Data warehousing is one of the hottest topics both in business and in data science. But if you’re new to the field, you’re probably wondering what a data warehouse is, why we need it, and how it works. Don’t worry because in 4 minutes you’ll know the answers to all these questions.First, let’s start with a definition: what is the meaning of the phrase: ‘Single source of truth’. In information systems theory, the ‘single source of truth’ is the practice of structuring all the best quality data in one place. But what if you knew that there is one single place where you would always have the single source of information? That would be quite helpful wouldn’t it? Well, a data warehouse exists to fill that need. So, what is a data warehouse exactly?It is the place where companies store their valuable data assets, including customer data, sales data, employee data, and so on. In short, a data warehouse is the de facto ‘single source of data truth’ for an organization. It is usually created and used primarily for data reporting and analysis purposes. The post What Is a Data Warehouse? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-a-data-warehouse/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-a-data-warehouse In this tutorial, we’re going to help you understand Python Data Types. Specifically, the data types in Python that we will look at are NUMBERS, BOOLEAN, and STRINGS.The post Understanding Python Data Types in 10 minutes appeared first on Data Science PR. Via https://datasciencepr.com/understanding-python-data-types-in-10-minutes/?utm_source=rss&utm_medium=rss&utm_campaign=understanding-python-data-types-in-10-minutes This Python Tutorial for Beginners includes everything you need to know about programming if you are just getting started. In this Introduction to Programming video, we’re also going to show you how to install both Python and Jupyter for a smooth start to your programming adventure! The post Python Tutorial for Beginners: Introduction to Programming in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/python-tutorial-for-beginners-introduction-to-programming-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=python-tutorial-for-beginners-introduction-to-programming-in-2020 This tutorial provides and introduction to Databases, SQL and the open source relational database – MySQL.What is MySQL?MySQL is an open-source relational database management system. Its name is a combination of “My”, the name of co-founder Michael Widenius’s daughter, and “SQL”, the abbreviation for Structured Query Language. The post MySQL TUTORIAL IN 10 MINUTES appeared first on Data Science PR. Via https://datasciencepr.com/mysql-tutorial-in-10-minutes/?utm_source=rss&utm_medium=rss&utm_campaign=mysql-tutorial-in-10-minutes Here’s our list of the 10 most common misconceptions about AI which we debunked with genuine pleasure! Misconceptions about AI are spreading like wildfire. It seems that the rapid technological advancements inspire various myths about people losing their jobs and the demise of humanity as a whole. At the same time, the sci-fi genre paints a dystopian future where robots have taken over. You have probably heard some of those myths. You may even find some of them believable. That’s why we compiled a list of the 10 most common misconceptions about AI and debunked them with genuine pleasure. One by one. So, before you end up in a heated argument with your friends about whether there’s a difference between AI and ML, check out the video and get the facts. The post Debunking 10 Common Misconceptions about AI in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/debunking-10-common-misconceptions-about-ai-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=debunking-10-common-misconceptions-about-ai-in-2020 What is segmentation, targeting and positioning? Learn customer analytics, data science, and how the two work together!Leading companies are always on the lookout for savvy data scientists to join their fast-growing Customers Analytics teams. In that sense, considering a career as a data scientist in customer analytics is a super smart choice. But here’s why exactly:
