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๐ *How to Start Learning Data Science* ๐ก Data Science may sound heavy, but hereโs a simple roadmap to begin your journey โ step by step: *1. Understand What Data Science Is* โข Itโs the art of extracting insights from data โข Combines: Statistics, Programming, Business Knowledge โข Common roles: Data Analyst, Data Scientist, ML Engineer *2. Learn Python or R* ๐ โข Python is preferred for beginners โข Focus on: variables, loops, functions, libraries like Pandas & NumPy *3. Get Strong with Math & Stats* ๐ง โข Focus on: โ Mean, Median, Mode, Standard Deviation โ Probability, Hypothesis Testing โ Linear Algebra & Calculus basics *4. Master Data Analysis & Visualization* ๐ โข Libraries: Pandas, Matplotlib, Seaborn โข Tools: Excel, Tableau, Power BI โข Learn to clean, explore & visualize datasets *5. Learn SQL for Data Queries* ๐๏ธ โข SELECT, WHERE, GROUP BY, JOIN โข Work with real datasets to extract insights *6. Explore Machine Learning* ๐ค โข Understand: Supervised vs. Unsupervised learning โข Tools: Scikit-Learn, Jupyter Notebooks โข Start with basic models: Linear Regression, KNN, Decision Trees *7. Build Real Projects* ๐ผ โข Sales dashboard โข Customer churn analysis โข Movie recommendations โข Host projects on GitHub & share on LinkedIn *8. Learn Cloud & Big Data (Optional but Useful)* โ๏ธ โข Tools: AWS, GCP, Hadoop, Spark โข Helps scale your work with larger datasets *9. Stay Updated & Practice Daily* ๐ โข Kaggle, DataCamp, Medium blogs โข Follow Data Science creators on X, LinkedIn ๐ฅ *You donโt need to learn everything at once โ just keep moving forward consistently.* ๐ฌ *Double Tap โค๏ธ for more*

Uber hiring Data Scientist Apply link: https://www.uber.com/global/en/careers/list/141654 ๐ WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 ๐ Telegram Channel: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best! ๐๐

Now, let's move to the next topic in the data science learning series *Data Cleaning & Preparation:* ๐น *Topic: Combining Datasets* In real-world projects, data often comes from multiple sources: โ Sales from one CSV โ Customer info from another โ Product data from a third To build a full picture, you need to combine them. โ *3 Ways to Combine Datasets in Pandas* *1. Concatenation (Stacking Data)* Used when datasets have same columns but are split across multiple files or timeframes. df_combined = pd.concat([df1, df2]) Use axis=0 for stacking rows (default) Use axis=1 for combining side by side (columns) *2. Merging (Joining on Keys)* Used when datasets share a common key/column, like customer_id. df_merged = pd.merge(df1, df2, on='customer_id', how='inner') *Merge types:* inner: only matching rows left: keep all rows from df1 right: keep all rows from df2 outer: keep all rows from both *3. Join Method (simplified merge)* df1.join(df2, how='left') Only works when indexes are aligned or you set the index before. ๐ *Real-Life Example:* You have: orders.csv โ order_id, product_id, customer_id customers.csv โ customer_id, name, age products.csv โ product_id, name, price Youโll use merge to link them all using common columns. *React with โค๏ธ once you're ready for the quiz* Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998 Python Cheatsheet: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1660

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Weโre Hiring: Machine Learning Engineer (Computer Vision) ๐ Experience: 2โ3 Years ๐ Location: Sector-44 Gurgaon Are you passionate about building next-gen Computer Vision systems? We're looking for a skilled Machine Learning Engineer to join our team and help shape AI-powered solutions that make a real impact! ๐ What weโre looking for: โ Proven experience developing Computer Vision systems โ Strong grip on ML frameworks โ PyTorch / TensorFlow โ Solid understanding of Convolutions, ResNet, EfficientNet, Vision Transformers โ Hands-on experience in the ML lifecycle โ from data annotation to model deployment & monitoring โ Proficiency in Python, with OOP experience โ Familiarity with tools like Git, GitHub, and packages like OpenCV, PIL, NumPy, Pandas, Albumentations โ Strong communication skills and a collaborative mindset ๐ฉ Interested or know someone who fits the role? Drop us a message or apply at: [email protected]

Who is Data Scientist? He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market. A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist: Determines correct datasets and variables. Identifies the most challenging data-analytics problems. Collects large sets of data- structured and unstructured, from different sources. Cleans and validates data ensuring accuracy, completeness, and uniformity. Builds and applies models and algorithms to mine stores of big data. Analyzes data to recognize patterns and trends. Interprets data to find solutions. Communicates findings to stakeholders using tools like visualization.

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