine learning. Now let’s see what topics you need to learn in Statistics & Probability-

Topics to Learn in Probability & Statistics–
  1. Mean
  2. Median
  3. Mode
  4. Standard deviation/variance
  5. Correlation coefficient and the covariance matrix
  6. Probability distributions (Binomial, Poisson, Normal)
  7. ANOVA, t-test
  8. Hypothesis testing
  9. Confidence intervals
  10. p-value
  11. Baye’s Theorem (Precision, Recall, Positive Predictive Value, Negative Predictive Value, Confusion Matrix, ROC Curve)
  12. A/B Testing
  13. Monte Carlo Simulation

3. Multivariate Calculus

So as we did in the previous two sections, let’s also understand the importance of multivariate calculus in machine learning-

Why Multivariate Calculus?

Multivariate Calculus helps us to explain the relationships between input and output variables. And Multivariate Calculus comes into the picture when you deal with a lot of features and huge data. That’s why familiarity with multivariate calculus is essential for building a machine learning model.

Now let’s see what topics you need to learn in Multivariate Calculus–

Topics to Learn in Multivariate Calculus–

  • Functions of several variables,
  • Derivatives and gradients,
  • Step function,
  • Sigmoid function,
  • Logit function,
  • ReLU (Rectified Linear Unit) function,
  • Cost function,
  • Plotting of functions,
  • Minimum and Maximum values of a function.

4. Optimization Methods

Optimization methods are important to understand the computational efficiency and scalability of our Machine Learning Algorithm. In the end, mostly all Machine learning algorithms come down to some optimization tasks.

Topics to learn in Optimization
  1. Cost function/Objective function
  2. Likelihood function
  3. Error function
  4. Gradient Descent Algorithm and its variants (e.g. Stochastic Gradient Descent Algorithm)

So now you understood how much math you need to learn. Now, the next step is-

Step 2- Find Out the Resources to Learn Math for Machine Learning

Once you identified the topics, the next step is to find some valuable resources for learning math. Thanks to the Internet Era, there are lots of resources available online. You can learn math from YouTube videos, online tutorials, and courses.

I am going to tell you some best resources for learning math-

Resources for Learning Linear Algebra-

  1. Linear Algebra Refresher Course– FREE Course
  2. Linear algebra (Wikipedia)
  3. Introduction to Linear Algebra, Fifth Edition (TextBook)
  4. Mathematics for Machine Learning: Linear Algebra (Online Course)
  5. Linear Algebra at Khan Academy
  6. First Steps in Linear Algebra for Machine Learning (Online Course)
  7. Linear Algebra for Beginners (YouTube Video)

I hope these resources are enough for you to learn Linear Algebra.

Resources for Learning Probability Theory and Statistics–

For Probability-

For Statistics-

Resources for Learning Multivariate Calculus

Resources for Learning Optimization Methods-

Step 3- Make a Plan for Learning

Now you understood how much math is needed, and got resources for learning, the next step is to make a learning plan. This means identifying how to learn all the required math for machine learning.

I would not suggest learning math first without learning ML Algorithms. Why…?

Because without having familiarity with ML algorithms, you can’t relate math terms with machine learning. So first gain some fundamental knowledge of machine learning algorithms, and then learn math.

Suppose you are learning Logistic Regression, and you realize that Linear Algebra is required for understanding logistic regression. So then you can go with these listed resources and learn linear algebra concepts.

So what will happen if you learn in this manner. You can easily relate the math terms with machine learning algorithms.

But if you start your ML journey with math, you will feel frustrated after some time, especially if you are from a software background like I am😃.

So if you are a complete beginner in ML and planning to start your journey in ML, I would recommend starting with Machine Learning Course by Andrew Ng. This course will provide you a basic understanding of ML algorithms. Once you will gain a basic understanding of Machine learning algorithms, then learn math.

I hope now you understood everything related to math for machine learning. Now it’s time to wrap up.

