Nine step guide for handling the data for Data Analysts and Data Scientists

In this live session, we’ll walk through a nine-step guide designed to simplify the process of data handling for both data analysts and data scientists. Whether you're new to data or looking to refine your workflow, this comprehensive guide will cover everything from data collection to data cleaning and preprocessing.

📌 What You’ll Learn:
Data Collection: Best practices for gathering relevant data.
Data Cleaning: Removing inconsistencies, dealing with missing values, and handling duplicates.
Data Transformation: Techniques for reshaping and converting data types.
Exploratory Data Analysis (EDA): Uncovering patterns and trends.
Data Visualization: Creating clear visuals for deeper insights.
Feature Engineering: Extracting key features for model building.
Data Normalization and Scaling: Preparing data for analysis.
Data Splitting: Creating training and testing datasets.
Final Preprocessing Tips: Ensuring your data is ready for modeling.
This live stream is packed with practical tips and examples to enhance your data handling skills, making it a must-watch for aspiring data professionals!

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Keywords/Tags:
DataHandling, DataCleaning, DataTransformation, EDA, DataVisualization, DataScience, DataAnalysis, FeatureEngineering, PythonData, UrduAI, BabaAmmar, Codanics, AammarTufail, Pakistan, DataScienceGuide, DataPreparation

[AKAK-nngy]

The way you are teaching the course, i have a feeling anyone can become a data analyst in no time.

[anandshaw-ieqk]

Sir RMSE MSE ye classification metrics ke upar ek video create kariye please 🥺

[abdullahjamal-oefy]

Very nice information, Sir, The summary is very keen information. 💚💖🧡💘❤❤‍🩹