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Data Analysis is the process of processing raw data into extra meaningful insights to aid business decision making. Data Analytics has gained a lot of prominence in recent years due to the capture of consumer/customer data from every interaction of mobile/PCs. Firms want to leverage the data through in-depth analysis and solve key business questions like customer segmentation, customer retention/churn, product recommendations, etc. A range of tools can be used such as Excel, SPSS, SAS, R Programming, Python, and STATA to process and analyze the data. We at The Statistics Assignment Help offer the best-in-class Data Analytics assignment help.
Data analysis finds its relevance in all the key fields i.e. engineering, medicine, programming, management, science, or R&D. Given the importance of data analysis, in today's world it has proved to be one of the prominent career tracks. It has been added as one of the key topics to study in the academic curriculum of almost all universities. Many students struggle to solve the data analytics assignments on their own. It gets challenging for them to handle huge data files. If you are one of them and need help with data analytics homework, then seek help from our experts.
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Data analytics is a method to extract raw data using statistical tools and logic. Along with data extraction, it also involves data cleaning, rearranging, and analysis.
The data analysis process can be broken down into the following parts
Data analytics can be used in different applications including but not limited to –
The list can go on and on for applications of data analytics in our daily life. For a student to achieve better grades in data analytics subject, they have to come up with an accurate solution. If the data is huge, the students find it cumbersome to process, clean, and analyze the data resulting in poor grades. If you are facing such challenges then ask our experts for data analysis assignment help.
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Listed below are 3 prominent data analysis techniques:
Descriptive Analytics: This is the technique that analyzes past data to summarize and find key patterns for it. Descriptive summaries such as mean, median, mode etc allow businesses to understand what have been the critical drivers for businesses in the past. It provides key insights on historical patterns of performance or behavior
Predictive Analytics: It refers to making predictions on future outcomes or performance based on historical data by applying statistics and modeling techniques. It allows businesses to adjust their strategies based on the predicted outcomes and accordingly allocate the resources.
Prescriptive Analytics: It goes beyond predictive analytics and refers to taking the right set of actions based on predicted outcomes to optimize the business. Prescriptive analytics techniques such as recommender systems leverages algorithms, business rules, machine learning, and other modeling procedure to take the right action. Such techniques help businesses to progress, with complicated management and administration.
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Learning data analytics is important for every student because it will help them to perform the below activities which are crucial for personal and professional success.
The following are the tools used to carry out data analytics. These include
SAS: This is the most popular and widely used tool to perform data analytics in the market. This is the largest BI vendor available in the market. In addition to managing the data, this is also used to carry out statistical analysis.
Statistica: This is the software package that comprises various data analytics techniques. This is a package that is used for data management, data mining, data visualization, etc.
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|Descriptive Analytics||Predictive Analytics|
|Prescriptive Analytics||DataAnalysisTools-SAS, MATLAB,Minitab, SPSS|
|Advanced Business Analytics||Segmentation and Clustering|
|Big Data Analytics||Experimental design|
|Analysis of variance||Regression Analysis|
|Data Collection||Data Processing|
|Data Cleaning||Exploring data analysis|
|Algorithm and modelling||Data Mining|
|Discretization||Cluster detection techniques|
|Causal Inference||Complex Correlation|
|Useful Patterns||Graph Summarization|
|Significant Results||Data Analysis|