Statistical interview questions for entry-level data analyst roles in an MNC.

1. Explain the difference between mean, median, and mode. When would you use each?
2. How do you calculate the variance and standard deviation of a dataset?
3. What is skewness and kurtosis? How do they help in understanding data distribution?
4. What is the central limit theorem, and why is it important in statistics?
5. Describe different types of probability distributions (e.g., normal, binomial, Poisson).
6. Explain the difference between a population and a sample. Why is sampling important?
7. What are null and alternative hypotheses? How do you formulate them?
8. Describe the steps in conducting a hypothesis test.
9. What is a p-value? How do you interpret it in the context of a hypothesis test?
10. When would you use a t-test versus a z-test?
11. Explain how you would conduct an independent two-sample t-test. What assumptions must be met?
12. Describe a scenario where you would use a paired sample t-test.
13. What is ANOVA, and how does it differ from a t-test?
14. Explain how you would interpret the results of a one-way ANOVA.
15. Describe a situation where you might use a two-way ANOVA.
16. What is a chi-square test for independence? When would you use it?
17. How do you interpret the results of a chi-square goodness-of-fit test?
18. Explain the assumptions and limitations of chi-square tests.
19. What is the difference between simple linear regression and multiple regression?
20. How do you assess the goodness-of-fit of a regression model?
21. Explain multicollinearity and how you would detect and handle it in a regression model.
22. What is the difference between correlation and causation?
23. How do you interpret the Pearson correlation coefficient?
24. When would you use Spearman rank correlation instead of Pearson correlation?
25. What are some common methods for forecasting time series data?
26. Explain the components of a time series (trend, seasonality, residuals).
27. How would you handle missing data in a time series dataset?
28. Describe your approach to exploratory data analysis (EDA).
29. How do you handle outliers in a dataset?
30. Explain the steps you would take to validate the results of your analysis.
31. Give an example of how you have used statistical analysis to solve a real-world problem

Hope this helps you 😊

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Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of denoscriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

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Let's break it down.

🔹 Reflection
You give the agent an input.
The agent then "reflects" on its output, and based on feedback, improves and refines.

Ideal tools to use:
- Base model (e.g. GPT-4o)
- Fine-tuned model (to give feedback)
- n8n to set up the agent.

🔹 RAG-based
You give the agent a task.
The agent has the ability to query an external knowledge base to retrieve specific information needed.

Ideal tools to use:
- Vector Database (e.g. Pinecone).
- UI-based RAG (Aidbase is the #1 tool).
- API-based RAG (SourceSync is a new player on the market, highly promising).

🔹 AI Workflow
This is a "traditional" automation workflow that uses AI to carry out subtasks as part of the flow.

Ideal tools to use:
- n8n to handle the workflow.
- GPT-4o, Claude, or other models that can be accessed through API (basic HTTP requests).

If you can master these 3 patterns well, you can solve a very broad range of different problems.

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Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:

1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.

2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.

3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.

Key concepts include:

- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.

- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.

- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.

- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.

In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.

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