n this post (which is a perfect companion to our SQL tutorials), we will pay attention to coding style. There are many ways you can write your code, but there are only a few considered professional. Complying with coding style rules is crucial. When you advance in programming, not only in SQL but in any language, you will never work on your own. Even at the very start of your career in data science, you will always work in a team. Even if you are a freelancer working online, there will always be someone who will read your code at some point. Then, you’ll find out there is one notion that will not be giving you a break – the notion of clean code. Clean code is code that is focused and understandable, which means it must be readable, logical, and changeable. Remember – good code is not the one computers understand; it is the one humans can understand. Often a program can be created in many ways, and code, in general, can be organized in several ways. Good practice implies you will choose the version that will be easiest to read and understand and will be the one that does not hinder your colleagues from updating it when necessary. They will likely work on top of your code, so it is best if they don’t lose time figuring out ideas beneath complex lines of code. That’s why the assumption is that, at your workplace, you will always type code cleanly – as simple as possible, perfectly organized, maintaining a steady logical flow. Good StyleNow, let’s focus on another aspect of coding in good style. When assigning names to variables or SQL objects, always choose shorter, meaningful names, conveying specific information. By ‘meaningful’, we mean names that are pronounceable, where one word per concept has been picked. For instance, that is the reason we chose “purchase number”, not “customer purchase unique number” in the “Sales” table. On that account, you need not be in a rush to choose names, since they must reflect as much of the object’s functionality as possible. Names will constitute more than 80% of your code, so it matters which ones you work with! It is often discussed whether capital or small letters must be used when coding. The truth is – it depends on your style or on the style of the company you are working for. Most often, professionals will capitalize the SQL keywords and will write objects’ names in a different way. When a name comprises more than one word, such as “purchase number”, words are usually either separated by an underscore or attached to each other, and each word starts with a capital letter. Both approaches are encountered in professional coding. One thing is sure, though – you can’t leave a blank space between words. MySQL will show an error message if you try to do that. Code ReadabilityThe third facet we will focus on in this post is the readability of your code. On one hand, this regards the horizontal and vertical organization of code, on the other, the color with which words are displayed. Technically, any SQL query can be written on a single line. However, many queries are too long and will become difficult to read if we do that. What needs to be done in such cases is to organize the code, not just horizontally, but also vertically. Depending on their meaning in a query and on the way we want them to be read, words can be written in different colors. SQL keywords are written in blue, objects’ names in black, numbers in orange, and so on. Maintain Your CodeAs we move on in the post, you will develop an impeccable organization of the code you write. For now, remember there are three main ways to maintain your code well: 1. Professionals use ad-hoc software that re-organizes code and colors different words consistently. In a more dynamic coding environment, time will be a factor, and unification of coding style will be a top priority. It is impossible to have 50 programmers in your company, all writing in the same style. It is unprofessional to merge code written in the same language but in a different style. So, when completed, pieces of working code go through the check of such a software, and your boss will have a pile of code all written in the same style. 2. Use the relevant analogical tool provided in Workbench. This little brush beautifies your code. The shortcut key combination to apply this function to the query where your cursor is located is Ctrl and B. You see? Awesome! 3. If you’d prefer to clean your code differently, you should intervene manually and adjust it as you like. ExerciseIn this simple line, we will create a test table with two columns – “Numbers” and “Words”. We’ve written the code on one line with small letters. Notice how MySQL changed the color of keywords, data types, and numbers. Now, we will not be dealing with ad-hoc software, as we are focusing on Workbench. After having placed the cursor somewhere in this query, you can press the little brush icon to reformat the script. To do the same operation faster, remember the Ctrl and B shortcut. Wow! It worked. Keywords are in capital letters, and the data for each column of the table starts on a new line. Great! Indentation StyleImagine you have a further preference regarding the horizontal organization of this code and the alignment of all data types. Using the tab button from your keyboard, you can set INT and VARCHAR to begin