Data Analytics Applications
Data Analytics is the method of examining the raw data that will help in drawing relevant conclusions about the data. It also involves applying various algorithms and tools to derive useful and meaningful insights. It is used in various industries which allows the companies and organizations to make better and useful decisions.Some trending industries which use data analytics are Health Care,Travel,Social Networks etc.
To become a Pro Data analyst, there are a couple of areas you should focus on. This would guarantee a successful career if you are looking for the Best.To become a Pro Data analyst, there are a couple of areas you should focus on. This would guarantee a successful career if you are looking for the Best.
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How To Get Start Data Analyst Career ?
✓ Find out what is Data analytics or Data Science
✓ Skills required to become a data scientist
✓ Attend meetups & free webinar on career in data analytics
✓ Interect with experienced people to understand the life of a data scientist
✓ Join to our Data Analyst training course in Hyderabad,India
Who are Eligible to the Data Analyst Course ?
✓ Find out what is Data analytics or Data Science
✓ Big Data Engineers
✓ Software Professionals
✓ All Graduates
✓ MBA & MCA & Degree Holders
✓ Anyone who interests to make a Career
Learn More about Data Analytics
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Data Analysis are of four types:
Prescriptive, predictive, descriptive, diagnostic
Descriptive analysis: This is also called Data mining and helps in understanding patterns to get the answer to “what Happened?” .They aim at discovering the success or failure of a business strategy. Descriptive analysis goes through page views in Google analytics to determine if a strategy followed by the business is a success or failure. This analysis does not tell the reasons behind the success or failure .For this reason, a large number of high data driven companies use this in combination with other analytics.
Diagnostic analysis: It answers the question “why is it Happening”? They can help to assess why a campaign failed and the reasons for failure can be found. They give us a detailed information regarding any problem. Descriptive and Diagnostic analyses together will help us in identifying the root cause of the problem.
Predictive analysis: It predicts “what will happen” in the future. They use variable data to make the predictions. Combined with statistical modelling, predictive modelling will give predictions as to give realistic goals for businesses. They combine data mining, machine learning and predictive modelling to get the predictions. Predictive analysis is used in marketing, financial services, retail, healthcare etc.
Prescriptive analysis: It tells “what action to take”. Prescriptive analysis uses sophisticated tools and techniques to get solution to a problem. It tells us the best course of action to take about how to eliminate a future problem or how to take advantage of an opportunity in the future.
Data analysis is divided into:
- EDA and CDA
- Exploratory Data Analysis (EDA) looks at new features and patterns and relationships in data while Confirmatory Data Analysis uses statistical techniques to determine if the hypothesis drawn are true or false.
- Data mining, predictive analytics, machine learning, artificial intelligence are the more advanced types of Data analytics.
- Quantitative analysis, Qualitative analysis
Quantitative analysis is statistical and is in the form of tabulations while qualitative analysis is non-statistical and methodical.
Data Mining Techniques:
Association, classification, clustering, prediction, sequential patterns and decision tree are some data mining techniques.
Association: this is used to identify patterns between variables. It is also known as relation technique. Correlation coefficients are used for quantitative variables while other measures are used for non-quantitative variables.
Classification: The variables or data is classified into various levels or categories to arrive at conclusions. Mathematical techniques like statistics, neural network, decision trees, linear programming are used for classification.
Clustering: Groups of data are put together based on their similarities. Nearest neighbor, furthest neighbor, median, method of K-Means, group average are the methods used for clustering.
Prediction: it helps in predicting the future by establishing relation between the variables.
Sequential patterns: it helps to identify regular or sequential patterns over a period of time.
Decision tree: it is the most simple and commonly used technique in data mining.
It is a tree like structure with the root which can be a question and the branches are the outcomes or conditions on which we can make a final decision.