Important data science topics you should definitely be aware of

1. Statistics & Probability

Denoscriptive Statistics (mean, median, mode, variance, std deviation)
Probability Distributions (Normal, Binomial, Poisson)
Bayes' Theorem
Hypothesis Testing (t-test, chi-square test, ANOVA)
Confidence Intervals

2. Data Manipulation & Analysis

Data wrangling/cleaning
Handling missing values & outliers
Feature engineering & scaling
GroupBy operations
Pivot tables
Time series manipulation

3. Programming (Python/R)

Data structures (lists, dictionaries, sets)
Libraries:
Python: pandas, NumPy, matplotlib, seaborn, scikit-learn
R: dplyr, ggplot2, caret
Writing reusable functions
Working with APIs & files (CSV, JSON, Excel)

4. Data Visualization
Plot types: bar, line, scatter, histograms, heatmaps, boxplots
Dashboards (Power BI, Tableau, Plotly Dash, Streamlit)
Communicating insights clearly

5. Machine Learning

Supervised Learning
Linear & Logistic Regression
Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
SVM, KNN

Unsupervised Learning
K-means Clustering
PCA
Hierarchical Clustering

Model Evaluation
Accuracy, Precision, Recall, F1-Score
Confusion Matrix, ROC-AUC
Cross-validation, Grid Search

6. Deep Learning (Basics)
Neural Networks (perceptron, activation functions)
CNNs, RNNs (just an overview unless you're going deep into DL)
Frameworks: TensorFlow, PyTorch, Keras

7. SQL & Databases
SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries
Window functions
Indexes and Query Optimization

8. Big Data & Cloud (Basics)
Hadoop, Spark
AWS, GCP, Azure (basic knowledge of data services)

9. Deployment & MLOps (Basic Awareness)
Model deployment (Flask, FastAPI)
Docker basics
CI/CD pipelines
Model monitoring

10. Business & Domain Knowledge
Framing a problem
Understanding business KPIs
Translating data insights into actionable strategies

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Some essential concepts every data scientist should understand:

### 1. Statistics and Probability
- Purpose: Understanding data distributions and making inferences.
- Core Concepts: Denoscriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals.

### 2. Programming Languages
- Purpose: Implementing data analysis and machine learning algorithms.
- Popular Languages: Python, R.
- Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R).

### 3. Data Wrangling
- Purpose: Cleaning and transforming raw data into a usable format.
- Techniques: Handling missing values, data normalization, feature engineering, data aggregation.

### 4. Exploratory Data Analysis (EDA)
- Purpose: Summarizing the main characteristics of a dataset, often using visual methods.
- Tools: Matplotlib, Seaborn (Python), ggplot2 (R).
- Techniques: Histograms, scatter plots, box plots, correlation matrices.

### 5. Machine Learning
- Purpose: Building models to make predictions or find patterns in data.
- Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score).
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA).

### 6. Deep Learning
- Purpose: Advanced machine learning techniques using neural networks.
- Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout.
- Frameworks: TensorFlow, Keras, PyTorch.

### 7. Natural Language Processing (NLP)
- Purpose: Analyzing and modeling textual data.
- Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings.
- Techniques: Sentiment analysis, topic modeling, named entity recognition (NER).

### 8. Data Visualization
- Purpose: Communicating insights through graphical representations.
- Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau.
- Techniques: Bar charts, line graphs, heatmaps, interactive dashboards.

### 9. Big Data Technologies
- Purpose: Handling and analyzing large volumes of data.
- Technologies: Hadoop, Spark.
- Core Concepts: Distributed computing, MapReduce, parallel processing.

### 10. Databases
- Purpose: Storing and retrieving data efficiently.
- Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra).
- Core Concepts: Querying, indexing, normalization, transactions.

### 11. Time Series Analysis
- Purpose: Analyzing data points collected or recorded at specific time intervals.
- Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing.

### 12. Model Deployment and Productionization
- Purpose: Integrating machine learning models into production environments.
- Techniques: API development, containerization (Docker), model serving (Flask, FastAPI).
- Tools: MLflow, TensorFlow Serving, Kubernetes.

### 13. Data Ethics and Privacy
- Purpose: Ensuring ethical use and privacy of data.
- Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance.

### 14. Business Acumen
- Purpose: Aligning data science projects with business goals.
- Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication.

### 15. Collaboration and Version Control
- Purpose: Managing code changes and collaborative work.
- Tools: Git, GitHub, GitLab.
- Practices: Version control, code reviews, collaborative development.

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Basics of Machine Learning 👇👇

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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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Interview Mail Tips-

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𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Application for [Job Title] - [Your Name]

Dear [Hiring Manager's Name],

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Thank you for considering my application. I look forward to the opportunity to speak with you.

Best regards,
[Your Name]

✅ 𝗙𝗼𝗹𝗹𝗼𝘄-𝗨𝗽 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Follow-Up on My Interview

Hi [Hiring Manager's Name],

I hope you're doing well. I wanted to follow up on the interview we had for the [Job Title] position at [Company Name]. I'm really excited about the opportunity and would love to hear about the next steps in the process.