The post Customer Analytics Tutorial – Segmentation, Targeting and Positioning appeared first on Data Science PR. Via https://datasciencepr.com/customer-analytics-tutorial-segmentation-targeting-and-positioning/?utm_source=rss&utm_medium=rss&utm_campaign=customer-analytics-tutorial-segmentation-targeting-and-positioning Levels of measurement can be split into two groups: qualitative and quantitative data.They are very intuitive, so don’t worry. Qualitative data can be nominal or ordinal. Nominal variables are like the categories we talked about just now – Mercedes, BMW or Audi, or like the four seasons – winter, spring, summer and autumn. They aren’t numbers and cannot be put in any order. Ordinal data, on the other hand, consists of groups and categories but follows a strict order. Imagine you have been asked to rate your lunch and the options are: disgusting, unappetizing, neutral, tasty, and delicious. Although we have words and not numbers, it is obvious that these preferences are ordered from negative to positive, thus the data is qualitative, ordinal. Okay, so what about quantitative variables? Well, as you may have guessed by now, they are also split into two groups: interval and ratio. Intervals and ratios are both represented by numbers but have one major difference. Ratios have a true zero and intervals don’t. For example, length is a ratio variable. You all know that 0 inches or 0 feet means that there is no length. With temperature, however, we have a different story. It is usually an interval variable. Let me explain. Usually, it is expressed in Celsius or Fahrenheit. They are both interval variables. 0 degrees Celsius or 0 degrees Fahrenheit don’t not mean anything, as the absolute zero temperature is actually -273.15 degrees Celsius, or -459.67 degrees Fahrenheit. However, we can easily say that 80 degrees Fahrenheit is less than 100 degrees Fahrenheit. In the case of interval variables, the difference is meaningful, but the 0 is not. Continuing this temperature example, there is another scale – Kelvin’s. According to it, the absolute minimum temperature is 0 degrees Kelvin. Therefore, if the degrees are stated in Kelvin’s the variable will be a ratio. So. Numbers like 2, 3, 10, 10.5, Pi, etc. can be both interval or ratio, but you have to be careful with the context you are operating in. Alright! We’ve quickly gone through the types of data and the measurement levels. The post Levels of measurement Tutorial in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/levels-of-measurement/?utm_source=rss&utm_medium=rss&utm_campaign=levels-of-measurement Illustration Diagrams are graphics that display an image, or images, accompanied by either notes, labels or a legend, in order to:
Images used can come in the form of illustrations, rough sketches, wire-frames or photographs. Therefore, images can be either symbolic, pictorial or realistic. Sometimes enlargements and cross-sections are used for more in-depth analysis or displaying more detail. The post What is an Illustration Diagram in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-an-illustration-diagram-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-an-illustration-diagram-in-data-visualization A Histogram visualises the distribution of data over a continuous interval or certain time period. Each bar in a histogram represents the tabulated frequency at each interval/bin.Histograms help give an estimate as to where values are concentrated, what the extremes are and whether there are any gaps or unusual values. They are also useful for giving a rough view of the probability distribution. The post What is a Histogram in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-a-histogram-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-a-histogram-in-data-visualization It is an understatement to say we are thrilled to announce our brand new partnership with Tomorrowland. Everybody knows how iconic this music festival is, for they have left a recognizable mark on our industry.In case you have been living under a rock, here’s a few sentences regarding Tomorrowland… First held in 2005, the Belgian electronic dance music festival has become truly legendary because of numerous reasons. Selling out in just minutes, announcing a line-up of 400 DJs in 2012, and receiving the “Best Global Festival” award for five consecutive years are only a few. So what does Tomorrowland have in store for us in 2020?Due to the current COVID-19 pandemic, the festival won’t be taking place in Boom, Antwerp, Belgium this summer. Instead, we will be experiencing Tomorrowland Around The World — a global online festival taking place on July 25th and 26th, 2020. Yup, in just three days!! The digital festival will include a line-up of more than 60 artists who will be premiering brand new content exclusively over the course of the weekend. The incredible line-up features big names like Lost Frequencies, Adam Beyer, Katy Perry, Tale of Us, Tiesto, and the Tomorrowland regular David Guetta. As the event is typically held during the last two weekends of July, Tomorrowland’s global community has been asking the organizers to provide them with something special amidst the current situation in the world, and due to the fact, the festival has been officially postponed to July 2021.