at the same horizontal point. According to some users, this trick further improves code’s readability. The technical term is called indentation. We say the column names and their data types were indented to the right. CommentsAnother aspect of maintaining good coding style is using comments. These are lines of text that Workbench will not run as code; they convey a message to someone who reads our code. Technically, in MySQL, you can start a comment by typing a forward slash and a star and close it reciprocally with a star and a forward slash. This approach is used mainly for large comments. Execute CodeFinally, I would like to elaborate on the lightning symbol, which helps you execute your code. Let’s start with the fact that your code is separated into blocks, as marked by the semi-colon separator. Ok. So, if you place your cursor on one block and then press the lightning icon or the Ctrl, Shift, Enter combination, SQL will run the selected and all remaining queries. That’s why, if I click on FROM and execute the code this way, I will see the output from the SELECT statement and will drop the test table. If you would like to run just the statement under the cursor, you must press the adjacent icon, where a lightning and a cursor are depicted. The corresponding keyboard combination is Ctrl and Enter. I can promise you will not stop using this keyboard combination, as it allows you to see the output of a certain query quickly, without having to run the entire SQL code. Well… I hope you liked this post! It was written to make you aware of the notion of clean coding and coding in good style. We consider these tools essential for good professional coding, so we would be happy if you can sense you started building good coding style habits. And that’s the end of our SQL tutorials. I hope you’ve enjoyed it and, of course, congratulations! P.S. You can find additional insights into SQL in our explainer videos: Relational Database Essentials, Database vs Spreadsheet, and Basic Database Terminology. What’s more, if you’re considering a career in data science, and you want to be in-the-know of what to expect during a job interview, feel free to check out our article SQL Interview Questions. The article first appeared on: https://365datascience.com/sql-best-practices/ The post SQL Best Practices – How to type code cleanly and perfectly organized appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/3gYuk9W
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What Are the Best Public Datasets for Machine Learning?In this day and age, the aspiration to automate and improve human related tasks with the help of computers is at the forefront. Today, this is mostly done through artificial intelligence (AI) and machine learning (ML). But, in reality, it is not that difficult to get into that part of data science. All you need is practice. And, in order to practice your machine learning skills, you need to train your models with data. Lots of data. Luckily, there is plenty of it available on the Internet for free. Yet still, you may be wondering where to begin and which of the thousands of machine learning datasets to choose. So, to help you get off to a good start, we have selected the 10 best free datasets for machine learning projects. We made sure the list we compiled covers all main topics of machine learning. Moreover, the projects get progressively more difficult as you go through the list. This way you can gradually improve your skills as you practice. Let’s get started, shall we? Top 10 Public Datasets for Machine Learning1. Boston House Price Dataset![]() The Boston House Price Dataset consists of the house prices in Boston area based on numerous factors, such as number of rooms, area, crime rates and many others. It is a perfect starting point for beginners to ML looking for easy machine learning projects, as you can practice your linear regression skills in order to predict what the price of a certain house should be. It is also a very popular machine learning dataset, so if you get stuck, you can find a lot of helpful resources about it online. 2. Iris Dataset![]() The Iris dataset is another dataset suitable for linear regression, and, therefore, for beginner machine learning projects. It contains information about the sizes of different parts of flowers. All these sizes are numerical, which makes it easy to get started and requires no preprocessing. The objective is pattern recognition – classifying flowers based on different sizes. 3. MNIST dataset![]() The MNIST dataset is the most popular dataset in Machine Learning. Practically everyone in the field has experimented on it at least once. It consists of 70,000 labeled images of handwritten digits (0-9). 