Simran Kaur Arora. Table of Contents. Top Data Analytics Tools 1. Python 2. SAS 4. Excel 5. Power BI 6. Tableau 7. Apache Spark Summary. Simran Kaur Arora Simran works at Hackr as a technical writer. Related Posts What is Splunk? Best Data Analysis Software in Leave a comment. Submit Cancel. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies.
The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.
Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.
An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice. Variable is a characteristic that varies from one individual member of population to another individual.
Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ]. Quantitative or numerical data are subdivided into discrete and continuous measurements.
Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… integer , whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit.
Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature. A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].
Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist as in gender male and female , it is called as a dichotomous or binary data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables. Ordinal variables have a clear ordering between the variables.
However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale. Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced.
A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning.
However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm. Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode.
Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population.
The examples if descriptive and inferential statistics are illustrated in Table 1. The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.
The measures of central tendency are mean, median and mode. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is. Median[ 6 ] is defined as the middle of a distribution in a ranked data with half of the variables in the sample above and half below the median value while mode is the most frequently occurring variable in a distribution.
Range defines the spread, or variability, of a sample. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into equal parts.
The median is the 50 th percentile. Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value.
The variance of a population is defined by the following formula:. The variance of a sample is defined by slightly different formula:. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation SD. TL;DR: Magnitude is a business user-friendly tool, but it can get complicated once you start integrating data from multiple sources.
Informatica MDM Reference is a cloud-based tool that provides an end-to-end approach with embedded data quality, data integration, process management, and more. As it's fully-cloud-based, you can improve performance and scalability without much effort. Reltio Cloud is a graph-based master data management tool that is equipped with reference data management tools. Reltio is built on graph databases to provide maximum flexibility in scaling data stores and defining clear relationships between the data in your repository.
Reltio can be used to manage mission-critical data and win in the experience economy. Reltio Connected Customer , built on cloud-native, big data architecture featuring graph technology and machine learning, is at the heart of customer experiences.
This approach enables hyper-personalization, accelerated real-time operations, and simplifies compliance, and all at scale. TL;DR: it's an excellent tool for Fortune companies focused on delivering enhanced customer experiences, but you'll have to contend with a steep learning curve. From machine learning-enabled notebooks to drag-and-drop dashboards, analytics and visualization tools are designed to help you derive insights from your data.
While all the options on this list offer some degree of data visualization, tools vary in the customizability of your data viz. These tools also offer a range of query options from SQL-first to drag-and-drop. Tableau is a BI platform available both on the cloud and as downloadable software, with the following key features:.
TL;DR: Tableau is great for businesses that are seriously into data viz but that also want the ease of drag-and-drop analysis. TL;DR: Cumul. Looker is another cloud-based analytics and visualization platform, with the following key features:.
TL;DR: Looker is ideal for companies that prefer downstream control of their data model and business logic. Metabase offers a user-friendly, open source interface for connecting and analyzing your data.
As a data visualization tool, it offers:. TL;DR: Metabase's low cost can help companies get started with analytics and visualization, but may fall short as a long-term solution. Metabase price: Metabase is free and open source, so its free tier offers a range of features that will be suitable for most users.
Main features:. TL;DR: Power BI is popular for a reason: it's easy to use thanks to its Excel-like interface that lowers the barrier to entry for non-analysts. Mode Analytics offers a web-based data analytics suite aimed at data scientists and analysts, with a focus on collaboration and sharing. If you need an all-in-one tool, ClicData could be a good fit. Its primary features include:. TL;DR: ClicData could work well for companies that prefer to work with a single vendor for all their data needs.
When evaluating cost, pay close attention to what pricing tier your data sources will land you in. There's no replacement for managing business processes around structured data in large organizations, but cloud-based platforms can help with data management strategy.
For example, they can support the treatment and preparation of raw data, data ingestion, loading, transformation, optimization, and visualization, all automatically in a single system.
