Steps in Data Analysis:

• Define the Question: Determine what you want to find out. A clear question helps focus your analysis and ensures you collect the relevant data.
• Collect Data: Gather the relevant data from various sources. This could include databases, surveys, or logs.
• Clean the Data: Remove any errors, duplicates, or inconsistencies in the data. Data cleaning is important to ensure accurate analysis.
• Analyze the Data: Use statistical methods and tools to find patterns, trends, and insights in the data. This can include calculations, statistical tests, and modeling.
• Interpret the Results: Draw conclusions from the analysis. Determine what the data tells you about the question you defined.
• Communicate Findings: Share the insights with others through reports, dashboards, or presentations. Effective communication ensures that the findings are understood and actionable.

Common Techniques

• Descriptive Statistics: Includes measures like mean (average), median (middle value), mode (most frequent value), and standard deviation (measure of spread).
• Data Cleaning: Involves handling missing values, removing duplicates, and correcting errors.
• Data Transformation: Converting data into a usable format, such as normalizing data or converting text to numerical values.
• Exploratory Data Analysis (EDA): Summarizing the main characteristics of data using visual methods like histograms, box plots, and scatter plots.

Example

You have a dataset of customer feedback scores. Using descriptive statistics, you can calculate the average score, identify the most common score, and find the spread of scores.

Activity

Collect a small dataset (e.g., daily temperatures for a week) and calculate the mean, median, and mode.

Quiz

• 1. What is the mean of the dataset {2, 4, 6, 8, 10}?

  • a) 4
  • b) 5
  • c) 6
  • d) 7

• 2. True or False: The median is the value that appears most frequently in a dataset.

  • a) True
  • b) False

• 3. What does a correlation coefficient close to 1 indicate?

  • a) No correlation
  • b) Weak negative correlation
  • c) Strong positive correlation
  • d) Strong negative correlation

• 4. Which method can be used to visualize the distribution of data?

  • a) Line graph
  • b) Pie chart
  • c) Histogram
  • d) Bar chart

• 5. What type of analysis involves summarizing the main features of a dataset?

  • a) Inferential
  • b) Predictive
  • c) Descriptive
  • d) Prescriptive