Conclusion

In this article, I tried to explain how much math is needed for machine learning, resources for learning math, and how to learn math without losing enthusiasm. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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How I Would Learn to be a Data Analyst

 what up done nerds i'm luke a data analyst and my channel is all about tech and skills for data science and in this video today i wanted to cover my pathway for becoming a data analyst if i had to start over again and for this i'm not going to be only sharing the skills that i recommend learning but also my process for learning different skills which i've applied and refined over my time in school learning engineering to my time in the navy learning how to drive a nuclear-powered submarine and then more recently to learning all the different skills of a data analyst in order to continue to gress further in my job this process has also been refined by my interactions from others that have not only gotten jobs as data analysts but also hired others for these roles as well my journey was filled with a lot of wasted time and effort and so i'm hoping that this video helps save you effort and also time and learning the skills you need to know for your job so let's break into my recipe for learning anything and it's an iterative two-step approach that i recommend taking that can be applied to anything that you really want to learn the process consists of learning it and then using it so let's expand further into what you should be learning as a data analyst and i feel that there are four general areas that you should be focusing on that consist of technical skills soft skills analytical skills and domain knowledge don't worry we'll go into all these general topic areas in a bit but we need to move to that next step of actually applying it immediately after learning it and using it is it true that if you don't use it you lose it is that a serious question which in this case is quite literally true because the tools that i've learned in the past and haven't applied i haven't been able to retain them so i feel like this is a really key important aspect in order to retain that skill and you can use these skills in a variety of different ways such as coursework projects on coursera portfolio projects for your resume work projects for your job and then also through teaching others now there's an added benefit of this second step and that's that because you've created something with your new skills you now have something to showcase to an employer as experience so when you're searching for a job you can now display this item that you created for employers to see i've rambled about this before but i think that online courses and certificates are great for this first step in the process of learning things but if you don't have experience to showcase on a resume on how you use these skills that you learned an employer is not going to risk hiring you and all of this relates directly to the sponsor of this video coursera coursera does a great job of hosting courses so that way you can learn the skills in that first step of the process but also in that second step of using something it then goes and has projects available for its specializations and certificates and this is great because you can not only display your certificate or specialization on a resume you can also showcase those projects as experience for employers to see so getting back to my learning process so once you have learned a skill and then used it it's then time to iterate back and learn a new skill so where do you actually start and what skill should you focus on first for learning a skill my preference is to start with those technical skills and then also incorporate those other skills such as analytical or domain knowledge while you're learning a technical skill so why do i say start on a technical skill first so one i feel like they're more tangible and they're easier to set goals that you can actually accomplish such as you can write out what functions you want to learn for excel and then learn it i also find that technical skills are funner to learn and i have higher motivation levels when actually setting out to accomplish that and two they allow you to apply other skills while actually focusing on that technical skill for example say you're learning a technical skill such as like r you could also write a blog post about it and this would showcase and build on your soft skill of writing so technically you're not only focusing on technical skills but you're also trying to incorporate those other skills as well alright so let's jump into my technical skill roadmap so i recently did a data analytics project where i went through and scraped job posting data from linkedin i was able to find the most important skills for entry-level data analysts based on how many times a skilled appeared in a job posting so my insights from this project were this that excel and sql are the most important skills to learn of a data analyst as they comprise almost half of all job postings following in popularity are the bi tools of tableau and power bi and then also the programming tools such as python or r so from this my recommended roadmap is this first i recommend getting a brief overview of all the different tools i think this is going to help with later on identifying tools that you want to focus on based on what your passion and interest is in i like the google data analytics certificate because it teaches you a lot about the popular tools of sql spreadsheets are in tableau and then going back to my recommendation on how to learn it not only teaches you about these skills but then you also implement these skills in a capstone project for the certificate now this first step in the process is all about breadth not depths and the google certificate is perfect for this because you're not going to be a master of any of these skills once you complete it but you will have a general overview of these tools and you also have an introduction to other skills as well such as soft skills and domain knowledge next it's time to get into a mastering skill and for this we need to focus on either excel or sql i recommend these two most popular tool of data analysts because from a probabilistic standpoint if you have these two skills on your resume i feel like you're more likely to get hired for an entry level data analyst job now regarding whether to use excel or sql first i really leave that up to you if you're looking for recommendations for resources to learn these type of skills check out this recent video i did where i went over some top courses to learn the skills of a data analyst next after mastering both excel and sql