Brighten your Career with Data Science Certificate Courses in Bangalore

We can combine information from two separate sources in a single Tableau worksheet or display by using the data blending tool.Unlike joining, which works on the row level and often duplicates data that repeats itself in several rows. A primary data source and a secondary data source are used in data blending. As a result of this, more relevant data from the secondary data source is shown alongside primary data. We can create graphs and charts in a single sheet by combining data from both sources. Data Blending in Tableau The fundamental distinction between combining two data tables and blending two data tables is the phase at which aggregation of data happens. Like in joining, the tables are first merged and then the data is aggregated, resulting in some duplicating values. Whereas, in blending, the tables are kept isolated at the database. Once the data has been compiled, it is sent to Tableau, where it is transformed into a single table free of duplicates. It can commonly happen that...

Text files contain enormous amounts of information. Language data analysis is the most difficult task for a computer to perform since a computer cannot understand the semantics of text. In order to accomplish this, we convert text data into a machine-readable format. Data in text format is converted to numerical values (or vectors) by text processing, so that these vectors may be given to the machine as input and analysed with the algebraic principles. However, there's a chance of data loss if we go through with the transition. The idea is to strike a balance between data conversion and data retention. Preprocessing of text is necessary before processing the text. Text processing techniques will be discussed in this article. Before we begin, it's important to define a few concepts. ● A Document is the name given to each piece of text data. ● Corpus is the name given to the collection of documents as a whole. The methods listed below can be used for text processing: ● Bag of Wor...

It's easy to use Tableau to produce a wide variety of charts. It is done utilising a visual query language that is carried out automatically. Tableau offers 24 different types of charts. Use the measurements and proportions you prefer to build the charts you want to see. All charts are not created equal. After selecting a location, Show Me will prompt us to view a Map. The Lines chart will be suggested while working with dates. ●       If we select Dimension first then Measure, Bar chart will appear by default. ●       A textual representation of the data can be obtained by selecting Measure First, then Dimension. Types of Tableau Chart Using Tableau we can create 24 different types of charts. Tableau gives us the best suited chart as per the dimensions and measures selected by us using Visualization Query Language. ●       If we select Dimension First and Measure second we will get a text. ●       If we select Measure first an...

Missing data can be dealt with in a variety of ways. I believe the most common reaction is to ignore it. Choosing to make no decision, on the other hand, indicates that your statistical programme will make the decision for you. Your application will remove things in a listwise sequence most of the time. Depending on why and how much data is gone, listwise deletion may or may not be a good idea. Another common strategy among those who pay attention is imputation. Imputation is the process of substituting an estimate for missing values and analysing the entire data set as if the imputed values were the true observed values. And how would you choose that estimate? The following are some of the most prevalent methods: Mean imputation Calculate the mean of the observed values for that variable for all non-missing people. It has the advantage of maintaining the same mean and sample size, but it also has a slew of drawbacks. Almost all of the methods described below are superior to mean imput...