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What are Data Visualization Heatmaps?
Heatmaps were originally introduced by Cormac Kinney to graphically represent real-time financial information. They are used in data visualization to measure the intensity of given business values, encoded using different colors.
The following image is an example of the heatmap, shared by NASA on July 13th, 2022. It shows the surface air temperatures for many countries in the world. The hottest the area is, the darkest the color. Investly, the coldest, the color is more driven towards blue. Such information can be easily understood at a glance.
Data Scientists can use them to turn companies' information into easy-to-understand visualizations to help them make actionable smart decisions.
As a Data Scientist, being able to provide businesses with a clear and concise visualization can help them grab key information about their data for quick and efficient decision-making.
Different types of visualizations exist to communicate different messages. For instance, histograms are used to measure the distribution of numerical data, scatterplots for analyzing relationships between variables, and pivot tables for summarizing key statistics in a single table, just to name a few using tools like Power BI, Tableau, etc.
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What is the Power BI Calculate Function?
According to the Microsoft Power BI documentation, the CALCULATE function forms part of the filter function category and is defined as "evaluating an expression in a modified filter context." An expression is essentially a measure and includes functions such as SUM, AVERAGE, and COUNT. This expression is evaluated in the context of one or more filters.
As you may know, filters can also be applied to a Power BI report simply by adding slicers without creating a measure using the CALCULATE function at all. However, there are many use cases where the CALCULATE function is more appropriate. It is especially useful to use it as a component of another function. We will see how this works in the example below for calculating the percentage of a total.
The CALCULATE function is arguably one of Power BI's most important (and most popular) DAX functions. It is easy to use and extremely versatile, allowing you to expand your data analyses and develop even more valuable Power BI reports.
This tutorial focuses on what the CALCULATE function is and how to use it and assumes you’re already familiar with the basics of Power BI and DAX.
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What are Tableau Calculated Fields?
Calculated Fields are one of the most powerful features of Tableau. They provide a way to create new data from existing data.
For example, let���s suppose we want to predict the price of an apartment in New York based on its characteristics, like the number of bedrooms, number of bathrooms, and so on.
It may be useful to how far away the apartment is from a subway station, but this information isn’t available directly from the dataset.
Then, we need to derive it from existing fields, like the address. You can easily learn about this Tableau feature from our course.
The creation of new variables or fields is very important when extracting insights from data..
Oftentimes, a dataset doesn’t contain all the necessary information to extract insights,
but it’s possible to derive new features from existing ones, such as averages, ratios, bins, and categorical variables.
Most of the time, there is a need for new fields to obtain clearer analyses and visualizations.
In this tutorial, we’ll walk you through creating fields with Tableau.
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Learn about different packages in R used for data science. Including how to load them and different resources you can use to advance your skills with them.
R is the most popular language for Data Science. There are many packages and libraries provided for doing different tasks. For example, there is dplyr and data.table for data manipulation, whereas libraries like ggplot2 for data visualization and data cleaning library like tidyr. Also, there is a library like 'Shiny' to create a Web application and knitr for the Report generation where finally mlr3, xgboost, and caret are used in Machine Learning.
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Comparing MSBI and Power BI: Exploring Business Intelligence Services
In the realm of Business Intelligence, both MSBI and Power BI have gained significant popularity.
In this article, we aim to provide a comparison between these two services: MSBI vs Power BI, shedding light on their unique features and functionalities.
It's important to note that MSBI is a comprehensive product with robust Extract, Transform, and Load (ETL) capabilities.
It excels in handling data extraction, transformation, and loading tasks, while also enabling the organization and visualization of multidimensional data.
On the other hand, Power BI stands as a powerful
Data Analytics
tool, offering advanced data modeling capabilities.
It facilitates tasks such as data preparation, data discovery, and the creation of interactive dashboards.
Given the distinct focus and functionalities of MSBI and Power BI, it would be unfair to directly compare these two products as a whole.
However, we can make a fair comparison by examining the reporting tools provided by each service.
In this context, when we refer to MSBI, we specifically focus on one of its SQL Server Data Tools – SSRS (SQL Server Reporting Service).
Similarly, when we mention Power BI, we are specifically discussing the capabilities of Power BI Desktop.
This approach allows us to compare and evaluate the reporting services offered by both
MSBI and Power BI
in an equitable manner.
Following are the criteria on which the comparison shall take place for MSBI vs Power BI:
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What is Tableau and it's capabilities?Tableau stands out as a prominent and highly sought-after business intelligence (BI) tool that empowers users to visualize and analyze their data in a visually engaging and easily understandable format. With Tableau, you can seamlessly work with live data sets, freeing up valuable time that would otherwise be spent on laborious data wrangling tasks. While this provided a brief overview of Tableau, let's delve deeper and explore what exactly Tableau entails
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Its applications vary from fraud detection and disease detection to recommendation engines and thus growing businesses. These wide ranges of applications and increased demand have led to the development of Data Science tools.