60,000 of those are in the training set and 10,000 in the test set. The images themselves are 28×28 pixels and are in grayscale (meaning each pixel has 1 numeric value – how “white” it is). They have been heavily sanitized and preprocessed, so you don’t have to do much preprocessing yourselves. The popularity of this dataset stems from its ease of use and flexibility. Given the small size of the images you don’t have to worry much about training times, so you can experiment a lot with it. Coupled with the preprocessing, this makes it very smooth and fast to get started with. In addition, this dataset allows for many different models to work well. So, if you are a beginner, you can use the straightforward linear classifier, however, you can also try and practice a deeper network. Given that the input is images, this is a perfect playground for learning Convolutional Neural Networks (CNN). Overall, we encourage everyone to give this dataset a try. 4. Dog Breed Identification![]() The previous entry in our list (MNIST) was a transitional dataset from feed forward neural networks to Computer Vision. This one, Dog Breed Identification, is now firmly in the Computer Vision field. It is, as the name suggests, a dataset of images of different dog breeds. Your objective is to build a model that given an image can accurately predict which breed it is. So, you can transfer the CNN skills you obtained from the MNIST dataset and build upon them. 5. ImageNet![]() ImageNet is one of the best Machine Learning datasets out there, focused on Computer Vision. It has more than 1,000 categories of objects or people with many images associated with them. It even ran one of the biggest ML challenges – ImageNet’s Large-Scale Visual Recognition Challenge (ILSVRC), that produced many of the modern state-of-the-art Neural Networks. So, if you want to do Computer Vision, you will need this dataset. 6. Breast cancer Wisconsin diagnostic dataset![]() The Breast Cancer Wisconsin diagnostic dataset is another interesting machine learning dataset for classification projects is the breast cancer diagnostic dataset. Its design is based on the digitized image of a fine needle aspirate of a breast mass. In this digitized image, the features of the cell nuclei are outlined. For each cell nucleus, ten real-valued features are calculated, i.e., radius, texture, perimeter, area, etc. There are two types of predictions – benign and malignant. In this database, there are 569 instances which include 357 benign and 212 malignant. 7. Amazon Reviews Dataset![]() We are now entering the territory of Natural Language Processing (NLP). This is recommended for more advanced machine learning enthusiasts. The Amazon Review Dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). The data spans more than 20 years of reviews. 8. BBC News![]() Continuing with NLP, this time we have text classification, or more precise news classification. So, to develop your news classifier, you need a standard dataset. The BBC News dataset contains more than 2,200 articles in different categories, and it is your job to try and classify them. 9. YouTube Dataset![]() Now we have arrived to an even more advanced topic – video classification. The YouTube dataset containing uniformly sampled videos with high-quality labels and annotations. 10. Catching Illegal Fishing![]() This final dataset for machine learning projects is for the experts. There are many ships and boats in the oceans, and it is impossible to manually keep track of what everyone is doing. That is why, it has been suggested to develop a system that can identify illegal fishing activities through satellite and Geolocation data. Witch the Catching Illegal Fishing dataset, The Global Fishing Watch is offering real-time data for free, that can be used to build the system. That was Our List of Public Datasets for Machine Learning ProjectsBear in mind, that we have included interesting data sets for all skill levels and many different parts of machine learning research, however, there might be other, more specific datasets that also work for you. Machine Learning for BeginnersYou already have a good dataset for machine learning but don’t know how to use it? Well, in that case you can explore our machine learning and deep learning courses that are part of the 365 Data Science program. There, you can learn all the skills necessary to tackle the projects outlined in the list above. Try Machine Learning course for free! The post originally appeared on: https://365datascience.com/public-datasets-machine-learning/ The post The best public datasets for machine learning in 2020 appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/2Zfc1Hx You Want to Land Your Dream Job? Learn How to Impress Recruiters, Beat the Competition and Win Job Interviews!What you’ll learn
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Description Tired of sending your Resume into an online black hole? Interviewers explain to you that you do not have the right track record for the job? You are frustrated that the whole process is too slow and you would like to finally get a job? Don’t worry. You have come to the right place! We will teach you how to polish your Resume, how to make it SEO friendly (yeah that’s right!), and how to bypass hundreds of candidates. Nowadays large firms use applicant tracking systems to screen resumes before a person looks at them. They receive hundreds of resumes for every single position and “robots” help them separate “good” from “not so good” candidates. That is why you need to optimize your Resume for search. If you do that, you are three times more likely to be invited for an interview! Three times! Sounds good? Well, that’s not everything! This course will show you how to spin your experience in a way that Hiring Managers will consider you as the perfect candidate for the job opening that they have. You will be able to set your Resume apart from everyone else’s with just a few hours of work. Most people make the same mistakes and they do not even realize it. They fail to:
