Data Science Projects

Randomized experiments are the gold standard for measuring impact. Here’s how to measure impact with randomized trials. 👇

𝟏. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭
Planning the structure and methodology of the experiment, including defining the hypothesis, selecting metrics, and conducting a power analysis to determine sample size.
⤷ Ensures the experiment is well-structured and statistically sound, minimizing bias and maximizing reliability.

𝟐. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐕𝐚𝐫𝐢𝐚𝐧𝐭𝐬
Creating different versions of the intervention by developing and deploying the control (A) and treatment (B) versions.
⤷ Allows for a clear comparison between the current state and the proposed change.

𝟑. 𝐂𝐨𝐧𝐝𝐮𝐜𝐭 𝐓𝐞𝐬𝐭
Choosing the right statistical test and calculating test statistics, such as confidence intervals, p-values, and effect sizes.
⤷ Ensures the results are statistically valid and interpretable.

𝟒. 𝐀𝐧𝐚𝐥𝐲𝐳𝐞 𝐑𝐞𝐬𝐮𝐥𝐭𝐬
Evaluating the data collected from the experiment, interpreting confidence intervals, p-values, and effect sizes to determine statistical significance and practical impact.
⤷ Helps determine whether the observed changes are meaningful and should be implemented.

𝟓. 𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐚𝐜𝐭𝐨𝐫𝐬
⤷ Network Effects: User interactions affecting experiment outcomes.
⤷ P-Hacking: Manipulating data for significant results.
⤷ Novelty Effects: Temporary boost from new features.

Hope this helps you 😊

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2.78K viewsedited 06:33

Data Science Projects

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

Some common supervised learning algorithms include:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

Some popular unsupervised learning algorithms include:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

Common semi-supervised learning algorithms include:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

Popular reinforcement learning algorithms include:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

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2.89K views10:05

Data Science Projects

🔟 Project Ideas for a data analyst

Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.

Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.

Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.

Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.

Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.

Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.

Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.

A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.

Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.

Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.

Remember to choose a project that aligns with your interests and the domain you're passionate about.

Data Analyst Roadmap
👇👇
https://t.iss.one/sqlspecialist/379

ENJOY LEARNING 👍👍

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2.96K views06:37

Data Science Projects

🔅 Convert Video to Audio using Python

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2.49K views03:36

Data Science Projects

Hi Guys,

Here are some of the telegram channels which may help you in data analytics journey 👇👇

SQL: https://t.iss.one/sqlanalyst

Power BI & Tableau: https://t.iss.one/PowerBI_analyst

Excel: https://t.iss.one/excel_analyst

Python: https://t.iss.one/dsabooks

Jobs: https://t.iss.one/jobs_SQL

Data Science: https://t.iss.one/datasciencefree

Artificial intelligence: https://t.iss.one/machinelearning_deeplearning

Data Engineering: https://t.iss.one/sql_engineer

Data Analysts: https://t.iss.one/sqlspecialist

Hope it helps :)

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2.75K views07:27

Data Science Projects

You don't need to buy a GPU for machine learning work!

There are other alternatives. Here are some:

1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim

Spend your time focusing on your problem.💪💪

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2.51K viewsedited 03:17

Data Science Projects

95% of Machine Learning solutions in the real world are for tabular data.

Not LLMs, not transformers, not agents, not fancy stuff.

Learning to do feature engineering and build tree-based models will open a ton of opportunities.

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2.41K views06:29

Data Science Projects