Time Series Data Analysis with Python Day 43

𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐒𝐤𝐥𝐞𝐚𝐫𝐧 𝐃𝐚𝐲 48: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 This session focused on preparing data for supervised machine learning by encoding the target variable into numeric form, separating features and labels, splitting the dataset into training and test sets to evaluate model generalization, and standardizing features using StandardScaler to ensure consistent scaling and improved model performance. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #SQL #Learning #ostinatorigore

#Day6 of Data Science with Harry Completed the second project: #CodersOfBangalore. In this project, I worked on processing raw Instagram-style data using pure Python and built a small data pipeline to convert unstructured text into structured data for analysis. Work completed: - Data collected - Data parsed - Raw data converted into JSON format - Structured data stored for further analysis This project focused on handling real-world messy data and transforming it into usable structured data without using external libraries like pandas or NumPy. GitHub Repository: https://lnkd.in/g8byfi4A #DataScience #Python #DataEngineering #LearningInPublic #100DaysOfCode

Mastering PySpark isn't just about syntax—it's about understanding data layout, partitioning, and the execution plan. I’ve put together a cheat sheet covering the 5 Advanced PySpark Techniques that separate junior scripts from production-grade pipelines. From solving the "Small File Problem" to handling data skew with salting, these are the patterns that actually scale. Save this for your next architectural review or technical interview! 💡 #DataEngineering #PySpark #BigData #ApacheSpark #Python #DataScience #CloudComputing #Explain

📊 Day 19 — 60 Days Data Analytics Challenge Today I learned about Crosstab in Pandas, which helps summarize data by showing the relationship between two categorical variables. 🔍 What I practiced today: • Creating cross-tabulations using pd.crosstab() • Understanding category-wise data distribution • Using margins=True to include total values • Improving table readability with row and column labels This feature is very helpful during Exploratory Data Analysis (EDA) because it allows us to quickly compare categories and identify patterns in the dataset. #DataAnalytics #Python #Pandas #60DaysChallenge #LearningJourney

#Day_60 of Data Analytics learning journey with Skill Shikshya. Today I focused on understanding data types in Python, which are one of the most fundamental concepts for working with data. I explored different types such as lists, tuples, dictionaries, and sets, and learned how each of them is used to store and manage data efficiently. Understanding data types is important because it helps in writing cleaner code, performing accurate data operations, and avoiding common errors during analysis. I also practiced identifying data types and converting them when necessary while working with small examples. Every small concept builds a stronger foundation for working with real-world datasets. Looking forward to applying these concepts more in upcoming data analysis tasks. #100daysoflearning #DataAnalytics #Learningjourney

🚀#120DaysChallenge of Python Full Stack Journey Hello everyone, I’m Lakshmi Sravani 😊 #120DaysChallenge #42Day -  Exploratory Data Analysis using the Titanic Dataset Today I practiced Exploratory Data Analysis (EDA) using the Titanic dataset in Jupyter Notebook. This exercise helped me understand how data can be explored, cleaned, and visualized to identify patterns and insights. Dataset Source: Kaggle – Titanic Dataset • Imported required libraries: Pandas and Matplotlib • Loaded the dataset using read_csv() • Explored the dataset structure using columns, shape, and info() • Checked missing values using isnull().sum() • Performed basic data cleaning by dropping unnecessary columns such as PassengerId, Name, Ticket, etc. • Visualized different features to understand patterns in the dataset 📊 Visualizations Performed: • Bar chart showing Survival Count • Pie chart representing Passenger Class Distribution • Bar chart showing Gender Distribution • Histogram showing Age Distribution • Histogram for Cabin Information • Pie chart for Embarked Port Distribution #DataAnalysis #Kaggle #TitanicDataset #Pandas #Matplotlib #JupyterNotebook #DataScience #LearningJourney #Python #PythonFullStack #120DaysChallenge #LearningJourney #Programming #FreshGraduate #CareerDevelopment #WomenInTech #Codegnan

📊 Day 24 — 60 Days Data Analytics Challenge | Pandas Indexing & Data Selection Today I practiced important Pandas concepts for structuring and accessing data in a DataFrame. 🔎 What I practiced: • Using set_index() to convert a column into the DataFrame index • Using reset_index() to convert the index back to a column • Accessing data using loc[] (label-based selection) • Accessing data using iloc[] (position-based selection) 💡 Key Learning: Understanding indexing and data selection techniques helps in navigating and analyzing datasets more efficiently. #60DaysDataAnalyticsChallenge #Python #Pandas #DataAnalytics #LearningInPublic

The "Big 5" of Python for Data Science 🐍 If you are just starting in Data Science, the sheer number of libraries can feel overwhelming. But if you master these five, you can handle 90% of most data projects. Pandas: Your go-to for data cleaning and exploration. NumPy: The powerhouse for numerical operations. Matplotlib: Great for basic, customizable plotting. Seaborn: Elevates your visuals for statistical analysis. Scikit-learn: The gold standard for implementing Machine Learning. Mastering the tools is the first step toward solving real-world business problems with data. Which of these do you use most in your daily workflow? Let’s discuss below! 👇 #DataScience #Python #DataAnalytics #MachineLearning #TechTips #GradeLearner

🚀 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝘁𝗼𝗼𝗹 𝘁𝗼 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻. Validating data during migrations can be time-consuming and error-prone. So I built a small application that: • compares datasets automatically • detects column-level mismatches • generates validation insights The tool is built with Python, Pandas, and Streamlit. 🎥 Quick demo below. 🔗 GitHub repository: https://lnkd.in/d5g8ESvx Feedback and suggestions are welcome. #Python #DataAnalytics #DataEngineering #Automation #OpenSource #GitHub #DataValidation #Fabric #DataBricks #DataAnalysis #DataScience

🚨 From Python Lists to Lightning-Fast Arrays ⚡ Just completed NumPy in my Data Science Bootcamp — and WOW. I finally understand why NumPy is called the backbone of Data Science. Here’s what leveled up my skills 👇 ✅ ndarray vs Python lists (Speed difference is insane 🔥) ✅ Indexing, slicing & reshaping like a pro ✅ Broadcasting (this felt like magic) ✅ Vectorized operations (No more slow loops!) ✅ Built-in statistical & mathematical functions Big realization: Performance + Clean Code = Real Data Science This is just the foundation… but foundations matter 🧱 Next stop → Turning raw data into insights 📊 If you're learning Data Science too, what are you currently working on? 👇 #DataScience #Python #NumPy #CodingJourney #LearnInPublic #DataAnalytics #100DaysOfCode #MonalS #KrishNaik