Real world data science projects to become data scientist

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Free Download Real world data science projects to become data scientist
Published 7/2024
Created by Vinay Karode
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 32 Lectures ( 5h 41m ) | Size: 5.43 GB

Data science portfolio: Churn Prediction, Insurance Pricing, Sales Forecasting with Logistic Regression & XGBoost
What you'll learn:
Predict customer churn using logistic regression and decision trees.
Understand and apply ensemble learning techniques for better prediction accuracy.
Develop insurance pricing models using XGBoost and evaluate their performance.
Forecast sales for large retail stores with real-world datasets and advanced techniques.
Gain practical experience with real-world data and industry-standard tools.
Learn to interpret model results and make data-driven business decisions.
Enhance your data visualization skills to communicate insights effectively.
Requirements:
Basic understanding of Python programming.
Familiarity with fundamental statistics and probability concepts.
Experience with basic data manipulation and visualization libraries like pandas and matplotlib.
Basic knowledge of machine learning concepts is a plus, but not required.
Description:
Are you ready to transform your data science skills and tackle real-world challenges? Welcome to "Real World Data Science Projects to Become Data Scientist," a hands-on course designed to equip you with the knowledge and practical experience needed to excel in the field of data science.In this course, you'll dive deep into five comprehensive projects, each focusing on a crucial aspect of data science:Churn Prediction Using Logistic Regression and Decision Trees: Learn to predict customer churn by implementing logistic regression and decision tree models. Understand key concepts like the confusion matrix, ROC-AUC, and the importance of evaluating model performance.Ensemble Learning for Churn Prediction: Discover the power of ensemble learning techniques. Explore bagging, boosting, Random Forest, AdaBoost, and gradient boosting. Gain hands-on experience with model interpretation using LIME.Insurance Price Prediction Using XGBoost: Develop and evaluate insurance pricing models. Conduct exploratory data analysis, understand correlations, and build robust models using XGBoost. Learn to interpret the results to make data-driven business decisions.Bigmart Sales Prediction: Forecast sales for large retail stores using advanced techniques. Gain insights from real-world datasets and apply machine learning models to predict future sales accurately.Throughout the course, you'll work with real datasets and industry-standard tools, enhancing your practical skills. You'll also learn to visualize data, interpret model results, and communicate insights effectively.This course is perfect for aspiring data scientists, current professionals looking to upgrade their skills, and anyone interested in building a strong portfolio of data science projects. Basic knowledge of Python and familiarity with fundamental statistics are recommended.Enroll now and take the first step towards mastering data science with real-world projects!
Who this course is for:
Aspiring data scientists looking to gain hands-on experience with real-world projects.
Current data professionals aiming to enhance their predictive modeling skills.
Students and learners who want to build a strong portfolio of data science projects.
Data enthusiasts who want to transition into a data science career.
Business analysts seeking to incorporate data science techniques into their work.
Students in computer science or related fields looking to enhance their skill set.
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