Looking forward to your response.

Best regards,
[Your Name]

✅ 𝗥𝗲𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

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Thank you once again.

Best regards,
[Your Name]

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𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Salary Discussion for [Job Title] Position

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Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌

I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.

Here are the probability units you will need to focus on:

Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra

Python:

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking

Machine Learning Prerequisites:

Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data

Machine Learning Fundamentals

Using scikit-learn library in combination with other Python libraries for:

Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)

Solving two types of problems:
Regression
Classification

Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:

Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.

In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.

Deep Learning:

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models

Machine Learning Project Deployment

Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:

Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs

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10 Machine Learning Concepts You Must Know

1. Supervised vs Unsupervised Learning

Supervised Learning involves training a model on labeled data (input-output pairs). Examples: Linear Regression, Classification.

Unsupervised Learning deals with unlabeled data. The model tries to find hidden patterns or groupings. Examples: Clustering (K-Means), Dimensionality Reduction (PCA).


2. Bias-Variance Tradeoff

Bias is the error due to overly simplistic assumptions in the learning algorithm.

Variance is the error due to excessive sensitivity to small fluctuations in the training data.

Goal: Minimize both for optimal model performance. High bias → underfitting; High variance → overfitting.


3. Feature Engineering

The process of selecting, transforming, and creating variables (features) to improve model performance.

Examples: Normalization, encoding categorical variables, creating interaction terms, handling missing data.


4. Train-Test Split & Cross-Validation

Train-Test Split divides the dataset into training and testing subsets to evaluate model generalization.

Cross-Validation (e.g., k-fold) provides a more reliable evaluation by splitting data into k subsets and training/testing on each.


5. Confusion Matrix

A performance evaluation tool for classification models showing TP, TN, FP, FN.

From it, we derive:

Accuracy = (TP + TN) / Total

Precision = TP / (TP + FP)

Recall = TP / (TP + FN)

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)



6. Gradient Descent

An optimization algorithm used to minimize the cost/loss function by iteratively updating model parameters in the direction of the negative gradient.

Variants: Batch GD, Stochastic GD (SGD), Mini-batch GD.


7. Regularization (L1/L2)

Techniques to prevent overfitting by adding a penalty term to the loss function.

L1 (Lasso): Adds absolute value of coefficients, can shrink some to zero (feature selection).

L2 (Ridge): Adds square of coefficients, tends to shrink but not eliminate coefficients.


8. Decision Trees & Random Forests

Decision Tree: A tree-structured model that splits data based on features. Easy to interpret.

Random Forest: An ensemble of decision trees; reduces overfitting and improves accuracy.


9. Support Vector Machines (SVM)

A supervised learning algorithm used for classification. It finds the optimal hyperplane that separates classes.

Uses kernels (linear, polynomial, RBF) to handle non-linearly separable data.


10. Neural Networks

Inspired by the human brain, these consist of layers of interconnected neurons.

Deep Neural Networks (DNNs) can model complex patterns.

The backbone of deep learning applications like image recognition, NLP, etc.

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Skills for Data Scientists 👆

Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting , F1 Score

1. What do you understand by the term silhouette coefficient?

The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.


2. What is the difference between trend and seasonality in time series?

Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.


3. What is Bag of Words in NLP?

Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.


4. What is the difference between bagging and boosting?

Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm

5. What do you understand by the F1 score?

The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.

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You know what DOESN'T matter?

How you got started in data.

Maybe you focused on a single tool.
Maybe you learned Python before SQL.
Maybe you thought you needed to know R.
Maybe you only know Excel and that's all you need.
Maybe you tried Power BI before deciding on Tableau.

It doesn't matter how you get started - it matters how you continue.

Do you...

- provide insights that drive business decisions?
- help stakeholders meet goals and objectives?
- analyze data to add value to your organization?
- ask questions and use them to guide analysis?
- effectively explain what your analysis means?

How you get started in data has much less importance than what you do once you're in.

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝘀𝗵𝗮𝗽𝗲 𝘆𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿: 👇

-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean noscripts, automate tasks, and manipulate data like a pro.

-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.

-> 3. Nail the Basics of Statistics & Probability
You can’t call yourself a data scientist if you don’t understand distributions, p-values, confidence intervals, and hypothesis testing.

-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.

-> 5. Learn Machine Learning the Right Way

Start simple:

Linear Regression

Logistic Regression

Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.


-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problems—don’t just learn, apply.
Make a portfolio that speaks louder than your resume.

-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
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Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.


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Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

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1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
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1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒕𝒉𝒆 𝑩𝒂𝒔𝒊𝒄𝒔 𝒐𝒇 𝑺𝑸𝑳

𝐀. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞?: Understanding the concept of databases and relational databases.

𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐃𝐁𝐌𝐒): Learn about different DBMS like MySQL, PostgreSQL, SQL Server, Oracle.

