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  • What is your experience with data analysis and what tools do you use?
  • How do you approach a data analysis project from start to finish?
  • What is your understanding of statistical analysis and how do you apply it to your work?
  • How do you handle missing data or outliers in your analysis?
  • How do you ensure the quality and accuracy of your data analysis?
  • How do you communicate your findings and insights to stakeholders who may not have a background in data analysis?
  • What is your experience with SQL and databases?
  • What is your experience with programming languages such as Python or R?
  • Can you explain a data analysis project you worked on that utilized machine learning techniques?
  • How do you evaluate the effectiveness of a machine learning model?
  • How do you handle imbalanced data sets in machine learning?
  • Can you explain the difference between supervised and unsupervised learning?
  • What is your experience with data visualization tools such as Tableau or Power BI?
  • Can you explain a data visualization project you worked on and how you chose the appropriate visualization techniques?
  • What is your experience with big data technologies such as Hadoop or Spark?
  • How do you handle scalability in data analysis projects?
  • Can you explain a project you worked on where you utilized data mining techniques?
  • What is your experience with data warehousing and ETL processes?
  • How do you handle data security and privacy concerns in your analysis?
  • What is your experience with cloud-based data analysis tools.
  • What is machine learning and how does it differ from traditional programming?
  • What are the different types of machine learning algorithms?
  • What is the difference between supervised and unsupervised learning?
  • What is the difference between classification and regression?
  • What is overfitting and how can it be avoided?
  • What is the bias-variance tradeoff?
  • What is cross-validation and why is it important?
  • What is regularization and why is it used?
  • What is the curse of dimensionality and how can it be addressed?
  • What is ensemble learning and how does it work?
  • What is deep learning and how is it used?
  • What is backpropagation and how is it used in neural networks?
  • What is a convolutional neural network and how is it different from other neural networks?
  • What is a recurrent neural network and how is it used?
  • What is transfer learning and how is it used in machine learning?
  • What is reinforcement learning and how is it used?
  • What is the difference between batch learning and online learning?
  • What is the difference between a generative model and a discriminative model?
  • What is the difference between a parametric model and a non-parametric model?
  • What is the difference between a decision tree and a random forest?
  • What is the difference between K-means and hierarchical clustering?
  • What is support vector machine and how does it work?
  • What is the difference between linear regression and logistic regression?
  • What is the ROC curve and why is it used?
  • What is precision and recall and how are they used in classification models?

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1- Advance Excel
2- SQL Sever
3- Python (Basic Python-Lib.Pandas-Numpy-Matplotlib-Seaborn Etc.)
4- Basic Statistics
5- Power-BI / Tableau
6- Live Project
 

Machine Learning
1- Advance Statistics
2- Python (Basic python-Lib.Pandas-Numpy-Matplotlib-Seaborn -Scikit Learn Etc.)
3- Machine Learning Algorithms.
    a- Linear Regression Algorithms.
    b- Logstic Regression
    c- Random Forecast
    d- Dicigon Making
    e- Naive Bayes Classifier
    f-  Cluster Analysis

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