Where and how is this digital experience happening you might be thinking.Being the people of tomorrow, the team behind Tomorrowland has certainly created and delivered well beyond our expectations (and they were high already, let me tell you). Taking us to Pāpiliōnem, a magical island, home to all the wonders of nature, they are promising us all we’ve been wanting, and even more: freedom, purity, and music. Isn’t that what Tomorrowland has always been about? ![]() ![]() Immersive and interactive, the festival will consist of a variety of exciting activities, not only musical acts. The program invites us to a series of inspirational webinars, games, and workshops, all related to lifestyle, food, and fashion. The so-called “Inspiration Sessions” are extra content in which international role models will be giving talks from the comfort of their homes. Loved by millions, these people will surely have fascinating stories and meaningful messages to share, and some of the many impressive names on the list include Black Eyed Peas frontman will.i.am, and former NBA champion and basketball legend Shaquille O’Neal. Another appealing detail worth mentioning is that most tickets and packages will include an access code to Tomorrowland’s Relive platform where festival-goers will be able to enjoy and relive all performances in the week following the event. So what is our role in all this?Evedo is partnering up with Tomorrowland to popularize their upcoming Around The World digital edition, especially for the Bulgarian market. This is an important strategic move for us as we believe that this is only the first step towards many future collaborations. We plan and hope to continue working with Tomorrowland, promoting and representing their festival in Bulgaria and around the world, and doing more efforts and projects within the ticketing space where we have one of our biggest strengths as a company. In typical Tomorrowland fashion, as they’ve been doing this for several years now (even in pre-COVID-19 times), they are inviting us to gather at home and to immerse ourselves into an extraordinary world of wonder and music. We will set up tents in the backyard, stir up delicious cocktails in the living room and, of course, dress up in our craziest festival attire (lots of glitter or whatever does the trick for ya!), while getting together to safely and responsibly experience Tomorrowland in 2020. This year has been strange, and honestly quite daunting for most of us, but it’s time to return to the dancefloor. Tomorrowland Around The World will without doubt be a once-in-a-lifetime experience and we plan to make the most of it. See you at the Atmosphere stage in just three days — it’s a date. You can learn more about Tomorrowland Around The World and grab your tickets here. If you want to become a part of the event change follow our community channels: Website: www.evedo.co Email: [email protected] Telegram: https://t.me/evedoco Facebook: https://www.facebook.com/evedo.co Twitter: https://twitter.com/evedotoken Reddit: https://www.reddit.com/r/Evedo Instagram: https://www.instagram.com/evedo.co The post Evedo partners up with Tomorrowland Around The World appeared first on Data Science PR. Via https://datasciencepr.com/evedo-partnership-tomorrowland/?utm_source=rss&utm_medium=rss&utm_campaign=evedo-partnership-tomorrowland Heatmaps visualise data through variations in colouring. When applied to a tabular format, Heatmaps are useful for cross-examining multivariate data, through placing variables in the rows and columns and colouring the cells within the table.Heatmaps are good for showing variance across multiple variables, revealing any patterns, displaying whether any variables are similar to each other, and for detecting if any correlations exist in-between them. Typically, all the rows are one category (labels displayed on the left or right side) and all the columns are another category (labels displayed on the top or bottom). The individual rows and columns are divided into the subcategories, which all match up with each other in a matrix. The cells contained within the table either contain colour-coded categorical data or numerical data, that is based on a colour scale. The data contained within a cell is based on the relationship between the two variables in the connecting row and column. A legend is required alongside a Heatmap in order for it to be successfully read. Categorical data is colour-coded, while numerical data requires a colour scale that blends from one colour to another, in order to represent the difference in high and low values. A selection of solid colours can be used to represent multiple value ranges (0-10, 11-20, 21-30, etc) or you can use a gradient scale for a single range (for example 0 – 100) by blending two or more colours together. Because of their reliance on colour to communicate values, Heatmaps are a chart better suited to displaying a more generalised view of numerical data, as it’s harder to accurately tell the differences between colour shades and to extract specific data points from (unless of course, you include the raw data in the cells). Heatmaps can also be used to show the changes in data over time if one of the rows or columns are set to time intervals. An example of this would be to use a Heatmap to compare the temperature changes across the year in multiple cities, to see