In the below section we’ll be discussing in-depth about the best Data Science tools in the market. But before we go there it is important that you understand that this blog is focused on the different Data Science tools and not the programming languages that can be used to implement Data Science. So, don’t expect there to be a war between which is better for Data Science, Python or R.
Enroll for the Data Science course, a Post Graduate program by Edureka to elevate your career.
With that being said let’s dive straight into the Data Science tools.
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The word ‘Data’ has been in existence for ages now. In the era of 2.5 Quintillion bytes of data being generated daily, data plays a crucial role in decision-making for business operations. But how do you think we can deal with so much data?
Well, there are several roles in the industry today that deal with data to gather insights, and one such vital role is of a Data Analyst. A Data Analyst requires many tools to gather insights from data.
This article on the Top 10 Data Analytics Tools will discuss the top tools that every budding Data Analyst to a skilled professional must learn in 2023.
In this article, we will explore a range of popular data analysis tools, including R and Python, Microsoft Excel, Tableau, RapidMiner, KNIME, Power BI, Apache Spark, QlikView, Talend, and Splunk.
These tools offer diverse capabilities for data analysts and are widely used in the industry for various analytical tasks.
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With the exponential outburst of AI, companies are eagerly looking to hire skilled Data Scientists to grow their business.
Apart from getting a Data Science Certification, it is always good to have a couple of Data Science Projects on your resume.
Having theoretical knowledge is never enough.
So, in this blog, you’ll learn how to practically use Data Science methodologies to solve real-world problems.
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One of the great perks of Python is that you can build solutions for real-life problems.
This applies in almost every industry. From building models to predict diseases to building web apps that can forecast the future sales of your online store, knowing how to code enables you to think outside of the box and broadens your professional horizons as a data scientist.
Whether you’ve just learned the Python basics or already have significant knowledge of the programming language, knowing your way around predictive programming and learning how to build a model is essential for machine learning. In this practical tutorial, we’ll learn together how to build a binary logistic regression in 5 quick steps.
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starting a career in data science : the ultimate guide
Are you starting a career in data science?
Just thinking about the first step can leave you dazed and confused, especially if you lack previous experience in the field.
With so many different data science careers to explore, you might find yourself wondering which is the right one for you and if you’ve got what it takes to fit the profile.
Is Data Science for Me?
Well, we’ve all asked ourselves that question when we were at square one of our data science learning path. And we haven’t forgotten that every expert was once a beginner
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What Is Data?
Look around you, there is data everywhere. Each click on your phone generates more data than you know. This generated data provides insights for analysis and helps us make better business decisions. This is why data is so important.
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“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms.
In fact, Mathematics is behind everything around us, from shapes, patterns and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives.
Although having a good understanding of programming languages, Machine Learning algorithms and following a data-driven approach is necessary to become a Data Scientist, Data Science isn’t all about these fields.
In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models.
To get in-depth knowledge on Data Science and the various Machine Learning Algorithms, you can enroll for live Data Science with Python Training Course by Edureka with 24/7 support and lifetime access.
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Have you ever wondered how Google ranks web pages?
If you’ve done your research then you must know that it uses the PageRank Algorithm which is based on the idea of Markov chains.
Markov chains have proven to be an integral part of Google's innovative algorithms and search technology.
A Markov chain is a mathematical concept that models a sequence of events where the probability of transitioning from one state to another depends only on the current state, disregarding the history of events.
Google utilizes Markov chains in its PageRank algorithm, which determines the relevance and importance of web pages for search results.
In this context, web pages represent states, and the probability of transitioning from one page to another is calculated based on the number and quality of incoming links.
By employing Markov chains, Google can efficiently crawl and rank web pages, providing users with highly relevant and accurate search results.
This powerful integration of Markov chains in Google's search technology has revolutionized the way people access information and contributed to the search engine's unparalleled success in organizing and retrieving vast amounts of online data.
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Although we have dedicated a series of blog posts to time series models, we are yet to discuss one very important topic – seasonality.
Each of the models we examined so far – be it AR, MA, ARMA, ARIMA or ARIMAX has a seasonal equivalent.
As you can probably guess, the names for these counterparts will be SARMA, SARIMA, and SARIMAX respectively, with the “S” representing the seasonal aspect.
Therefore, the full name of the model would be Seasonal Autoregressive Integrated Moving Average Exogenous model.
We can all agree that it’s a mouthful, so we’ll stick with the abbreviation.
Additionally, the SARMA and SARIMA can be considered simpler cases of the SARIMAX, where we don’t use integration or exogenous variables, so we’ll mainly focus our attention to the SARIMAX in this tutorial.
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A solid understanding of statistics is crucial for anyone who wants to work with data in data science, economics, psychology, social sciences, business, sports, etc.
But the terminology and calculations involved in statistics can be overwhelming for beginners. That’s why we created our Basic Statistics Crash Course that provides a simple example to help you understand fundamental statistical concepts.
Our Basic Statistics Crash Course is perfect for preparing for university exams or enhancing your analytical skills. Soon, you’ll be well-equipped to tackle statistics questions during your data science interview or conduct analyses in your work or studies.
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