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LESSONS & TOPICS

1-1 Data analysis

1-1 Data analysis

What is data analysis

  • Comprehensive method of inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
  • A multifaceted process involving various techniques and methodologies to interpret data from various sources in different formats, both structured and unstructured.
  • Tool that empowers organizations to make informed decisions, predict trends, and improve operational efficiency. It’s the backbone of strategic planning in businesses, governments, and other organizations.

https://www.datacamp.com/blog/what-is-data-analysis-expert-guide

The data analysis process

  • Define the question or goal behind the analysis: what are you trying to discover?
  • Collect the right data to help answer this question.
  • Perform data cleaning/data wrangling to improve data quality and prepare it for analysis and interpretation–getting data into the right format, getting rid of unnecessary data, correcting spelling mistakes, etc.
  • Manipulate the data. This may include plotting the data out, creating pivot tables, and so on.
  • Analyze and interpret the data using statistical tools (i.e. finding correlations, trends, outliers, etc.).
  • Present this data in meaningful ways: graphs, visualizations, charts, tables, etc.

https://www.datacamp.com/blog/what-is-data-analysis-expert-guide
https://learntocodewith.me/posts/data-analysis/

Types of Data Analysis

  • Descriptive analysis

    • Designed to answer the question “What happened?” The goal of descriptive analytics is to summarize data in a meaningful and descriptive manner, not to make any predictions

  • Exploratory analysis

    • Dives a bit deeper than descriptive analytics, skimming for detectable patterns and trends in data. Another way to think of this is the initial investigation phase.

  • Diagnostic analysis

    • Takes the insights found from both descriptive and exploratory analytics and investigates further to find the causes.

  • Predictive analysis

    • Uses data, statistics, and machine learning algorithms and techniques to figure out the likelihood of future outcomes based on data. Examples include sales forecasting and risk assessment.

  • Prescriptive analysis

    • Takes insights found from all types of data analysis (descriptive, exploratory, diagnostic, predictive) to determine the best course of action.

https://learntocodewith.me/posts/data-analysis/

https://www.datacamp.com/blog/what-is-data-analysis-expert-guide

Why should we learn data analysis?

  • Job growth for data professionals
  • Data analytics is in demand
  • Higher than average salaries
  • Competitive advantage
  • Universal need

https://learntocodewith.me/posts/data-analysis/