where’s the hottest or coldest places. So the rows could list the cities to compare, the columns contain each month and the cells would contain the temperature values. The post What is a Heatmap in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-a-heatmap-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-a-heatmap-in-data-visualization Commonly used as an organisational tool for project management, Gantt Charts display a list of activities (or tasks) with their duration over time, showing when each activity starts and ends. This makes Gantt Charts useful for planning and estimating how long an entire project might take. You can also see what activities are running in parallel to each other. Gantt Charts are drawn within a table: rows are used for the activities and columns are used as the timescale. The duration of each activity is represented by the length of a bar plotted along this timescale. The start of the bar is the beginning of the activity and the end of the bar is when the activity should finish. Colour-coding the bars can be used to categorise the activities into groups. To show the percentage of completion of an activity, a bar can be partially filled in, shaded differently or use a different colour, to differentiate between what is done and what is left to do. Connecting arrows can be used to show which tasks are dependent on each other. Critical paths, the key activities required to finish the project can also be displayed with a series of highlighted arrows. Symbols can also be placed within a Gantt Chart to signify milestones and a vertical line running through the chart is used to highlight the current date. The post What is Gantt Chart in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-gantt-chart-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-gantt-chart-in-data-visualization Flow Maps geographically show the movement of information or objects from one location to another and their amount.Typically Flow Maps are used to show the migration data of people, animals and products. The magnitude or amount of migration in a single flow line is represented by its thickness. This helps to show how migration is distributed geographically. Flow Maps are drawn from a point of origin and branch out of their “flow lines”. Arrows can be used to show direction, or if the movement is incoming or outgoing. Drawing flow lines without arrows can be used to represent trade going back-and-forth. Merging/bundling flow lines together and avoiding crossovers can help to reduce visual clutter on the map. The post What is Flow Map in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/flow-maps/?utm_source=rss&utm_medium=rss&utm_campaign=flow-maps As known as Flow Diagram, Flow Process Chart, Process Chart, Process Map, Process Model, Work Flow Diagram.This type of diagram is used to show the sequential steps of a process. Flow Charts map out a process using a series of connected symbols, which makes the process easy to understand and aids in its communication to other people. Flow Charts are useful for explaining how a complex and/or abstract procedure, system, concept or algorithm work. Drawing a Flow Chart can also help in planning and developing a process or improving an existing one. Symbols are divided up and standardised into different types that each have their own particular shape. Labels for each step are written inside of the symbol shape. Flow Charts begin and end with a curved rectangle to signify the start and finishing of the process. Lines or arrows are used to show the direction of flow from one step in the process to another. Simple instructions or actions are represented by a rectangle. While a diamond shape is used when a decision is needed. There are also many other symbols that can be used in Flow Chart. Flow Charts can run horizontally or vertically. The post What is a Flow Chart in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-a-flow-chart-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-a-flow-chart-in-data-visualization Although not a chart outright, Error Bars function as a graphical enhancement that visualises the variability of the plotted data on a Cartesian graph. Error Bars can be applied to graphs such as Scatterplots, Dot Plots, Bar Charts or Line Graphs, to provide an additional layer of detail on the presented data.Error Bars help to indicate estimated error or uncertainty to give a general sense of how precise a measurement is. This is done through the use of markers drawn over the original graph and its data points. Typically, Error bars are used to display either the standard deviation, standard error, confidence intervals or the minimum and maximum values in a ranged dataset. To visualise this information, Error Bars work by drawing cap-tipped lines that extend from the centre of the plotted data point (or edge with Bar Charts). The length of an Error Bar helps reveal the uncertainty of a data point: a short Error Bar shows that values are concentrated, signalling that the plotted average value is more likely, while a long Error Bar would indicate that the values are more spread out and less reliable. Also depending on the type of data, the length of each pair of Error Bars tend to be of equal length on both sides. However, if the data is skewed, then the lengths on each side would be unbalanced. Error Bars always run parallel to