Learning how to avoid these mistakes will boost your chances significantly. Moreover, we will provide you with 8 professionally written Resume templates that were used by successful candidates who have careers in Investment Banking, Finance, Marketing and International Management. You can, of course, use their templates and wording when editing your own Resume. Sometimes the main difference between a successful and an unsuccessful application is your Cover Letter. A page of text that was written by you tells a lot about your skills and motivation to join the company that you are applying for. Don’t let your Cover Letter keep doors closed for you! Learn how to write a compelling Cover Letter that will make recruiters want to meet you. It is not difficult. In one hour we will explain to you what needs to be included in your Cover Letter and which are the mistakes that you should avoid. You will also receive 6 professionally written Cover Letter templates that you can use as example. So far so good. But what about interviews? Phone interviews, Live interviews, Group assessments, Difficult interview questions? No need to panic. You will gain an “unfair” advantage over other candidates by going through our lessons. You will be completely aware of what to expect in interviews and what will win the job for you at the end of the day. But what about the typical interview questions that always come up? Do you feel like your answers are like everybody else’s? You wouldn’t impress your recruiters by memorizing canned responses that they have heard a million times before. What you need to do is show them that you understand the question, and respond to it in a way that they want to hear. Good news! We have done the hard work. After careful research and going through numerous HR blogs, we filtered the 40 most frequently asked interview questions! They fall into six categories (Behavioral questions, Brainteasers, Difficult questions, Guesstimates, Situational and Qualification questions). Each of these questions comes with a suggested answer or solution that you can take into consideration when practicing your own answers. Afterwards, provided that you have doubts, we will gladly review any of your answers if you post them in the Discussion Board or as a personal message (whichever you prefer, of course). And what about LinkedIn? We have not forgotten LinkedIn given that it is one of the greatest tools that are at disposal to job seekers nowadays. We will show you how to be successful on LinkedIn, how to build your profile in a way that it is 30 times (yeah, that’s correct!) more likely to get you noticed by recruiters who are looking for people just like you! Here are some of the reviews that we have received so far for this course: “The course is structured and presented very well, and is great for graduate students to get a good grasp on learning how to find their first the job. The instructor’s lecture style is engaging and catches the listener’s attention easily, and helps the people enrolled in the course to learn very quickly.” Lilia, Udemy Student “I’m really glad I took this course because next year I will be looking for my first job and it really puts a lot of things in perspective. I think that the resume and cover letter advice will be really useful when i have to submit my own applications. good stuff!” Jean, Udemy Student Just one more thing. If you still haven’t decided whether you would like to take the course, please be aware that it comes with a 30-day money-back-in-full guarantee. This means that you can subscribe, study all of the lectures and decide for yourself whether it was worth it or not. If the answer is “No”, you can click a button and get your money back. No questions asked. So why not try it, right? After all, what is a well-paid career worth to you? Your starting salary would be a fraction of the price of this course. Every minute you are not on the job is actually Costing you money… Take the course today and improve your chances of being successful! Onwards and upwards!Who this course is for:
Join now!The post Job Search Success Strategies: Proven Job Hunting Strategies appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/32IDqCs Learn Python. Enjoy Python. Master Python. Become a Python Programmer.What you’ll learn
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Description Python Programmer Bootcamp 2020 This Python course is different. It will not only teach you Python, it will give you a problem solving super-power using Python code! And that will make all the difference, especially if you are pursuing a career in data science, AI, web development, big data, web testing, or programming for smart devices in Python. The author of this course, Giles McMullen-Klein, is a British programmer who went to Oxford University and used Python for his research there. Giles is one of the best-known Python and data science vloggers on YouTube where more than 133,000 subscribers follow his videos. There are several reasons why this course is different and why Giles could be the perfect Python teacher for you: · Engaging, informative and fun! Giles’ lectures