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Step19..continue …towards Data Science and ML model creation ****** This is how to visualization of data from scratch to master**** How to start write code 1. Please follow my steps, which is very helpful when we start creating ML Model. 2. Guys use google colab for practices. Problem --: How to show data in a graphical mode.. Solution --: matplotlib is solution in python # Data visualization # This is simple line visualization with help of matplotlib # import required libraries import numpy as np import matplotlib.pyplot as plt # Create data x = np.arange(1,10,2) # We create a data point between 1-10 with 2 difference y = 3*x + 2 # create y axis plt.plot(x,y) # plot x and y axis plt.show()

Data Literacy and Analytics Headline: 📈 Turn Metrics into Money: The Power of Data Literacy It’s not enough to say "response time is 2 seconds." The future tester must link performance data to business outcomes. Can you prove that 0.5 seconds faster load time increases conversion by 3%? This segment discusses using data analytics (think Python/Pandas) to transform raw metrics into compelling business insights for stakeholders. Learn to speak the language of business: https://lnkd.in/emqWW6FJ Don't forget to like and subscribe to the Little's Law YouTube channel! #DataAnalytics #BusinessIntelligence #Python #Metrics #PerformanceEngineering #CareerGrowth Subscribe to Littles Law Youtube Channel : https://lnkd.in/e5ytMitw

🚀✅ DAY-7 of My Data Analytics Learning Journey – Exploring All Charts in Matplotlib! Today, I explored different types of charts in Matplotlib and learned how each one helps in visualizing data effectively. 🔹 Line Chart – Used to show trends or changes over time. 🔹 Bar Chart – Best for comparing categories or groups. 🔹 Histogram – Helps visualize the distribution of numerical data. 🔹 Pie Chart – Represents proportions and percentage distribution. 🔹 Scatter Plot – Displays relationships and correlations between two variables. 🔹 Box Plot – Useful for detecting outliers and data spread. 🔹 Area Chart – Highlights cumulative totals over time. 🔹 Stacked Bar/Area Charts – Compare parts within a whole over categories. Matplotlib makes data visualization easier, allowing us to understand complex data in a visual and insightful way. #Matplotlib #DataAnalytics #Python #DataVisualization #LearningJourney #DataScience #AnalyticsWithPython

💻 Loading Datasets in Jupyter Notebook — Two Simple Methods Working on my latest data project, I explored two quick ways to load a dataset into Jupyter Notebook 👇 📍 Method 1 – Uploading the dataset directly Using the upload option in Jupyter, I imported my CSV file into the working directory, so it stays accessible even after I reopen the notebook. 📍 Method 2 – Using the file path By copying the complete file location from my system and pasting it inside pd.read_csv("file_path"), I connected the dataset directly through its stored path. Both methods work perfectly — one keeps the data inside your workspace, while the other links it from your local system. This is a small but essential step every data analyst or ML enthusiast must master before diving into exploration, cleaning, or visualization 🧠📊 #JupyterNotebook #Python #DataScience #LearningByDoing #Pandas #DataAnalytics #WomenInTech 

🚀 Power BI + Python = Used Bike Market Solved. Dropping my latest project! I harnessed Python for the heavy-lifting data crunching ⚙️ and brought the insights to life in a stunning Power BI dashboard. The Data Story: 💰 $106K Avg. Price — What’s driving the cost? 🥇 First Owners dominate the supply. 📈 Clear Demand for post-2000 models. This demonstrates how data engineering and visualization can transform complex data sets into actionable business intelligence. Happy to discuss the data modeling approach! #PowerBI #Python #DataVisualization #DataAnalytics #BusinessIntelligence #DataScience #DashboardDesign

Day 8 of My Python for Data & Business Analytics Series Question: How do you filter data based on conditions? Answer: Use Pandas’ conditional filtering — simple and fast. Example: df[df["Sales"] > 500] gives only high-sales records. Pro Tip: You can chain multiple conditions using & (AND) or | (OR). #DataFiltering #PythonTips #DataAnalytics #FenilPatel #DailyLearning

I’ve recently completed the Introduction to Data Visualization with Matplotlib course by DataCamp — a hands-on journey into turning data into clear, meaningful visuals using Python’s most popular plotting library. Here’s what I learned and practiced throughout the course: 1. Creating Visuals with the Object-Oriented Interface I learned how to use fig and ax objects to build customized visualizations — from simple line plots to multi-panel layouts using plt.subplots(). 2. Customizing Plots for Clarity By adjusting colors, markers, line styles, axis labels, and titles, I discovered how small design choices can make data more engaging and easier to interpret. 3. Working with Time-Series Data Using pandas and Matplotlib together, I visualized climate data over time, learned how to handle DateTimeIndex, and used twin axes to display multiple variables (like CO₂ levels and temperature) on the same plot. 3. Annotating and Highlighting Key Insights I explored how to add annotations and arrows to focus attention on important trends or events — making visual storytelling more impactful. 5. Exploring Quantitative Comparisons From bar charts, histograms, and boxplots to scatter plots, I practiced visualizing comparisons, distributions, and relationships — even adding error bars to communicate variability effectively. #DataVisualization #Python #Matplotlib #DataAnalysis #LearningJourney #DataCamp #ContinuousLearning

🎨 Visualizing Overlapping Data with Transparency in Matplotlib When comparing multiple datasets, clarity is just as important as color. In this example, I used the alpha parameter in Matplotlib to make overlapping bars semi-transparent — allowing both datasets to remain visible and easy to compare. In this chart, I compared 2023 vs 2024 sales using overlapping bar plots. By adding alpha=0.5, both datasets remain visible — giving a clear, layered comparison instead of a cluttered one. In this example 👇 🔹 The blue bars represent 2023 data. 🔹 The red bars represent 2024 data. 🔹 By setting alpha=0.5, both datasets remain visible — creating a clear, balanced comparison. 💡 Takeaway Great data visualization isn’t just about colour — it’s about clarity and communication. 📢 #Python #DataVisualization #Matplotlib #DataScience #Analytics #MachineLearning #CodingTips #VisualizationDesign

Working with Pandas DataFrames — Simplifying Data Manipulation Now that we know what DataFrames are, let’s dive into how to work with them efficiently! With Pandas, you can easily: ✅ Select specific rows and columns ✅ Filter data based on conditions ✅ Sort and summarize data ✅ Handle missing values with ease These operations turn raw datasets into clean, structured, and meaningful insights — a must-have skill for every data analyst! 📊 #Python #Pandas #DataAnalytics #LearningJourney #PythonForData

Day 10 of My Python for Data & Business Analytics Series Question: How do you merge or join two datasets? ✅ Answer: Use pd.merge() — it works just like SQL joins (inner, left, right, outer). Pro Tip: Always verify column names before merging — mismatched keys cause silent errors. #DataMerging #PythonForData #Analytics #BusinessAnalytics #FenilPatel #DailyLearning