a quantitative scale axis, so they can be displayed either vertically or horizontally, depending on whether the quantitative scale is on the Y or X axis. If there are two quantitative scales, then two pairs of Error Bars can be used for both axes. The post Data Visualization in 2020: Error Bars Explained appeared first on Data Science PR. Via https://datasciencepr.com/error-bars-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=error-bars-data-visualization Dot Matrix Charts display discreet data in units of dots, each coloured to represent a particular category and grouped together in a matrix.They are used to give a quick overview of the distribution and proportions of each category in a data set and also to compare distribution and proportion across other datasets, in order to discover patterns. When only one variable/category is used in the dataset and all the dots are the same colour, a Dot Matrix Chart can be used to primarily show proportions. The post What is a Dot Matrix Chart in Data Visualization appeared first on Data Science PR. Via https://datasciencepr.com/dot-matrix-chart/?utm_source=rss&utm_medium=rss&utm_campaign=dot-matrix-chart Also known as a Point Map, Dot Distribution Map, Dot Density Map.Dot Maps are a way of detecting spatial patterns or the distribution of data over a geographical region, by placing equally sized points over a geographical region. There are two types of Dot Map: one-to-one (one point represents a single count or object) and one-to-many (one point represents a particular unit, e.g. 1 point = 10 trees). Dot Maps are ideal for seeing how things are distributed over a geographical region and can reveal patterns when the points cluster on the map. Dot Maps are easy to grasp and are better at giving an overview of the data, but are not great for retrieving exact values. The post What is a Dot Map in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-a-dot-map-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-a-dot-map-in-data-visualization A donut chart is essentially a Pie Chart with an area of the centre cut out.Pie Charts are sometimes criticised for focusing readers on the proportional areas of the slices to one another and to the chart as a whole. This makes it tricky to see the differences between slices, especially when you try to compare multiple Pie Charts together. A Donut Chart somewhat remedies this problem by de-emphasizing the use of the area. Instead, readers focus more on reading the length of the arcs, rather than comparing the proportions between slices. Also, Donut Charts are more space-efficient than Pie Charts because the blank space inside a Donut Chart can be used to display information inside it. The post Donut Chart in Data Visualization appeared first on Data Science PR. Via https://datasciencepr.com/donut-chart-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=donut-chart-in-data-visualization You can ask around, read Quora answers, or talk to someone in the industry, sure, these methods will supply you with information, but there’s no doubt that this information will be biased towards someone else’s personal experience. How others became data scientists is of little importance to you, I bet. What you’re interested in is whether YOU can become one. Are your skills appropriate for this field? What steps do you need to take to become a successful data scientist? Will your background affect the chances of becoming a data scientist? All valid questions. In this article, we will have a look at the best Data Science course on Udemy in 2020: The Problem Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist. And how can you do that? Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming) Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture The Solution Data science is a multidisciplinary field. It encompasses a wide range of topics.
“If you’re trying to prepare for an eventual career in finance, but are still looking to round out your knowledge of the subject, The Complete Financial Analyst Course might be a perfect fit for you.”, Business Insider “A Financial Analyst Career is one of the top-paying entry-level jobs on the market.” “Even in the toughest job markets, the best candidates find great positions.”, Forbes You simply have to find a way to acquire practical skills that will give you an edge over the other candidates. But how can you do that? You haven’t had the proper training, and you have never seen how analysts in large firms do their work … Stop worrying, please! We are here to help. The Complete Financial Analyst Course is the most comprehensive, dynamic, and practical course you will find online. It covers several topics, which are fundamental for every aspiring Financial Analyst:
As you can see, this is a complete bundle that ensures you will receive the right training for each critical aspect. Here comes the fun part! We have a challenge for you! After covering each major roadblock, you will be asked to solve a challenge. You will:
Sounds interesting, right? At the end of the challenge, you will send us the work you’ve done, and we will reply with personalized feedback. This makes for an interactive student experience that optimizes what you will learn from the course. What makes this course different from the rest of the Finance courses out there?
Why should you consider a career as a Financial Analyst?
