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TOP 10 SQL Concepts for Job Interview

1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)


TOP 10 Statistics Concepts for Job Interview

1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression


TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

One day or Day one. You decide.

Data Science edition.

𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.

Breaking into Data Analytics doesn’t need to be complicated.

If you’re just starting out,

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Avoid:
🚫 Jumping into advanced tools like Hadoop or Spark before mastering the basics.
🚫 Focusing only on tools, not on business problem-solving.
🚫 Collecting certificates instead of solving real problems.
🚫 Thinking you need to know everything from SQL to machine learning right away.

Instead:
✅ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau).
✅ Learn how to clean, explore, and interpret data to solve business questions.
✅ Understand core concepts like KPIs, dashboards, and business metrics.
✅ Pick real datasets and analyze them with clear goals and insights.
✅ Build a portfolio that shows you can translate data into decisions.

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Want to become a Data Scientist?

Here’s a quick roadmap with essential concepts:

1. Mathematics & Statistics

Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.

Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.

Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.


2. Programming

Python or R: Choose a primary programming language for data science.

Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.

R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.


SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.


3. Data Wrangling & Preprocessing

Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.


4. Data Visualization

Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.


5. Machine Learning

Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.


6. Advanced Machine Learning & Deep Learning

Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.


7. Natural Language Processing (NLP)

Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.


8. Big Data Tools (Optional)

Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.


9. Data Science Workflows & Pipelines (Optional)

ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).


10. Model Validation & Tuning

Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.


11. Time Series Analysis

Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.


12. Experimentation & A/B Testing

Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.

ENJOY LEARNING 👍👍

#datascience

𝐒𝐭𝐚𝐫𝐭 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — 𝟏𝟎𝟎% 𝐅𝐫𝐞𝐞 & 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲😍

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Top 10 CSS Interview Questions

1. What is CSS and what are its key features?
CSS (Cascading Style Sheets) is a stylesheet language used to describe the presentation of a document written in HTML or XML. Its key features include controlling layout, styling text, setting colors, spacing, and more, allowing for a separation of content and design for better maintainability and flexibility.

2. Explain the difference between inline, internal, and external CSS.
- Inline CSS is applied directly within an HTML element using the style attribute.
- Internal CSS is defined within a <style> tag inside the <head> section of an HTML document.
- External CSS is linked to an HTML document via the <link> tag and is written in a separate .css file.

3. What is the CSS box model and what are its components?
The CSS box model describes the rectangular boxes generated for elements in the document tree and consists of four components:
- Content: The actual content of the element.
- Padding: The space between the content and the border.
- Border: The edge surrounding the padding.
- Margin: The space outside the border that separates the element from others.

4. How do you center a block element horizontally using CSS?
To center a block element horizontally, you can use the margin: auto; property. For example:

.center {
  width: 50%;
  margin: auto;
}

5. What are CSS selectors and what are the different types?
CSS selectors are patterns used to select elements to apply styles. The different types include:
- Universal selector (*)
- Element selector (element)
- Class selector (.class)
- ID selector (#id)
- Attribute selector ([attribute])
- Pseudo-class selector (:pseudo-class)
- Pseudo-element selector (::pseudo-element)

6. Explain the difference between absolute, relative, fixed, and sticky positioning in CSS.
- relative: The element is positioned relative to its normal position.
- absolute: The element is positioned relative to its nearest positioned ancestor or the initial containing block if none exists.
- fixed: The element is positioned relative to the viewport and does not move when the page is scrolled.
- sticky: The element is treated as relative until a given offset position is met in the viewport, then it behaves as fixed.

7. What is Flexbox and how is it used in CSS?
Flexbox (Flexible Box Layout) is a layout model that allows for more efficient arrangement of elements within a container. It is used to align and distribute space among items in a container, even when their size is unknown or dynamic. Flexbox is enabled by setting display: flex; on a container element.

8. How do you create a responsive design in CSS?
Responsive design can be achieved using media queries, flexible grid layouts, and relative units like percentages, em, and rem. Media queries adjust styles based on the viewport's width, height, and other characteristics. For example:

@media (max-width: 600px) {
  .container {
    width: 100%;
  }
}

9. What are CSS preprocessors and name a few popular ones.
CSS preprocessors extend CSS with variables, nested rules, and functions, making it more powerful and easier to maintain. Popular CSS preprocessors include:
- Sass (Syntactically Awesome Style Sheets)
- LESS (Leaner Style Sheets)
- Stylus

10. How do you implement CSS animations?
CSS animations are implemented using the @keyframes rule to define the animation and the animation property to apply it to an element. For example:

@keyframes example {
  from {background-color: red;}
  to {background-color: yellow;}
}

.element {
  animation: example 5s infinite;
}

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ENJOY LEARNING 👍👍

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