Data Analytics Homework Help



 

Data Analytics: Data analytics is all about processing row data to extract some favorable information from that. It is used to make good decisions in business. Data analytics very focuses on inference and hypothesis.

It is the process of examining and inspecting data, making use of logical as well as rational reasoning to take a look at each part of the data provided.

DATA TYPE: Data analysis is possible on all of them structured or unstructured data.

REQUIRED KNOWLEDGE: It requires the knowledge of mathematics, computer science, A/I machine learning, etc.

RESPONSIBILITIES: It is liable for explanations, creating models, testing, and proposing hypotheses utilizing analytical methods

OUTPUT: The output of data analysis is a verified hypothesis or proper understanding of the data.


Data Analytics is the process of deriving value out of vast volumes of data. It involves exploring and analyzing large datasets to find hidden patterns, unseen trends, discover correlations and valuable insights. There are a lot of data analytics tools.

Some of the examples are Python, R, Tableau, Power BI, Qlik, Apache Spark, and SAS.

Data Analytics is used in almost every business sector starting from manufacturing to healthcare to retail and finance.

Companies use Data Analytics to ease their business process, solve business problems and improve customer satisfaction.

There are 4 different types of analytics majorly used - Descriptive, Predictive, Prescriptive, and Diagnostic analytics.

 

Types of Data Analytics

There are mainly four main types of data analysis which are;

  1. Descriptive Analytics: Provides a summary view of facts and figures in the simplest format for example Data visualization, Reports, etc.
  2. Diagnostic Analytics: Examines the data to answer the questions for example Data Discovery, Data Mining.
  3. Predictive Analytics: Forecast trends based on the current event for example Linear Regression, Time series)
  4. and lastly Prescriptive Analytics: Helps to make the decision to optimize the output)


 

Difference between data mining and data analytics:

Data Analytics: Data analytics is all about processing row data to extract some favorable information from that. It is used to make good decisions in business. Data analytics very focuses on inference and hypothesis.

Data mining : Data mining is all about extract meaning full data from large data. It’s about pre-processing, Complexity, risk matrix, Visualization.

Data Mining and Data Analysis are two categories under Data Analytics. Both technologies are often used in customer relationship management (CRM). They are used in CRM processes to analyze patterns and queries related to the customer databases for the purpose of gathering corporate information together in a single structure. Data Mining can be defined as the process of extracting data, assessing it from various viewpoints, and then creating a summary of the data in a valuable method that identifies relationships within the data. Whether Data Analysis is concerned with a variety of different tools and strategies that have been created to clear the queries of existing data, find exemptions, and verify hypotheses.

It is the process of evaluating the hidden structure of data according to various contexts for categorization right into useful details, which is collected as well as set up in alike locations.

DATA TYPE: Data mining studies are usually done on structured data.

REQUIRED KNOWLEDGE: It includes the convergence of machine learning, statistics, database, etc.

RESPONSIBILITIES: It is answerable for extracting meaningful structure in the data.

 

OUTPUT: The output of data mining is a data design.


DATA MININGDATA ANALYTICS
FUNCTION: Discovering hidden patterns in data sets.FUNCTION: Everything involved in examining data sets to draw conclusions.
GOAL: Make data useable identify patterns
GOAL: Make data-driven decisions 
- Test hypothesis
METHOD: Mathematical and scientific methods
- Often does not need visualizations
METHOD: Analytical and business intelligence models
- Always involves visualizations
DATASET: Large
- Structured
DATASET: Small, medium, large
- Unstructured, semi-structured, structured
REQUIRED KNOWLEDGE: Machine   learning, statistics, databaseREQUIRED KNOWLEDGE: Computer science, statistics, mathematics, subject knowledge, AI, machine learning
OUTPUT: Data patterns
- Trends
OUTPUT: Actionable insights
- Verified or rejected hypothesis
Comes under data analytics, it's used to draw a hidden pattern from the data.Getting insights/ meaningful information from the raw data.
Data Mining experts are mostly computer scientists or software engineers. Data Analytics ones are data scientists (generally with a computational social science background).




 

Data Analytics Tools:

  1. R Programming
  2. Tableau
  3. SAS
  4. Apache Spark
  5. QlikView



 

The distinction between Data Science and Analytics:


Data Science: Dealing with unstructured as well as structured data, Data Science is an area that includes exactly what is associated with data cleansing, preparation, as well as analysis.

Data Science may be the combination of statistics, math, encoding, issue-resolving, capturing data within clever methods, the ability to look at points in a different way, and also the activity associated with cleansing, planning, as well as aligning the data.


Data Analytics: Data analytics is all about processing row data to extract some favorable information from that. It is used to make good decisions in business. Data analytics very focuses on inference and hypothesis.

It is the process of examining and inspecting data, making use of logical as well as rational reasoning to take a look at each part of the data provided.

Data Analytics is the process of deriving value out of vast volumes of data. It involves exploring and analyzing large datasets to find hidden patterns, unseen trends, discover correlations and valuable insights.

Some of the examples are Python, R, Tableau, Power BI, Qlik, Apache Spark, and SAS.

Data Analytics is used in almost every business sector starting from manufacturing to healthcare to retail and finance.


 

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