Advertising on the Telegram channel «Data Science Projects»

I was lost in crypto noise — until I found a channel that shows where the real money is made👍 No hype, just clear signals and smart entries. 👉🏼 Subscribe now — all you need to do is follow the trades. It’s that simple: https://t.me/+ixExN-YdZsc5M2Iy

Some essential concepts every data scientist should understand: ### 1. Statistics and Probability - Purpose: Understanding data distributions and making inferences. - Core Concepts: Descriptive 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. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING 👍👍

𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗝𝘂𝘀𝘁 𝟯 𝗖𝗼𝗿𝗲 𝗦𝗸𝗶𝗹𝗹𝘀!😍 Want to break into Data Analytics without a degree or expensive bootcamps?👨‍💻📌 All you need are 3 essentials to get started👇 📊 Excel | 🛢 SQL | 🧠 Basic Maths 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3IwVWGE You can learn & practice them 100% FREE✅️

Hi guys, Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch. For those of you who are new to this channel, here are some quick links to navigate this channel easily. Data Analyst Learning Plan 👇 https://t.me/sqlspecialist/752 Python Learning Plan 👇 https://t.me/sqlspecialist/749 Power BI Learning Plan 👇 https://t.me/sqlspecialist/745 SQL Learning Plan 👇 https://t.me/sqlspecialist/738 SQL Learning Series 👇 https://t.me/sqlspecialist/567 Excel Learning Series 👇 https://t.me/sqlspecialist/664 Power BI Learning Series 👇 https://t.me/sqlspecialist/768 Python Learning Series 👇 https://t.me/sqlspecialist/615 Tableau Essential Topics 👇 https://t.me/sqlspecialist/667 Best Data Analytics Resources 👇 https://heylink.me/DataAnalytics You can find more resources on Medium & Linkedin Like for more ❤️ Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing. Hope it helps :)

𝗖𝗿𝗮𝗰𝗸 𝗙𝗔𝗔𝗡𝗚 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍 If you’re serious about cracking top tech interviews — from FAANG to startups — this is the roadmap you can’t afford to miss🎊 Thousands have used it to land roles at Google, Amazon, Microsoft, and more — completely free🤩📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3TJlpyW Your dream job might just start here.✅️

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.

𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍 Want to break into data science in 2025—without spending a single rupee?💰👨‍💻 You’re in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analytics—for free🤩✔️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/42vCIrb Level up your career in the booming field of data✅️

Data Science Cheat sheet 2.0 A helpful 5-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between. It covers over a semester of introductory machine learning, and is based on MIT's Machine Learning courses 6.867 and 15.072. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this resource helpful as well. Creator: Aaron Wang Stars ⭐️: 4.5k Forked By: 645 https://github.com/aaronwangy/Data-Science-Cheatsheet #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