For example, Panoply's cloud data platform can connect directly to data sources, manage data loading, and automatically transform your data into clean tables that are ready for analysis. Tools that provide an integrated big data stack take us one step closer to a truly holistic data management concept. At Panoply, we believe in simple and robust data management. Although Panoply was developed to work well for data engineers that simply don't have the bandwidth to manage everything on their own, analysts can also be successful.
Topics Topics. Speak with a Panoply Data Architect. Resources Resources. Integrations Integrations. Why Panoply. Demo Demo. Visit Panoply. Data Stack. By Andrew Zola February 15, What is Data Management? Tools essential to effective data management fall into these general categories: Cloud data management ETL and data integration Data transformation Master data management Reference data management Data analytics and visualization Below we cover several great tools from each of these categories, both to help you understand each category and to move closer to selecting the best data management tool for your needs.
Best cloud data management tools Cloud data management tools help organizations integrate and manage data across multi-cloud environments. Panoply Panoply is an ETL tool and a cloud-native data warehouse that makes data integration and management effortless.
Key features include: An extensive selection of native data connectors that enable easy, one-click data ingestion An intuitive dashboard that takes the guesswork out of data management and budgeting Automated scaling of multi-node databases for low-maintenance data warehousing In-browser SQL editor for data analysis and querying Connections to common data visualization and analysis suites such as Tableau, Looker, Power BI, and more TL;DR: it's an excellent turn-key business intelligence solution for SMBs who want to derive the most value from their data at a fraction of the cost.
Key services include: Amazon Athena for SQL-based data analytics Amazon S3 for temporary and intermediate storage Amazon Glacier for long-term backup and storage AWS Glue for building data catalogs to categorize, search, and query your data Amazon QuickSight for dashboard construction and data visualization Amazon Redshift for data warehousing Separate billing for each spun up service so that costs depend on the extent of utilization TL;DR: it's a useful tool for large enterprises that generate oceans of data and have the technical prowess to manage it.
AWS price: varies and depends on your implementation. Microsoft Azure Microsoft Azure offers a variety of options when it comes to setting up a cloud-based data management system. Azure price: varies and depends on your implementation. Google Cloud price: varies and depends on your implementation. Informatica PowerCenter offers the following key features: Seamless connectivity and integration with all types of data sources using out-of-the-box connectors Automated data validation via script-free automated audit Advanced data transformations including non-relational data, XML, JSON, PDF, Microsoft Office, and IoT data Metadata-driven management that provides graphical views of data flows, impact and lineage TL;DR: In a world of cloud platforms, Informatica PowerCenter is an on-prem holdout that could be exactly what companies bound by complex regulatory concerns need.
Informatica PowerCenter price: available upon request. It is best for matrix manipulation: data function plotting, algorithms implementations, user interface creations, and many more. There is a well-known fact that a coin has two faces. In a similar way, statistical data can be used in positive as well as negative ways. Or we can say that statistics can also be misguided and misused. But when you have enough knowledge of statistics and its tools, then you can not be misguided.
Therefore, you must know the methods of using these statistics tools. Apart from this, you must cross-check the statistics data if you want to buy any service or product. This will not only help you to know the reliability of the service or product but also state its performance. As per the statistical data, you can easily make the decision that can affect your organization and its profit. This will also help the nation to improve its economy.
We have seen that there are plenty of statistics tools for data analysis, data science, and data visualization. Even more statistical tools are available in the world that can fulfill your requirements for data analysis as well as data science. Even some of the statistics tools online are an alternative to these statistics tools that I have mentioned above. But all these tools are the best in their class. Moreover, you can use any one of these tools without having a second opinion.
If you are looking for how can I get help for statistics homework , then here we are offering you the best service at nominal charges. The five main methods for statistical analysis are standard deviation, hypothesis testing, mean, regression, and sample size determination. These methods are not only carried out by the statisticians but also by the researchers.
The best 5 data collecting methods are: Questionnaires and surveys. Focus groups. Documents and records. There are two branches of statistics: inferential statistics and descriptive statistics. Table of Contents. What are the 5 basic methods of statistical analysis?
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