it's time to get into mastering other tools such as bi tools and programming languages once again when selecting one of these tools i'd go off what your passion is select one that you have an interest for and you really want to dive into and learn and apply other skills with i think it's important to understand that you don't have to master every single one of these skills here in order to land your first job as a data analyst my first job i landed with only the skills of excel but i continued to progress my skills and because of that i began and because of that i continued to advance in my career as a data analyst being able to level up and get different opportunities based on the skills i was learning so that's my roadmap for technical skills but what about those other areas of analytical skills domain knowledge and soft skills and what do i mean by these skills and how do i incorporate them while learning those technical skills let's break it down first up is analytical skills and by this i mean things like problem solving critical thinking research and then math skills so i get a lot of questions around this math skill whether more in-depth training or studies is needed prior to taking any courses or prior to diving into the field of data analytics part of my life as a data analyst i was fortunate enough to be exposed to a lot of different math subjects so everything from algebra to more advanced mathematics like calculus and differential equations because of this and now being my role of data analyst i can say that the most of the math that i've applied to my job has been pretty basic math and has focused on algebra probability and statistics i don't think subjects like calculus and discrete math are necessary especially for entry-level data analyst roles and the good news is that for most secondary schools like high schools in the united states you're exposed to subjects such as algebra and also other subjects like probability and statistics so based on this i wouldn't necessarily worry that you don't have the math skills to get started instead if you don't know something in math you can then learn it or apply it in a project as you're going along so getting back to how to apply analytical skills in a project when i was learning excel one of my portfolio projects that i was working on in school was building a food nutrition calculator this was a spreadsheet that could tell you what to eat in order to be healthy this project not only required learning excel it also applied probability and statistics in determining what foods to recommend along with basic algebra in calculating macronutrient values of food this project was not only great for teaching me the technical skill of excel but also testing my analytical skills in solving this problem of building this calculator interesting enough this project got brought up multiple times in different job interviews i were in specifically by interviewees that were interested in physical fitness and well-being and it was really great because it allowed me to connect on a similar interest with the interviewee next up is domain knowledge and this is knowledge of a specific discipline or field so for example i recently asked you all what fields you were transitioning from to become a data analyst and the results range from students and business and engineering to those working in an industry such as education and health care from what i found you don't have to actually switch industries or domains in order to become a data analyst in fact what i found is those that have the most success in becoming data analysts apply those newly learned data analytical skills in the current domain or industry that they're in as an example of this in my first role i was working in the procurement industry working only with the skill of excel at the time i was looking to improve my bi tools and an opportunity came up to build a solution using power bi as i had a general understanding of this field of procurement i was able to apply these newly learned skills of power bi in my role to build this dashboard but also i was able to actually go more in depth and learn even more about this field of procurement so for those that are working in an industry or maybe going to school to learn a certain subject i highly encourage you to take a similar approach and dive into a tool while also diving deeper into that domain so you can apply those skills in a relevant project last up is soft skills and this relates to how you work and also interact with other people with the current pandemic this shifted the way that we're interacting with each other and instead of doing the normal face-to-face interactions we've actually shifted this quite differently to using alternate forms of communication i actually think this is a positive in that you can actually showcase these alternate forms of communication in your portfolio and in the projects that you do so what do i mean by this well when i was learning tableau i decided to make a youtube series documenting my learnings these videos were not only improving my tableau skills but also a way for me to improve my soft skills of communication where i was getting first hand feedback on my presentation skills now i'm not saying that you have to make youtube videos per se instead what i'm saying is that you can use social media in order to showcase those soft skills that you have such as writing posts or tutorials on medium sharing your code or processes on github or making short form content on instagram or tick tock all these not only have the benefit of working on those technical and soft skills they also are able to be used and showcase your experience for employers to see how you interact with others all right so that's my roadmap on how i've learned to become a data analyst remember this is not a comprehensive plan so you don't need to learn every single skill that i showed here today instead i'd start small right so start with that one technical still and add in another skill maybe analytical or soft skill that you want to work on and build a project from there iterate remember for me one skill of excel was good enough to get my first job as an analyst and i feel the same can apply to you as well as always if you got value out of this video smash that like button and with that [Music] [Music] new video from ken on how to start in data science yes every year i like to refresh my advice about how i feel about learning data science the data domain is changing [Music] learn more about data science in the upcoming year thank you so much for watching and good luck on your data science journey wait a second we need a video like this but for data analysts what i've done nerds i'm luke a dad analyst and my channel