are entertaining and will inspire you to learn Python · Motivating ,enthusiastic and effective – Giles’ passion for coding in Python and teaching the language is infectious · Develop a thorough understanding of Python · Carefully crafted lectures and superb quality of production (Full HD videos) + animations and callouts · Practice, practice, practice – the course contains dozens of exercises to help you master the Python programming concepts covered in the lessons · Giles’ English accent Have you always wanted to learn one of the world’s most popular programming languages? If so, this is the perfect course for you. It will teach you how to program in Python and help to prepare you for coding challenges frequently posed during job interviews. Giles’ teaching style builds a connection with students. And what’s more – he’s there for you if you need any help. Just post any queries or questions in the course Q&A section. In this comprehensive course, we will cover several key topics: ⁃ Why program? Why study Python? ⁃ How to install Python ⁃ Hands-on programming with strings ⁃ Print function ⁃ Variables ⁃ Conditionals ⁃ Loops ⁃ Data structures ⁃ Modules ⁃ Files ⁃ OOP ⁃ Time complexity ⁃ Big O ⁃ Stacks ⁃ Debugging There are many exercises throughout the course, some of our favourites are: ⁃ The Sierpinski Triangle ⁃ The Towers of Hanoi ⁃ And the Computer Vision capstone project 365 Careers’ team is very excited about this project. The creation of a Python course has been an ambition of ours for quite some time. but as we were not prepared to make any compromises on the quality of the course content, we needed to choose the right partner. Luckily, Giles was as excited about working with us as we were with him and together, after much hard, work we have created what we believe to be a first-class learning experience. We are confident that programming novices will benefit from Giles’ authenticity combined with our visual approach to teaching which includes our much-loved graphics and animations. To date, employees from 80 of the Fortune 100 companies have taken our courses. 600,000 students have given us an average rating of 4.5 stars. And we are confident that this is the perfect course for you if you want to become a Python programmer. Sounds great, doesn’t it? Are you ready for a life-changing adventure? If you are serious about learning Python, this is the only bootcamp you will need. The course comes with a 30-day money-back guarantee. If you decide it wasn’t for you, you will be refunded in full (+keep all the downloaded resources free of charge)! No risk for you, so go ahead, click the “Buy now” button and start your Python programmer journey today!Who this course is for:
Join now!The post The Complete Python Programmer Bootcamp 2020 appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/3j9haIq See the full picture: Learn how to combine the three most important tools in data science: Python, SQL, and Tableau.What you’ll learn
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Description Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language; SQL is the most widely used means for communication with database systems; Tableau is the preferred solution for data visualization; To put it simply – SQL helps us store and manipulate the data we are working with, Python allows us to write code and perform calculations, and then Tableau enables beautiful data visualization. A well-thought-out integration stepping on these three pillars could save a business millions of dollars annually in terms of reporting personnel. Therefore, it goes without saying that employers are looking for Python, SQL, and Tableau when posting Data Scientist and Business Intelligence Analyst job descriptions. Not only that, but they would want to find a candidate who knows how to use these three tools simultaneously. This is how recurring data analysis tasks can be automated. So, in this course we will to teach you how to integrate Python, SQL, and Tableau. An essential skill that would give you an edge over other candidates. In fact, the best way to differentiate your job resume and get called for interviews is to acquire relevant skills other candidates lack. And because, we have prepared a topic that hasn’t been addressed elsewhere, you will be picking up a skill that truly has the potential to differentiate your profile. Many people know how to write some code in Python. Others use SQL and Tableau to a certain extent. Very few, however, are able to see the full picture and integrate Python, SQL, and Tableau providing a holistic solution. In the near future, most businesses will automate their reporting and business analysis tasks by implementing the techniques you will see in this course. It would be invaluable for your future career at a corporation or as a consultant, if you end up being the person automating such tasks. Our experience in one of the large global companies showed us that a consultant with these skills could charge a four-figure amount per hour. And the company was happy to pay that money because the end-product led to significant efficiencies in the long run. The course starts off by introducing software integration as a concept. We will discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints. Then, we will continue by introducing