Data science analyst -for intermediate

Data Science Analyst Study
(Intermediate Level)
1. Exploratory Data Analysis (EDA)
 Descriptive Statistics: Mean, Median, Mode, Standard Deviation,
Variance
 Data Visualization: Histograms, Boxplots, Scatter Plots, Pair
Plots
 Correlation & Covariance: Pearson and Spearman correlation
 Outlier Detection: Z-score, IQR method, Visual inspection

2. Data Wrangling & Cleaning
 Handling Missing Data: Imputation techniques (mean, median,
mode, KNN, regression)
 Data Transformation: Encoding categorical variables (One-Hot,
Label Encoding)
 Feature Engineering: Creating new features, Feature scaling
(Normalization, Standardization)
 Dealing with Duplicates & Anomalies

3. SQL for Data Analysis

 Basic Queries: SELECT, WHERE, GROUP BY, ORDER BY,
HAVING
 Joins & Subqueries: INNER, LEFT, RIGHT, FULL JOINs,
Common Table Expressions (CTEs)
 Aggregation Functions: COUNT, SUM, AVG, MIN, MAX
 Window Functions: ROW_NUMBER(), RANK(), LEAD(),
LAG()