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Sunday 12 June 2022

6 Ways That Makes Data Analytics in The Hospitality Industry Useful

Excerpt copied from 

for educational purposes

How Data Analytics in The Hospitality Industry Can be Helpful? (6 Tips Inside)

Last modified on  by   6 min read

In recent years, we have seen more industries adopt data analytics as they realize how important it is. Even the hotel industry is not left behind in this. 

This is because the hospitality industry is data-rich. And the key to maintaining a competitive advantage has come down to ‘how hotels manage and analyze this data’. 

With the changes taking place in the hospitality industry, data analysis can help you gain meaningful insights that can redefine the way hotels conduct business. 

Below are some of the ways through which data analytics in the hospitality industry can help hotels with business strategies.

Ways Data Analytics Can Help the Hotel Industry

The importance of data analytics in the hotel industry is necessary, since it serves millions of guests every day. Each of them has their own set of preferences, expectations, and needs for the journey.

But, how does it impact your hotel’s business? Let take a deeper dig at the use of data analytics in the hospitality industry.

1. Can assist in improving revenue management

Data analytics in the hospitality industry can help hoteliers to develop a strategy for managing revenue by using the data gathered from various sources like the information found on the internet. 

Through analysis of these data, they can make predictions that will help owners with forecasting. They would learn about: 

  • Expectation in terms of demand for accommodation in the hotels
  • The best price-value ratio for their guests 

Revenue management professionals are in search of opportunities for marketing services to the right buyer through an appropriate marketing channel at a fair price. 

Various measurements are monitored by experts for determining how competitive a property is, in comparison to its compset. 

Different kinds of data can be beneficial in improving revenue management, such as current bookings, past occupancy levels, and other key performance statistics.

2. Helps in improvisation of services and guest experience

By using hotel data analytics, you can get information like customers’ feedback about their services and experience at the hotel.

This information is readily available on platforms like social media, reviews made on magazines and hotel websites, or even notes left for the hotel. 

However, for reviews, you can also use a traditional survey method which is more detailed, and provides insight into factors that influence guests’ booking decisions. 

With this information, hotel owners and managers can know about their property’s strengths and weaknesses. Hoteliers can also improve their services and provide guests better experiences. 

By using data analytics for hotels, new perspectives can be generated in the hospitality industry. Hoteliers can discover new and better ways to leverage big data for attracting customers and increasing sales. 

3. Data analytics can improve the effectiveness of your marketing

With proper data analysis, the hotel industry can improvise and make its marketing more effective, by knowing exactly what to market to potential customers. 

This enables advertisers to build more unique segments that may assist in identifying key consumer groups who frequently visit hotels or other relevant locations. 

If a guest books the property for the whole family, then with the help of data analytics, one can market the family activities available in the hotel. 

If one usually comes for business, they will showcase activities related to business, which will effectively influence them to come to the hotel. Also, you can market to a specific demographic to beat your competition through target marketing.

4. Helps to scout the business environment and competition

To stay ahead in the industry, hotels must keep an eye out for the competition, and there is no better way of doing so than with the use of data analytics. 