the real-life example exercise the course is centered around – the ‘Absenteeism at Work’ dataset. The preprocessing part that follows will give you a taste of how BI and data science look like in real-life on the job situations. This is extremely important because a significant amount of a data scientist’s work consists in preprocessing, but many learning materials omit that Then we would continue by applying some Machine Learning on our data. You will learn how to explore the problem at hand from a machine learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it. A truly comprehensive ML exercise. Connecting Python and SQL is not immediate. We have shown how that’s done in an entire section of the course. By the end of that section, you will be able to transfer data from Jupyter to Workbench. And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together. As you can see, this is a truly comprehensive data science exercise. There is no need to think twice. If you take this course now, you will acquire invaluable skills that will help you stand out from the rest of the candidates competing for a job. Also, we are happy to offer a 30-day unconditional no-questions-asked-money-back-in-full guarantee that you will enjoy the course. So, let’s do this! The only regret you will have is that you didn’t find this course sooner!Who this course is for:
Enroll now!The post Python + SQL + Tableau: Integrating Python, SQL, and Tableau appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/2YpucJZ A complete data science case study: preprocessing, modeling, model validation and maintenance in Python.What you’ll learn
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Description Brand new course!! Hi! Welcome to Credit Risk Modeling in Python. The only online course that teaches you how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This is the perfect course for you, if you are interested in a data science career. Here’s why: · The instructor is a proven expert (PhD from the Norwegian Business school, who has taught in world renowned universities such as HEC, the University of Texas, and the Norwegian Business school). · The course is suitable for beginners. We start with theory and initial data pre-processing and gradually solve a complete exercise in front of you · Everything we cover is up-to-date and relevant in today’s development of Python models for the banking industry · This is the only online course that shows the complete picture in credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation – PD, LGD, and EAD) including creating a scorecard from scratch · Here we show you how to create models that are compliant with Basel II and Basel III regulations that other courses rarely touch upon · We are not going to work with fake data.The dataset used in this course is an actual real-world example · You get to differentiate your data science portfolio by showing skills that are highly demanded in the job marketplace · What is most important – you get to see first-hand how a data science task is solved in the real-world Most data science courses cover several frameworks, but skip the pre-processing and theoretical part. This is like learning how to taste wine before being able to open a bottle of wine. We don’t do that. Our goal is to help you build a solid foundation. We want you to study the theory, learn how to pre-process data that does not necessarily come in the ‘’friendliest’’ format, and of course, only then we will show you how to build a state of the art model and how to evaluate its effectiveness. Throughout the course, we will cover several important data science techniques. – Weight of evidence – Information value – Fine classing – Coarse classing – Linear regression – Logistic regression – Area Under the Curve – Receiver Operating Characteristic Curve – Gini Coefficient – Kolmogorov-Smirnov – Assessing Population Stability – Maintaining a model Along with the video lessons you will receive several valuable resources that will help you learn as much as possible: · Lectures · Notebook files · Homework · Quiz questions · Slides · Downloads · Access to Q&A where you could reach out and contact the course tutor. Signing up for the course today could be a great step towards your career in data science. Make sure that you take full advantage of this amazing opportunity! See you on the inside!Who this course is for:
Enroll now!The post Credit Risk Modeling in Python 2020 appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/31fyu8q R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R.What you’ll learn
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Description R Programming for Statistics and Data Science 2020 R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn’t you? Data scientist is the hottest ranked profession in the US. But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title. This course is packing all of this, and more, in one easy-to-handle bundle, and it’s the perfect start to your journey. So, welcome to R for Statistics and Data Science! R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’. Laying strong foundations This course wastes no time and jumps right into hands-on coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R. What makes this course different from other courses?
