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topic covers Types of Analysis Analysis is categorized based on the number of variables involved: Distribution Analysis What is a Histogram? A histo…
topic covers Descriptive Statistics: The Basics Descriptive statistics help summarize and describe the essential features of a dataset. They are gen…
Scheme and Syllabus Data Science Please select the unit you would like to study. Simply click on a unit title to focus on those specific topics: UN…
topic covers
Types of Analysis
Analysis is categorized based on the number of variables involved:
Distribution Analysis
A histogram takes a large set of data points and groups them into logical ranges called "bins."
* The Bins (X-axis): These represent the intervals of the data (e.g., age groups 0–10, 11–20, etc.).
The result is a series of rectangles whose area is proportional to the frequency of the variable. Because the data is continuous, the bars touch each other, unlike a categorical bar chart.
In the context of data science and EDA, the importance of a histogram can be summarized into four key points:
Checks Distribution: It reveals if your data follows a "Normal Distribution." Many machine learning algorithms (like Linear Regression) perform better when data is normally distributed.
Spots Outliers: It visually highlights "lonely" bars far from the main group, helping you identify errors or extreme values that could skew your model.
Identifies Skewness: It shows if your data is "leaning" to one side (left or right). This tells you if you need to transform the data (e.g., using a Log transform) before training a model.
Reveals Data Spreading: It provides an instant look at the range and variance of your dataset—showing whether your values are tightly packed or widely scattered.
Box Plot (Whisker Plot)
Definition: A graphical representation of the five-number summary of a dataset: Minimum, First Quartile (Q1), Median (Q2), Third Quartile (Q3), and Maximum. It uses a "box" to represent the middle 50% of the data and "whiskers" to show the rest.
Short Importance:
Outlier Detection: Visually isolates data points that fall outside the typical range.Comparison: Easily compares the spread and medians of different categories side-by-side. Skewness: Shows if data is symmetrical or pushed toward one end.
Short Code (Python):
Scatter Plot
Definition: A plot that uses dots to represent the values of two different numerical variables. One variable is plotted on the horizontal axis (X) and the other on the vertical axis (Y).
Short Importance: * Correlation: Shows if variables move together (positive), in opposite directions (negative), or not at all.Patterns: Helps identify clusters or non-linear shapes (like curves) in the data.Individual Points: Allows you to see every single data point, making it easy to spot specific anomalies.
When performing Exploratory Data Analysis (EDA), the choice of visualization is determined by the data types of the variables you are analyzing.
The following table summarizes the best plots to use based on the combination of Categorical (labels/groups) and Numerical (continuous numbers) variables.
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Mean (Average): The sum of all values divided by the total number of values. It is sensitive to outliers (extreme values).
These metrics describe how much the data observations vary from the center and from each other.:
Variance (σ²): The average of the squared differences from the Mean. Squaring the differences ensures that negative deviations don't cancel out positive ones.
Examaple >>>
Please select the unit you would like to study. Simply click on a unit title to focus on those specific topics:
UNIT I: Introduction to Data Science
UNIT II: Data Wrangling & Cleaning
UNIT III: Exploratory Data Analysis (EDA) – Part I
UNIT IV: Exploratory Data Analysis (EDA) – Part II
UNIT V: Case Studies & EDA Projects
Projects Completed
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Worldwide Clients
Worldwide Clients