Data Cleaning and Preparation

Asikur19

Statistical Concepts

• Probability distributions: Normal, binomial, Poisson, etc.
• Hypothesis testing: T-test, ANOVA, Chi-square test
• Correlation and regression: Simple and multiple regression
• Time series analysis: Forecasting methods (ARIMA, exponential smoothing)
  

Data Cleaning and Preparation

• Missing data handling: Imputation techniques
• Outlier detection and handling: Methods to identify and treat outliers
• Data normalization and standardization: Techniques to scale data
• Data transformation: Log transformations, etc.
  

Data Visualization

• Choosing appropriate visualizations: Bar charts, line charts, scatter plots, histograms, etc.
• Data storytelling: Effectively Telegram Number communicating insights through visualizations
• Using tools like: Tableau, Power BI, Excel, Python (Matplotlib, Seaborn)
  

SQL

• Basic queries: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY
• Joins: Inner, left, right, outer joins
• Subqueries and correlated subqueries
• Aggregation functions: COUNT, SUM, AVG, MIN, MAX
  

Python for Data Analysis

• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• Data manipulation and analysis
• Machine learning concepts: Classification, regression, clustering
  

Business Understanding

• Interpreting data in a business context
• Identifying key metrics and KPIs
• Communicating findings to stakeholders
  

Example Questions:

• How would you handle a dataset with a large number of missing values?
• Explain the difference between correlation and causation.
• What is the purpose of data normalization?
• Write a SQL query to find the top 5 customers by total sales.
• Describe the steps involved in building a regression model.
• What is the bias-variance trade-off in machine learning?
• How would you visualize the distribution of a categorical variable?
  

Practice resources:

• Online courses: Coursera, edX, Udemy
• Books: "Python for Data Analysis" by Wes McKinney, "Data Analysis with Python" by Jake VanderPlas
• Practice problems: HackerRank, LeetCode, Kaggle
  

Remember: The key to success in a data analyst exam is a strong understanding of the underlying concepts and the ability to apply them to real-world problems.