Please don’t forget that the course comes with Udemy’s 30-day unconditional, money-back-in-full guarantee. And why not give such a guarantee, when we are convinced the course will provide a ton of value for you? Welcome to The Business Intelligence Analyst Course, the only course you need to become a BI Analyst. We are proud to present you this one-of-a-kind opportunity. There are several online courses teaching some of the skills related to the BI Analyst profession. The truth of the matter is that none of them completely prepare you. Our program is different than the rest of the materials available online. It is truly comprehensive. The Business Intelligence Analyst Course comprises of several modules:
These are the precise technical skills recruiters are looking for when hiring BI Analysts. And today, you have the chance of acquiring an invaluable advantage to get ahead of other candidates. This course will be the secret to your success. And your success is our success, so let’s make it happen! Here are some more details of what you get with The Business Intelligence Analyst Course:
Sounds amazing, right? Our courses are unique because our team works hard to:
We are proud to present Python for Finance: Investment Fundamentals and Data Analytics – oneof the mostinteresting and complete courses we have created so far. It took our team slightly over four months to create this course, but now, it is ready and waiting for you. An exciting journey from Beginner to Pro. If you are a complete beginner and you know nothing about coding, don’t worry! We start from the very basics. The first part of the course is ideal for beginners and people who want to brush up on their Python skills. And then, once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks. Finance Fundamentals. And it gets even better! The Finance block of this course will teach you in-demand real-world skills employers are looking for. To be a high-paid programmer, you will have to specialize in a particular area of interest. In this course, we will focus on Finance, covering many tools and techniques used by finance professionals daily:
Everything is included! All these topics are first explained in theory and then applied in practice using Python. Is there a better way to reinforce what you have learned in the first part of the course? This course is great, even if you are an experienced programmer, as we will teach you a great deal about the finance theory and mechanics you will need if you start working in a finance context. Teaching is our passion. Everything we teach is explained in the best way possible. Plain and clear English, relevant examples and time-efficient videos. Don’t forget to check some of our sample videos to see how easy they are to understand. If you have questions, contact us! We enjoy communicating with our students and take pride in responding within the 1 business day. Our goal is to create high-end materials that are fun, exciting, career-enhancing, and rewarding. What makes this course different from the rest of the Programming and Finance courses out there?
How important is database management in the age of big data and analytics? It is really important. How many employers would be happy to hire employees who can use data for the purposes of business intelligence? All of them. How many people have these skills? Not enough. This is why now is the time to learn SQL and gain a competitive advantage in the job market. Remember, the average salary of a SQL developer is $92,000! That’s a lucrative career. How come? Well, when you can work with SQL, it means you don’t have to rely on others sending you data and executing queries for you. You can do that on your own. This allows you to be independent and dig deeper into the data to obtain the answers to questions that might improve the way your company does its business. For instance, Database management is the foundation for data analysis and intelligent decision making. Worried that you have no previous experience? Not an issue. We will start from the very basics and gradually teach you everything you need to know. Step by step. With no steps skipped. Why take this course in particular? Isn’t it like the rest of the SQL courses out there? We would like to think it isn’t. Our team worked hard to create a course that is:
Some of these aspects have been covered in other courses. Others haven’t. However, no one provides such a variety of topics in one place. We firmly believe this course is the best training material out there. It is a truly interactive experience preparing you for a real-life working environment. Do you want to learn how to use Excel in a real working environment? Are you about to graduate from university and start looking for your first job? Are you a young professional looking to establish yourself in your new position? Would you like to become your team’s go-to person when it comes to Financial Modeling in Excel? If you answered yes to any of these, then this is the right course for you! Join over 119,002 successful students taking this course! The instructor of this course has extensive experience in Financial Modeling:
Learn the subtleties of Financial Modeling from someone who has walked the same path. Beat the learning curve and stand out from your colleagues. A comprehensive guide to Financial Modeling in Excel:
What we offer:
By completing this course, you will:
Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist? Well then, you’ve come to the right place! Statistics for Data Science and Business Analysis is here for you with TEMPLATES in Excel included! This is where you start. And it is the perfect beginning! In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. We have created a course that is:
