Data analyst step 2

Data analysis is one of the key component of data science. Data analysis is described as the process of cleaning, converting, and modelling data to obtain actionable business intelligence.

  • Accurate Data − We need data analysis that helps businesses acquire relevant and accurate information that they can use to plan business strategies and make informed decisions related to future plans and realign the company’s vision and goal.

  • Better decision-making − Data analysis helps in making informed decisions by identifying patterns and trends in the data and providing valuable insights. This enables businesses and organizations to make data-driven decisions, which can lead to better outcomes and increased success.

  • Improved Efficiency − Analyzing data can help identify inefficiencies and areas for improvement in business operations, leading to better resource allocation and increased efficiency.

  • Competitive Advantage − By analyzing data, businesses can gain a competitive advantage by identifying new opportunities, developing new products or services, and improving customer satisfaction.

  • Risk Management − Analyzing data can help identify potential risks and threats to a business, enabling proactive measures to be taken to mitigate those risks.

  • Customer insights − Data analysis can provide valuable insights into customer behavior and preferences, enabling businesses to tailor their products and services to better meet customer needs.

Typically, the data analysis process involves many iterative rounds. Let's examine each in more detail.

  • Identify − Determine the business issue you want to address. What issue is the firm attempting to address? What must be measured, and how will it be measured?

  • Collect − Get the raw data sets necessary to solve the indicated query. Internal sources, such as client relationship management (CRM) software, or secondary sources, such as government records or social media application programming interfaces, may be used to gather data (APIs).

  • Clean − Prepare the data for analysis by cleansing it. This often entails removing duplicate and anomalous data, resolving inconsistencies, standardizing data structure and format, and addressing white spaces and other grammatical problems.

  • Analyze the Data − You may begin to identify patterns, correlations, outliers, and variations that tell a narrative by transforming the data using different data analysis methods and tools. At this phase, you may utilize data mining to identify trends within databases or data visualization tools to convert data into an easily digestible graphical format.

  • Interpret − Determine how effectively the findings of your analysis addressed your initial query by interpreting them. Based on the facts, what suggestions are possible? What constraints do your conclusions have?