Receive top class training with content which we’ve built – and rigorously edited – to deliver powerful and efficient results. Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.
We will take you through descriptive statistics and the fundamentals of inferential statistics. We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills. You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.
Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations.
Learn to work with vectors, matrices, data frames, and lists. Become adept in ‘the Tidyverse package’ – R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it. Create meaning-heavy data visualizations and plots.
Reinforce your learning through numerous practical exercises, made with love, for you, by us. What about homework, projects, & exercises? There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck. You: Is there something to show for the skills I will acquire? Us: Indeed, there is – a verifiable certificate. You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R. If that sounds good to you, then welcome to the classroom
Enroll now!The post R Programming for Statistics and Data Science 2020 appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/2Fz9xwy Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting.What you’ll learn
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Description How does a commercial bank forecast the expected performance of their loan portfolio? Or how does an investment manager estimate a stock portfolio’s risk? Which are the quantitative methods used to predict real-estate properties? If there is some time dependency, then you know it – the answer is: time series analysis. This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist. In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also: · Easy to understand · Comprehensive · Practical · To the point · Packed with plenty of exercises and resources But we know that may not be enough. We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind… Welcome to Time Series Analysis in Python! The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis. We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards. Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima. With these tools we will master the most widely used models out there: · AR (autoregressive model) · MA (moving-average model) · ARMA (autoregressive-moving-average model) · ARIMA (autoregressive integrated moving average model) · ARIMAX (autoregressive integrated moving average model with exogenous variables) . SARIA (seasonal autoregressive moving average model) . SARIMA (seasonal autoregressive integrated moving average model) . SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables) · ARCH (autoregressive conditional heteroscedasticity model) · GARCH (generalized autoregressive conditional heteroscedasticity model) . VARMA (vector autoregressive moving average model) We know that time series is one of those topics that always leaves some doubts. Until now. This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included. What you get? · Active Q&A support · Supplementary materials – notebook files, course notes, quiz questions, exercises · All the knowledge to get a job with time series analysis · A community of data science enthusiasts · A certificate of completion · Access to future updates · Solve real-life business cases that will get you the job We are happy to offer a 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it. Why wait? Every day is a missed opportunity. Click the “Buy Now” button and start mastering time series in Python today.Who this course is for:
Join now!The post Time Series Analysis in Python 2020 appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/3atTviE Julia was designed from the start for scientific and numerical computation.Thus it’s no surprise that Julia has many features advantageous for such use cases:
Source: InfoWorld The post Julia vs Python in 2020 appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/3kw2swA Multi-set Bar Chart is a variation of a Bar Chart is used when two or more data series are plotted side-by-side and grouped together under categories, all on the same axis.Like a Bar Chart, the length of each bar is used to show discrete, numerical comparisons amongst categories. Each data series is assigned an individual colour or a varying shade of the same colour, in order to distinguish them. Each group of bars are then spaced apart from each other. The use of Multi-set Bar Charts is usually to compare grouped variables or categories to other groups with those same variables or category types. Multi-set Bar Charts can also be used to compare mini Histograms to each other, so each bar in the group would represent the significant intervals of a variable. The downside of Multi-set Bar Charts is that they become harder to read the more bars you have in one group. The post What is Multi-set Bar Chart in Data Visualization appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/33JqVJ4 |