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What is Data Analysis?
Data analysis is a technique for refining and modeling data in order to uncover useful information for commercial decision-making. The analysis' goal is to extract useful information from data and make a decision based on that knowledge. We study our history or future and make decisions based on it whenever we make a decision in our daily lives. This is nothing more than data analysis, but it is carried out for commercial objectives.
Text analysis : Text analysis is a machine learning technique used to automatically extract the useful information from textual data. Lot of companies use text analysis tools to analyze the online data and documents and transform them into actionable insights.
Statistical analysis : Statistical Analysis is the science of collecting, exploring and presenting large amounts of data and identifying the pattern and trends in the data. It is used every day in research, industry and government to become more scientific about decisions that need to be made.
Descriptive Analysis : Descriptive analysis is the complete information or sample of summarized numerical data.
Diagnostic analysis : This Analysis is the helpful to spot behavior patterns of information
Predictive Analysis : Predictive analytics helps connect the data to effective action by drawing a reliable conclusion which a data analyst can predict the future based on the current and previous data. This term is mainly used for analytics and statistical techniques.
Prescriptive Analysis : Prescriptive analytics is a statistical method that focuses on finding the ideal way forward or action necessary for a particular scenario, based on data.
Data analysis Process
Now we will discuss the data analysis process and how to perform the data analysis. It involves step data gathering, data preprocessing, data exploration, finding patterns and other insights. Data analysis process consist of :
Data gathering : Before performing the data analysis you want to know why you are doing this data analysis. what type of data analysis you want to use and what data you want to analyze.
Data Collection : Collect the data from various sources. Sources may include survey, observation, interviews, questionnaires, discussion etc. make sure the collected data is for analysis.
Data cleaning : During the data collection, sometimes data may be incompletely filled, sometimes wrongly entered and sometimes get the wrong information. Data cleaning process removes the missing values or fills it by mean value, removing duplicate records from the dataset. This is the most important process before performing the data analysis, because wrong information or incomplete information may affect the data analysis result.
Data analysis : There are a lot of tools available for data analysis which help you to understand and interpret the data and get the conclusion. The most commonly used tools for data analysis are excel, python, R programming, Microsoft power BI and Tableau.
Data Interpretation : After performing the data analysis you will get the result. You need to analyze the result and come up with the best action based on your findings.
Data Visualization : Data visualization is a technique to represent the data in graphically. We can use charts, graphs, maps, bullet points, or other methods. Visualization helps us derive important information by helping you compare datasets and observe relationships.
Data analysis using Python
In this we will analyze the data using python. We will prepare the data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data and many more.
Cleans prepare data for analysis
Manipulate data dataframe
Visualize the data using python
Build the machine learning model using scikit-learn
Build data pipelines
Data analysis using R
In this we will analyze the data using R language. We will prepare the data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data and many more.
Understanding the dataset
Clean prepare data for analysis using R
Visualize the data using R language
Build the model
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