𝐁. 𝐁𝐚𝐬𝐢𝐜 𝐒𝐐𝐋 𝐂𝐨𝐦𝐦𝐚𝐧𝐝𝐬

𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥:
𝐒𝐄𝐋𝐄𝐂𝐓: Basic retrieval of data.
𝐖𝐇𝐄𝐑𝐄: Filtering data based on conditions.
𝐎𝐑𝐃𝐄𝐑 𝐁𝐘: Sorting results.
𝐋𝐈𝐌𝐈𝐓: Limiting the number of rows returned.

𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧:
𝐈𝐍𝐒𝐄𝐑𝐓: Adding new data.
𝐔𝐏𝐃𝐀𝐓𝐄: Modifying existing data.
𝐃𝐄𝐋𝐄𝐓𝐄: Removing data.

2. 𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐒𝐐𝐋 𝐒𝐤𝐢𝐥𝐥𝐬
𝐀. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥

𝐉𝐎𝐈𝐍𝐬: Understanding different types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Using functions like COUNT, SUM, AVG, MIN, MAX.
𝐆𝐑𝐎𝐔𝐏 𝐁𝐘: Grouping data to perform aggregate calculations.
𝐇𝐀𝐕𝐈𝐍𝐆: Filtering groups based on aggregate values.

𝐁. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 𝐚𝐧𝐝 𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬
𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬: Using queries within queries.
𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬: Subqueries that reference columns from the outer query.

𝑪. 𝑫𝒂𝒕𝒂 𝑫𝒆𝒇𝒊𝒏𝒊𝒕𝒊𝒐𝒏 𝑳𝒂𝒏𝒈𝒖𝒂𝒈𝒆 (𝑫𝑫𝑳)
𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐓𝐚𝐛𝐥𝐞𝐬: CREATE TABLE.
𝐌𝐨𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐓𝐚𝐛𝐥𝐞𝐬: ALTER TABLE.
𝑹𝒆𝒎𝒐𝒗𝒊𝒏𝒈 𝑻𝒂𝒃𝒍𝒆𝒔: DROP TABLE.

3. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬
𝐀. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
𝐈𝐧𝐝𝐞𝐱𝐞𝐬: Understanding and creating indexes to speed up queries.
𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Techniques to write efficient SQL queries.

𝐁. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬
𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Using functions like ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG.
𝐂𝐓𝐄 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬): Using WITH to create temporary result sets.

𝐂. 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲
𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬: Using BEGIN, COMMIT, ROLLBACK.
𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Understanding isolation levels and locking mechanisms.

4. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬
𝐀. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧
𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Understanding normal forms and how to normalize databases.
𝐄𝐑 𝐃𝐢𝐚𝐠𝐫𝐚𝐦𝐬: Creating Entity-Relationship diagrams to model databases.

𝐁. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧
𝐄𝐓𝐋 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬: Extract, Transform, Load processes for data integration.

𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐓𝐫𝐢𝐠𝐠𝐞𝐫𝐬: Writing and using stored procedures and triggers for complex logic and automation.

𝐂. 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐚𝐧𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬: Work on case studies involving complex database operations.

𝐂𝐚𝐩𝐬𝐭𝐨𝐧𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Develop comprehensive projects that showcase your SQL expertise.

𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐓𝐨𝐨𝐥𝐬
𝐁𝐨𝐨𝐤𝐬: "SQL in 10 Minutes, Sams Teach Yourself" by Ben Forta, "SQL for Data Scientists" by Renee M. P. Teate.
𝐎𝐧𝐥𝐢𝐧𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: Coursera, Udacity, edX, Khan Academy.
𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: LeetCode, HackerRank, Mode Analytics, SQLZoo.

Let's explore some of the best open source projects by language.

1⃣ Best Python Open Source Projects

🚣‍♂ TensorFlow
🚣‍♂ Matplotlib
🚣‍♂ Flask
🚣‍♂ Django
🚣‍♂ PyTorch

2⃣ Best JavaScript Open Source Projects

🚣‍♂ React
🚣‍♂ Node.JS
🚣‍♂ jQuery

3⃣ Best C++ Open Source Projects

🚣‍♂ Serenity
🚣‍♂ MongoDB
🚣‍♂ SonarSource
🚣‍♂ OBS Studio
🚣‍♂ Electron

4⃣ Best Java Open Source Projects

🚣‍♂ Mockito
🚣‍♂ Realm
🚣‍♂ Jenkins
🚣‍♂ Guava
🚣‍♂ Moshi


It's time to start developing your own open source projects. Explore the projects

New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc.

Don't get bogged down trying to learn every new term & technology you come across.

Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.

The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!

10 commonly asked data science interview questions along with their answers

1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.

2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.

3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.

4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.

5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.

6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.

7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.

8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.

9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.

🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://news.1rj.ru/str/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

Source codes for data science projects 👇👇

1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro

2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python

3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/

4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/

5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/

6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/

7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/

𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