1-what is Data Analytics
Data analytics is the process of using data analysis techniques to understand and improve the performance of an organization. Data analytics is used to understand customer behavior, optimize supply chains, and identify and fix business problems.
Data analytics is the science of analyzing data to extract useful information. It can be used to find trends, make predictions, or simply understand the data better. Data analytics is a powerful tool that can be used in many different industries, from marketing to healthcare.
There are many different types of data analytics, but they all share one common goal: to help organizations make better decisions by understanding their data. Some common types of data analytics include predictive analytics, descriptive analytics, and prescriptive analytics.
Predictive analytics uses data to make predictions about future events. This type of analytics can be used to identify trends and patterns, and to make forecasts about future behavior.
Descriptive analytics uses data to describe what has happened in the past. This type of analytics can be used to understand customer behavior, identify areas of improvement, or simply understand the data better.
Prescriptive analytics uses data to prescribe actions to take in the future. This type of analytics can
2-what is Data Mining
Data mining is the process of extracting useful information from large data sets. Data mining can be used to find relationships between different pieces of data, identify patterns in data, and predict future events.
How is data mining used?
Data mining can be used to help companies find new customers, to identify fraud, and to predict how customers will behave. Data mining can also be used to help companies find new products or services.
3-what is Data Science
Data science is the study of data. It involves using the scientific method to collect, process, and analyze data. Data science can be used to study anything from social media to climate change.
Data science is a relatively new field, and it is constantly evolving. The term “data science” was first coined in the early 1990s by statistician and computer scientist William S. Cleveland.
Data science is a combination of statistics, computer science, and domain knowledge. It is used to uncover patterns and insights in data.
Data science is used in many different fields, such as business, medicine, and physics. In each field, data science can be used to answer different questions.
For example, in business, data science can be used to understand customer behavior, optimize marketing campaigns, and predict future trends.
In medicine, data science can be used to diagnose diseases, develop new treatments, and improve patient care.
4-what is Machine Learning
Machine Learning is a subset of Artificial Intelligence that is concerned with the design and development of algorithms that can learn from and make predictions on data. Machine Learning algorithms are often used in applications where it is difficult or impossible for humans to write the rules that govern the behavior of the system.
Some common examples of Machine Learning tasks are:
Classification: Given a set of data points, a classification algorithm will learn to identify which class each data point belongs to.
Regression: Given a set of data points, a regression algorithm will learn to identify the underlying trends and relationships between the data points.
Clustering: Given a set of data points, a clustering algorithm will learn to group the data points into clusters.
Anomaly detection: Given a set of data points, an anomaly detection algorithm will learn to identify which data points are outliers.
Recommender systems: Given a set of data points, a recommender system will learn to identify which data points
5-what is the Big Data area
Big Data is a term that refers to large sets of data that can be used to answer complex questions. Big Data sets are often too large and complex for traditional data analysis tools to handle. However, with the right tools and techniques, Big Data can be used to solve problems that were previously thought to be unsolvable.
Some examples of Big Data sets include social media data, web log data, clickstream data, and sensor data. Big Data sets can come from a variety of sources, including traditional enterprise data sources, social media, and the Internet of Things.
Big Data has the potential to transform the way businesses operate. With the right tools and techniques, Big Data can be used to improve decision-making, optimize operations, and create new products and services.
6-difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data:
as we mentioned Data science is a combination of statistics, computer science, and domain knowledge. In data science we have 4 big parts to do;
the first it’s to collect and clean the data; here we have to use a large set of data it’s the Big Data so we can’t clean them manually we use the part of programming to replace missing value or delete incomplete rows… that’s help to create a clean data so we can extract information safely and clearly.
2nd is analyzing the data or data analytics; in this part, we start by reading the data and check the best way to plot this data to extract the information you need to achieve your goals then we visualize that data this also depends on the quantity of data you use you must use preprogramming programs or if you use big data sometimes you need to work with you programming skills in Python or R. finally I have to mention that in this part of analyzing data we can predict some information that depends on one parameter or two, this prediction based on human vision without using the machine.
3rd make predictions: it’s simply machine learning, here we use our cleaned data to build a machine learning model that can understand this data and make predictions depending on the parameters we define. The most of time, we use machine learning to make an accurate prediction, or when we have to take into account several factors to make a decision.
4th make the decision: the last thing the data scientist must do is to use their domain knowledge and the machine predictions to make the best decision.
as you said in the explanation of data science parts, we saw Data Analysis and machine learning these two are big parts of data science that are based on Big Data, Data Mining is a little bit far on the data science job but we can calculate the as a part of data collection.