DATA ANALYTICS 101
Most companies are collecting lots of data all the time-but this data did not really mean anything because it's in raw form. This is where Data Analytics comes in.
Data Analytics is the process of collecting, cleaning, sorting, and processing raw data to draw out meaningful and actionable insights . The goal of Data Analytics is to get actionable insights resulting in smarter decisions and better business outcomes.
Data Analytics relies on the simultaneous application of statistics, computer programming, and operation research to qualify performance. Analytics often favors data visualization to communicate insight. It also helps you to make sense of the past and to predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you.
Data analytics is kind of a business intelligence used to solve problems every companies have. It makes faster & better business decisions. Data analytics also protects against fraud & analyzing the effectiveness of marketing campaigns.
The process of data analytics typically involves the following steps:
Define the problem: Identify the business problem you're trying to solve or the question you're trying to answer.
Collect and clean the data: Gather relevant data from various sources and clean it to remove any errors, duplicates, or missing values.
Analyze the data: Use statistical and computational methods to explore the data and uncover patterns, correlations, and insights.
Visualize the results: Use graphs, charts, and other visualizations to communicate the insights and findings to stakeholders.
Interpret the results: Draw conclusions and make recommendations based on the analysis and insights
There are a variety of tools and technologies used in data analytics, ranging from basic spreadsheet programs to advanced statistical software. Here are some common tools used in data analytics:
SQL: Structured Query Language is a standardized programming language that is used to manage and query large databases of structured data.
Python: Python is a popular programming language for data analysis and machine learning, and has a variety of libraries and tools for data manipulation and analysis
R: R is a programming language for statistical computing and graphics, and is widely used in academia and research.
Tableau: Tableau is a data visualization software that allows users to create interactive dashboards and reports. It was founded in 2003 in Mountain View, California.
Power BI: Power BI is a Data Visualization and Business Intelligence tool by Microsoft that converts data from different data sources to create various business intelligence reports.
Apache Hadoop: Hadoop is an open-source software framework used to store and process large datasets.
Apache Spark: Spark is an open-source distributed computing system used for big data processing and analytics.
Apache Cassandra: Cassandra is a distributed NoSQL database management system used for handling large amounts of structured and unstructured data.
There are four types of data analytics:
1. Predictive (forecasting)
2. Descriptive (business intelligence and data mining)
3. Prescriptive (optimization and simulation)
4. Diagnostic analytics
1. Predictive: Just as the name suggests, predictive analytics tries to predict what is likely to happen in the future. Predictive analytics holds a variety of statistical techniques from modeling, machine, learning, data mining, and game theory that analyze current and historical facts to make predictions about a future event. Predictive analytics tells you what will happen
2. Descriptive: Descriptive analytics is a simple, surface-level type of analysis that looks at what has happened in the past. The two main techniques used in descriptive analytics are data aggregation and data mining—so, the data analyst first gathers the data and presents it in a summarized format (that’s the aggregation part) and then “mines” the data to discover patterns. It involves the use of data visualization tools like charts, graphs, and tables to present data in a way that's easy to understand. Descriptive analytics tells you what happened.
3. Prescriptive: Building on predictive analytics, prescriptive analytics advises on the actions and decisions that should be taken. Prescriptive Analytics automatically synthesize big data, mathematical science, business rule, and machine learning to make a prediction and then suggests a decision option to take advantage of the prediction. Prescriptive analytics tells you how to make it happen.
4. Diagnostic analytics: In this analysis, we generally use historical data over other data to answer any question or for the solution of any problem. We try to find any dependency and pattern in the historical data of the particular problem. Why did it happen?
In today's data-driven world, businesses of all sizes and industries are turning to data analytics to gain insights and make informed decisions. While there are many tools and technologies available for data analytics, the key to success lies in understanding the problem at hand, selecting the right tools, and having skilled data analysts who can make sense of the data and extract meaningful insights. By investing in data analytics, businesses can gain a competitive advantage, optimize operations, and drive growth.