Competitor rates can be determined using real-time data analytics that compares your hotel’s current pricing strategy to your compset. 

This assists in determining the right price for each room using competitive pricing that operates 24/7, resulting in increased hotel bookings. 

The data collected can help you figure out what others in the hotel industry are doing, and how to become better than them in terms of services and experience.

5. Aids in providing additional services 

Hotels communicate with existing and potential guests in a number of ways, allowing them to collect large amounts of data. When data is properly gathered and analyzed, it will show a great deal about not just the programs that guests use, but also the services that they avail. 

In addition to this, hotel owners are able to decide on new products and services to introduce. If guests often request gym equipment that the hotel is lacking, this can assist them to refurbish their gym. 

Data analytics can also help in making decisions about forming partnerships with other companies, such as taxi companies, pubs, restaurants, and travel agencies. 

The use of data analytics in the hotel industry is essential for increasing productivity, efficiency, and profitability. The outcomes of data analysis informs a business where they can optimize, whether operations need improvement, which activities can gain higher efficiency, and more. 

6. Can help in using social media platforms to your advantage

Social media platforms are the most powerful tools for engaging and connecting with the audience from all over the world. 

These media tools are important, especially if you want to have better communication with your guests and remain ahead of the competition. 

It’s feasible for guests to interact with the hotel in new ways because of social media. Nowadays, they are using it for requests, needs, opinions, or concerns. Whereas, hoteliers may use these platforms to offer useful information to their guests about their services. 

On the other hand, being active on social media also means that customers may use it to express their dissatisfaction over the services they received.

Types of data analysis reports your hotel needs

Effective analytics can aid in the development of intelligent marketing and logistics strategies, as well as the identification of target audience. This approach has shown to be quite significant in the hotel business. 

Guests’ sentiments  must be carefully monitored by hospitality services at all times. Here are some of the most important hotel reports to keep an eye on.

1. Identification of guests

For better and more successful service delivery and promotion, the hotel business must first identify and understand its guests. They are more likely to share a positive feedback for a hotel that tends to their needs and preferences. 

This technique of identifying target audience considers certain criteria, such as age, family background, income, hobbies, and previous purchases. 

The hotel industry can use this technology to serve current guests by personalizing services to their specific requirements, as well as target new customers.

2. Report on transactions

By analyzing your daily transactions, you could learn more about what works best for your hotel. You could observe a pattern and figure out which days are more productive and which are not.

You can include payment methods that are more preferred for your hotel. Here are some factors to be considered while creating the transaction report:

  • The total number of check-ins
  • The total number of check-outs
  • The number of unpaid check-outs
  • Occupied and unoccupied rooms on average
  • Method of payment 

3. Forecasting

Every corporation develops a sales forecast for strategic planning, by using customer data, and conducting regular reporting. Such predictive analytics have proven to be useful tools in the data arsenal. 

Forecasting is used to estimate future demand for products and services, and further sales. It also assists businesses in operating more efficiently by managing money. 

The use of analytics to forecast consumer behavior, improve inventory, and product availability is known as revenue management. The objective is to provide good products to the consumers at the right time.

4. Report on statistics 

Small hotel businesses must be able to measure specific indicators that provide them with an insight into how well the business is performing. 

Here are the important factors that you need to consider for analysis:

  • The total number of nights spent (Closed, occupied, and vacant rooms)
  • Canceled reservations
  • The average occupancy
  • Length of stay
  • Lead time
  • Revenue per booking
  • Daily pricing

Conclusion

Lately, hoteliers have realized that using data analytics to implement Business Intelligence (BI) can improve their services, enhance marketing strategies, and even gain more in their line.

It can also be used to uncover perspectives that contribute to strategic business decisions. With data, you will find key answers that can facilitate product growth, better decision making, optimized offerings, and decreased costs.

You can access this data from the internet, through guests’ reviews, travel guides, and even on various social media platforms. 

Data is the new oil and the ones making the best out of it are getting an edge over the competition. So, what are you waiting for? Start using hotel data analytics today.

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