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How to master Python from scratch🚀 1. Setup and Basics 🏁    - Install Python 🖥️: Download Python and set it up.    - Hello, World! 🌍: Write your first Hello World program. 2. Basic Syntax 📜    - Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.    - Control Structures 🔄: Understand if-else statements, for loops, and while loops.    - Functions 🛠️: Write reusable blocks of code. 3. Data Structures 📂    - Lists 📋: Manage collections of items.    - Dictionaries 📖: Store key-value pairs.    - Tuples 📦: Work with immutable sequences.    - Sets 🔢: Handle collections of unique items. 4. Modules and Packages 📦    - Standard Library 📚: Explore built-in modules.    - Third-Party Packages 🌐: Install and use packages with pip. 5. File Handling 📁    - Read and Write Files 📝    - CSV and JSON 📑 6. Object-Oriented Programming 🧩    - Classes and Objects 🏛️    - Inheritance and Polymorphism 👨‍👩‍👧 7. Web Development 🌐    - Flask 🍼: Start with a micro web framework.    - Django 🦄: Dive into a full-fledged web framework. 8. Data Science and Machine Learning 🧠    - NumPy 📊: Numerical operations.    - Pandas 🐼: Data manipulation and analysis.    - Matplotlib 📈 and Seaborn 📊: Data visualization.    - Scikit-learn 🤖: Machine learning. 9. Automation and Scripting 🤖    - Automate Tasks 🛠️: Use Python to automate repetitive tasks.    - APIs 🌐: Interact with web services. 10. Testing and Debugging 🐞     - Unit Testing 🧪: Write tests for your code.     - Debugging 🔍: Learn to debug efficiently. 11. Advanced Topics 🚀     - Concurrency and Parallelism 🕒     - Decorators 🌀 and Generators ⚙️     - Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy. 12. Practice Projects 💡     - Calculator 🧮     - To-Do List App 📋     - Weather App ☀️     - Personal Blog 📝 13. Community and Collaboration 🤝     - Contribute to Open Source 🌍     - Join Coding Communities 💬     - Participate in Hackathons 🏆 14. Keep Learning and Improving 📈     - Read Books 📖: Like "Automate the Boring Stuff with Python".     - Watch Tutorials 🎥: Follow video courses and tutorials.     - Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars. 15. Teach and Share Knowledge 📢     - Write Blogs ✍️     - Create Video Tutorials 📹     - Mentor Others 👨‍🏫 I have curated the best interview resources to crack Python Interviews 👇👇 https://topmate.io/coding/898340 Hope you'll like it Like this post if you need more resources like this 👍❤️

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An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science. Basically, there are 3 different layers in a neural network : Input Layer (All the inputs are fed in the model through this layer) Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers) Output Layer (The data after processing is made available at the output layer) Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

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Breaking into Data Science doesn’t need to be complicated. If you’re just starting out, Here’s how to simplify your approach: Avoid: 🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once. 🚫 Spending months on theoretical concepts without hands-on practice. 🚫 Overloading your resume with keywords instead of impactful projects. 🚫 Believing you need a Ph.D. to break into the field. Instead: ✅ Start with Python or R—focus on mastering one language first. ✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter. ✅ Dive into a simple machine learning model (like linear regression) to understand the basics. ✅ Solve real-world problems with open datasets and share them in a portfolio. ✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests. Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Like if you need similar content 😄👍 Hope this helps you 😊 #ai #datascience

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Data-Driven Decision Making Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation. 1️⃣ A/B Testing & Hypothesis Testing A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better. ✔ Key Metrics in A/B Testing: Conversion Rate Click-Through Rate (CTR) Revenue per User ✔ Steps in A/B Testing: 1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks"). 2. Split users into Group A (control) and Group B (test). 3. Analyze differences using statistical tests. ✔ SQL for A/B Testing: Calculate average purchase per user in two test groups

    
        SELECT test_group, AVG(purchase_amount) AS avg_purchase  
FROM ab_test_results  
GROUP BY test_group;{}
    

Run a t-test to check statistical significance (Python)

    
        from scipy.stats import ttest_ind
t_stat, p_value = ttest_ind(group_A['conversion_rate'], group_B['conversion_rate'])
print(f"T-statistic: {t_stat}, P-value: {p_value}"){}
    

🔹 P-value < 0.05 → Statistically significant difference. 🔹 P-value > 0.05 → No strong evidence of difference. 2️⃣ Forecasting & Trend Analysis Forecasting predicts future trends based on historical data. ✔ Time Series Analysis Techniques: Moving Averages (smooth trends) Exponential Smoothing (weights recent data more) ARIMA Models (AutoRegressive Integrated Moving Average) ✔ SQL for Moving Averages: 7-day moving average of sales

    
        SELECT order_date,  
       sales,  
       AVG(sales) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg  
FROM sales_data;{}
    

✔ Python for Forecasting (Using Prophet)

    
        from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
model.plot(forecast){}
    

3️⃣ KPI & Metrics Analysis KPIs (Key Performance Indicators) measure business performance. ✔ Common Business KPIs: Revenue Growth Rate → (Current Revenue - Previous Revenue) / Previous Revenue Customer Retention Rate → Customers at End / Customers at Start Churn Rate → % of customers lost over time Net Promoter Score (NPS) → Measures customer satisfaction ✔ SQL for KPI Analysis: Calculate Monthly Revenue Growth

    
        SELECT month,  
       revenue,  
       LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,  
       (revenue - prev_month_revenue) / prev_month_revenue * 100 AS growth_rate  
FROM revenue_data;{}
    

✔ Python for KPI Dashboard (Using Matplotlib)

    
        import matplotlib.pyplot as plt
plt.plot(df['month'], df['revenue_growth'], marker='o')
plt.title('Monthly Revenue Growth')
plt.xlabel('Month')
plt.ylabel('Growth Rate (%)')
plt.show(){}
    

4️⃣ Real-Life Use Cases of Data-Driven Decisions 📌 E-commerce: Optimize pricing based on customer demand trends. 📌 Finance: Predict stock prices using time series forecasting. 📌 Marketing: Improve email campaign conversion rates with A/B testing. 📌 Healthcare: Identify disease patterns using predictive analytics. Mini Task for You: Write an SQL query to calculate the customer churn rate for a subscription-based company. Data Analyst Roadmap: 👇 https://t.me/sqlspecialist/1159 Like this post if you want me to continue covering all the topics! ❤️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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