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Data Analysis: What You Need to Know?

August 15, 2024

تحلیل داده یا Data Analysis: چه چیزی را باید بدانید؟
In today’s data-filled world, data analysis is recognized as a key element in business success. This process involves organizing, cleaning, and examining raw data to extract useful information and insights from it.
Simply put, data analysis means transforming raw data into valuable information that can be used for better, more informed decision-making. Data analysis helps us answer our questions, test our hypotheses, and challenge existing theories.
Today, many organizations regularly collect large volumes of data. However, data on its own in its raw form has no particular meaning. This is where data analysis becomes crucial. This process helps companies leverage their data, improve performance, boost productivity, and make better decisions.

What Is Data Analysis?

Data analysis is the process of examining, cleaning, transforming, and modeling data to extract useful information and aid decision-making. This process encompasses various aspects, approaches, and techniques, and finds application in diverse fields such as business, social sciences, and beyond.

Stages of Data Analysis

There are multiple methods for analyzing data. The most important stages are as follows:
  1. Data Collection: The first step in data analysis is gathering the required data. At this stage, you must determine which data you need, from which sources to collect them, and over what time period. For example, if you intend to analyze your product sales data, you must specify where to gather the data—such as the sales system, customer database, or social media—and define the relevant time frame.
  2. Data Review: After collecting the data, you must examine it to ensure completeness and accuracy. You may encounter incomplete or erroneous data, which then needs to be corrected or completed.
  3. Data Analysis: The final stage is analyzing the data. In this phase, statistical techniques and modeling methods are used to extract meaningful insights. Data analysis requires skill, knowledge, and experience, as well as specialized tools.

Data Analysis Tools

Data analysis is a complex process that requires powerful tools. There are numerous data analysis tools on the market, each with its own features and use cases. Choosing the right tool depends on factors such as data type and volume, analysis complexity, and budget.
Below are some of the popular data analysis tools:
  1. Excel: Excel is a popular office tool that also offers data analysis capabilities. It is suitable for small to medium datasets and includes statistical functions, charts, and dashboards.
  2. Tableau: Tableau is a visual data analysis tool designed for interactive, graphical data presentation. It handles large and complex datasets and offers real-time data analysis, predictive modeling, and text analytics.
  3. Power BI: Developed by Microsoft, Power BI is a business analytics tool for data analysis from various sources. It provides big data analytics, artificial intelligence, and machine learning features.
  4. Other Tools: In addition to the above, tools such as SAS, IBM SPSS, QlikView, MicroStrategy, and Oracle BI are also used for data analysis.

Python in Data Analysis

Using Python for data analysis has become popular due to its diverse libraries. Some of these libraries include:
  1. NumPy: A library for supporting n-dimensional arrays and numerical computations, useful for linear algebra and Fourier transforms.
  2. Pandas: A library for handling missing data, performing mathematical operations, and manipulating data structures.
  3. Matplotlib: A library for plotting data points and creating interactive data visualizations.
  4. SciPy: A library for scientific computing with modules for optimization, linear algebra, integration, special functions, image processing, and signal processing.
  5. Scikit-Learn: A library providing tools for building regression, classification, and clustering models.

Data Scientist vs. Data Analyst

Data Scientist: Uses advanced statistical techniques, machine learning, and programming to analyze and interpret data, extracting new patterns and insights. Required skills include programming, machine learning, statistics, mathematics, and handling big data.
Data Analyst: Collects, processes, and analyzes data to derive actionable insights for business decision-making. Data analysts focus more on descriptive and historical analysis and need skills in statistical analysis, programming, and data visualization.
Skills Needed to Become a Data Analyst:
  1. Mathematical and Statistical Abilities
  2. Familiarity with Programming Languages
  3. Proficiency with Data Analysis Tools
  4. Problem-Solving and Analytical Thinking
  5. Communication Skills and Domain Knowledge
  6. Awareness of Modern Technologies

The Importance of Data Analysis in Business

As technology and markets evolve rapidly, businesses need appropriate tools for management and growth. Data analysis helps businesses leverage large volumes of information to gain competitive advantages, make better decisions, and increase profitability.