It is no secret that a lot of these topics have been explained online. Thousands of times. However, it is next to impossible to find a structured program that gives you an understanding of why certain statistical tests are being used so often. Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction. 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 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. Are you about to graduate from university and start looking for your first job? Are you a young professional who wants to establish themselves at their new position? Would you like to become your team’s go-to person when it comes to creating important PowerPoint presentations? If so, then this is the right course for you! It certainly pays off to be able to create great-looking PowerPoint presentations from scratch:
The instructor of the course has extensive experience with PowerPoint. His slides have been in front of some of the most influential executives in Europe. Learn how to organize your presentations in a professional manner, exactly like employees of Fortune100 companies do. Beginner to Pro in PowerPoint: Complete PowerPoint Training is THE ONLY course that will teach you how to prepare professional business presentations that are identical to the ones that are delivered by major investment banks and consulting firms. It’s a one-stop-shop for everything you need in order to create sophisticated presentations. In the first part of the course, we will cover PowerPoint’s basic tools. This makes the course appropriate for beginners and inexperienced users. Once we have done that, we will explore some advanced features, which are often neglected by average users. The third part of the course is a case study. We will go through the entire thought process that is necessary to create a well-structured company presentation. And then, we’ll create the actual presentation. You will gain first-hand experience on how to design great-looking PowerPoint slides! Get excited! This course is an opportunity to beat the learning curve and stand out from the crowd. Here’s what you get with Beginner to Pro in PowerPoint: Complete PowerPoint Training:
In addition to that you will receive:
By taking this course you will have every chance to:
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. The post List of the Best Data Science Courses on Udemy in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/list-of-the-best-data-science-courses-on-udemy-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=list-of-the-best-data-science-courses-on-udemy-in-2020 Python is a general-purpose interpreted programming language used for web development, machine learning, and complex data analysis. Python is a perfect language for beginners as it is easy to learn and understand.As the popularity of the language is soaring, the opportunities in Python programming are amplifying, as well. If you wish to learn Python programming, there are plenty of books available in the market. Books provide you the ability to learn at your on time even if you are on the go and they go really in detail. We bring you a list of the best Python books for beginners and advanced programmers. Python Crash Course‘Python Crash Course’ by Eric Matthews is a fast-paced and comprehensive introduction to Python language for beginners who wish to learn Python programming and write useful programs. The book aims to get you up to speed fast enough and have you writing real programs in no time at all. This book is also for programmers who have a vague understanding of the language and wish to brush up their knowledge before trying their hands-on Python programming. As you work through the book, you learn the use of libraries and tools such as Numpy and matplotlib and work with data to create stunning visualizations. You also learn about the idea behind 2d games and Web applications and how to create them. This 560 pages long book majorly dissects into two parts. The first part of the book discusses the basics of Python programming and sheds light on concepts such as dictionaries, lists, loops, and classes. You understand the working of a Python program and learn how to write clean and readable code, which creates interactive programs. The part ends with the topic of how to test your code before you add it to a project. The second part of the book follows a practical approach and help you test your knowledge by presenting three different projects, an arcade game, a simple web application and data visualizations using Python’s libraries. Head-First Python (2nd edition)‘Head-First Python’ by Paul Barry is a quick and easy fix for you if you wish to learn the basics of Python programming without having to slog through counterproductive tutorials and books. The book helps you in gaining a quick grasp of the fundamentals of Python programming and working with built-in functions and data structures. The book then moves to help you build your web application, exception handling, data wrangling, and other concepts. The head first Python makes use of a visual format rather than a text-based approach, helping you to see and learn better. The author is Paul Barry, a lecturer at the Institute of Technology, Carlow, Ireland. Before entering the academic world, he worked for over a decade in the IT industry. He is the author of individual well-known programming books, such as Programming the Network with Perl, Head First Programming, and Head First Python. Learn Python the Hard Way (3rd Edition)‘Learn Python the Hard Way’ by Zed A. Shaw (3rd Edition) is a collection of 52 correctly collated exercises. You have to read the code and type it precisely. Once typed, you have to fix the mistakes in the code for a better understanding and watch the programs run. These exercises help you understand the working of the software, structure of a well-written program, and how to avoid and find common mistakes in code using some tricks that professional programmers have up their sleeves. The book begins it all by helping you install a complete Python environment, which helps you in writing optimized code. The book then discusses various topics, such as basic mathematics, variables, strings, files, loops, program design, and data structures, among many others. The book is ideal for beginners who wish to learn Python programming through the crux of the language. The author is Zed A. Shaw, who is the creator of