• views: 296
• date: 4 October 2026
• posted by: 0nelove

Data Science for Beginners: From Data to Insights
Published 10/2026
Created by Arjun Vaid, School of AI, MEET SHAH
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 65 Lectures ( 3h 0m ) | Size: 334.4 MB

Learn Python, Pandas, data analysis, visualization, and machine learning through hands-on labs and a capstone project

What you'll learn
⚡ Use Python and Pandas to load, explore, and clean datasets, handling missing values, duplicates, inconsistent formats, and outliers.
⚡ Apply exploratory analysis and practical statistics to identify patterns, compare groups, and test business claims.
⚡ Build and evaluate regression and classification models, compare performance, and recognize overfitting, data leakage, and bias.
⚡ Complete a data science capstone and communicate findings through clear visualizations and actionable business recommendations.

Requirements
❗ No prior experience in data science or machine learning is required; Python fundamentals are covered in the course.
❗ Basic computer skills and familiarity with arithmetic, averages, and percentages.
❗ A computer with internet access and the ability to run Python notebooks.
❗ Willingness to practice with datasets and complete hands-on labs, coding exercises, and a capstone project.

Description
This course contains the use of artificial intelligence.

Turn raw data into useful insights with a practical, beginner-friendly introduction to data science.

In Data Science for Beginners: From Data to Insights, you will work through the complete data science workflow: defining a problem, preparing data, exploring patterns, building predictive models, and communicating results.

Start with Python fundamentals, then use Pandas to load, filter, and summarize datasets. Learn to handle missing values, duplicates, inconsistent formats, and outliers so your data is ready for analysis.

Explore distributions, compare groups, and investigate relationships between variables. Apply practical statistics to test business claims, and create clear visualizations that turn findings into actionable recommendations.

Next, build regression and classification models. Evaluate predictions using appropriate metrics, compare models against baselines, and recognize common problems such as overfitting and data leakage. You will also examine bias, privacy, fairness, and the importance of documenting limitations.

Hands-on labs, coding exercises, quizzes, practice tests, and presentation role plays help you apply what you learn. Finish with a capstone project that brings together problem framing, data preparation, analysis, modeling, and a final recommendation.

This course is designed for beginners, students, career changers, and business professionals who want practical data science skills. It also suits aspiring data analysts who want experience completing a project from data to insights.

Who this course is for
⭐ Beginners who want a practical introduction to data science, from preparing data to building models and presenting insights.
⭐ Students and career changers looking to develop Python, Pandas, data analysis, and machine learning skills through hands-on practice.
⭐ Business professionals who want to turn business questions into clear analysis and actionable recommendations.
⭐ Aspiring data analysts and junior data scientists who want to apply their skills in a complete capstone project.



   
   
   

Forex Trading

Crypto Trading. 20 USD Bonus after KYC

We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm

0 1 2 3 4 5

| Print version |

	Information
		Users of GUESTS are not allowed to comment this publication.