Data Analysis Assignment Help

Data Analysis Assignment Help | Data Analysis Homework Help

This is the systematic and scientific process of data evaluation that involves analytical examination together with logical explanations to scrutinize every element of the data in hand. The data in hand is previously collected from specific sources for the purpose of examining and explaining a situation with logical support.

Data analysis is crucial for every business more so in the data driven business organisations. It includes seven key steps that are universally accepted across organisations. They are outlining objectives, business levers identification. Data collection, data cleaning, data modelling, development of a data science team and data optimization.

Data analysis is done through a number of techniques/ methods. The decision of technique to use depends on the type of data at hand, the variables been analysed and the objectives of the process. These techniques include frequency distribution, descriptive statistics, statistical testing, cross tabulation, correlations, linear regression and text analytics.

What is Data Analysis Help?

Data analysis assignment is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. In other words, data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Our statisticians are highly efficient in teaching the use and application of data analysis assignment help. In addition, Our experts have experience with SPSS, AMOS, SAS, R, Matlab, STATA, and other statistical software.

An overview of data analysis

Data Analysis is the process of methodically applying statistical and logical procedures or techniques to describe and demonstrate data, condense and recap data, and above all evaluate data systematically. Thus, Data analysis provides a methodical way of drawing inductive interpretations from data and distinguishing the signal from the noise or statistical fluctuations present in the data. As far as qualitative research procedure is concerned, data analysis includes statistical procedures. In many instances, data analysis becomes an ongoing where data is collected and analyzed continuously in real time situations.

Indeed, data analysis experts generally analyze for patterns in observations through the entire data collection phase. The procedure of the data analysis is determined by the exact qualitative approach taken, like field of study, ethnographic content analysis, oral history, biography, unobtrusive research, etc. and the form of the data, like field notes, audiotape, documents, videotape.

In data analysis, data integrity is another important factor. Without data integrity, accuracy and appropriate data analysis is not possible. Improper analyses misrepresent scientific findings, and mislead the readers leading to a negative influence on the public perception of research. Integrity issues are relevant to the analysis of both statistical and non-statistical data as well.

Skills for data analysis

Data analysis is another challenging matter for the professionals. It is attributed to those data sets that are too big to analyze through traditional data processing software. Such difficulties include the following

• Capture of data,
• Metadata,
• Storage of data,
• Search, sharing, and transfer of data,
• Data visualization
• Analysis

To become a successful data analyst one needs to develop skills in all facets of data analysis. He needs to learn how to manage different data sets and how to work with them. Besides he also needs to learn the following –

• computing new variables,
• merge data sets,
• re-code data
• learn about flat file databases and
• learn about hierarchical databases and
• relational databases
• Learn all kinds of statistical techniques such as OLS, HLM, SEM,
• learn missing data estimation techniques,
• Facet analysis,
• social Network Analysis,
• longitudinal analysis techniques
• learn qualitative data analysis techniques,
• learn Atlas.ti or NVivo,

Top 10 Data Analysis Tools

Data analysis With Power BI

Microsoft Power BI is a top business intelligence platform with support for dozens of data sources. It allows users to create and share reports, visualizations, and dashboards. Users can combine a group of dashboards and reports into a Power BI app for simple distribution. Power BI also allows users to build automated machine learning models and integrates with Azure Machine Learning.

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Data Analysis For Tableau

Tableau is a data visualization and analytics platform that allows users to create reports and share them across desktop and mobile platforms, within a browser, or embedded in an application. It can run on the cloud or on-premises. 

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Data Analysis With Jupyter Notebook

Jupyter Notebook is a free, open source web application that can be run in a browser or on desktop platforms after installation using the Anaconda platform or Python’s package manager, pip. It allows developers to create reports with data and visualizations from live code. The system supports more than 40 programming languages.

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Data Analysis With RapidMiner

RapidMiner provides all the technology users need to integrate, clean, and transform data before they run predictive analytics and statistical models. Users can perform nearly all of this through a simple graphical interface. RapidMiner can also be extended using R and Python scripts, and numerous third-party plugins are available through the company's marketplace.

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Data Analysis With R

R is an open source programming language and computing environment with a focus on statistics and graphical data visualization. R features numerous graphical tools and over 15,000 open source packages available, including many for loading, manipulating, modeling, and visualizing data.

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Data Analysis With Python

Python is an open source, high-level programming language that's often used by technical analysts and data scientists. It now boasts more worldwide developers than Java and has more than 200,000 available packages.

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Data Analysis With Excel

Microsoft Excel is the most common tool used for manipulating spreadsheets and building analyses. With decades of development behind it, Excel can support almost any standard analytics workflow and is extendable through its native programming language, Visual Basic.

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Data Analysis With Oracle Analytics Cloud

Oracle Analytics Cloud is a suite of cloud business intelligence and analytics applications. It's focused on helping large enterprises transition their legacy systems to a modern cloud platform.

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Data Analysis With Google Data Studio

Google Data Studio is a free dashboarding and data visualization tool that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Thanks to its integration with other Google services, Data Studio is great for those who need to analyze their Google data. 

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