the Hard Way series, which includes books on C, Python, and Ruby programming language. Python Programming: An Introduction to Computer Science (3rd Edition)Python Programming’ by John Zelle is the third edition of the original Python programming book published in 2004, the second edition of which released in 2010. Instead of treating this book as a source of Python programming, it is recommended to take it as an introduction to the art of programming. This book introduces you to computer science, programming, and other concepts, only using Python language as the medium for beginners. The book discusses its contents in a style that is most suitable for beginners, who find the concepts in the book easy to understand and engaging. The third edition of this hugely successful book follows the path paved by the first edition and continues to test students through a time-tested approach while teaching introductory computer science. The most notable change in this edition is the removal of nearly every use of python eval() library and the addition of a section that discusses its negatives. The latest version also uses new graphic examples. The post The Best Python Books for Beginners in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/the-best-python-books-for-beginners-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=the-best-python-books-for-beginners-in-2020 Connection Maps are drawn by connecting points placed on a map by straight or curved lines.While Connection Maps are great for showing connections and relationships geographically, they can also be used to display map routes through a single chain of links. Connection Maps can also be useful in revealing spatial patterns through the distribution of connections or by how concentrated connections are on a map. The post What is Connection Map in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-connection-map-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-connection-map-in-data-visualization A Density Plot visualises the distribution of data over a continuous interval or time period. This chart is a variation of a Histogram that uses kernel smoothing to plot values, allowing for smoother distributions by smoothing out the noise.The peaks of a Density Plot help display where values are concentrated over the interval. An advantage Density Plots have over Histograms is that they’re better at determining the distribution shape because they’re not affected by the number of bins used (each bar used in a typical histogram). A Histogram comprising of only 4 bins wouldn’t produce a distinguishable enough shape of distribution as a 20-bin Histogram would. However, with Density Plots, this isn’t an issue. The post Density Plot in Data Visualization appeared first on Data Science PR. Via https://datasciencepr.com/density-plot-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=density-plot-in-data-visualization Circle Packing is a variation of a Treemap that uses circles instead of rectangles. Containment within each circle represents a level in the hierarchy: each branch of the tree is represented as a circle and its sub-branches are represented as circles inside of it. The area of each circle can also be used to represent an additional arbitrary value, such as quantity or file size. Colour may also be used to assign categories or to represent another variable via different shades. As beautiful as Circle Packing appears, it’s not as space-efficient as a Treemap, as there’s a lot of empty space within the circles. Despite this, Circle Packing actually reveals hierarchal structure better than a Treemap. The post What is Circle Packing in Data Visualization? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-circle-packing-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-circle-packing-in-data-visualization Choropleth Maps display divided geographical areas or regions that are coloured, shaded or patterned in relation to a data variable. This provides a way to visualise values over a geographical area, which can show variation or patterns across the displayed location.The data variable uses colour progression to represent itself in each region of the map. Typically, this can be a blending from one colour to another, a single hue progression, transparent to opaque, light to dark or an entire colour spectrum. One downside to the use of colour is that you can’t accurately read or compare values from the map. Another issue is that larger regions appear more emphasised then smaller ones, so the viewer’s perception of the shaded values are affected. A common error when producing Choropleth Maps is to encode raw data values (such as population) rather than using normalized values (calculating population per square kilometre for example) to produce a density map. The post Data Visualization Explained: Choropleth Map appeared first on Data Science PR. Via https://datasciencepr.com/data-visualization-explained-choropleth-map/?utm_source=rss&utm_medium=rss&utm_campaign=data-visualization-explained-choropleth-map This type of diagram visualises the inter-relationships between entities. The connections between entities are used to display that they share something in common. This makes Chord Diagrams ideal for comparing the similarities within a dataset or between different groups of data. Nodes are arranged along a circle, with the relationships between points connected to each other either through the use of arcs or Bézier curves. Values are assigned to each connection, which is represented proportionally by the size of each arc. Colour can be used to group the data into different categories, which aids in making comparisons and distinguishing groups. Over-cluttering becomes an issue with Chord Diagrams when there are too many connections displayed. The post Data Visualization Explained: Chord Diagram appeared first on Data Science PR. Via https://datasciencepr.com/data-visualization-explained-chord-diagram/?utm_source=rss&utm_medium=rss&utm_campaign=data-visualization-explained-chord-diagram |
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