Udemy – Statistics for Data Science Solve Real World Data Problems

Level: All | Genre: eLearning | Language: English | Duration: 21 Lectures ( 3h 59m ) | Size: 1.13 GB
Learn The Statistics For Data Science & Build Practical Real-World Case Studies.Visualize, analyze & predict like Apro.

What you'll learn

Understand different types of dаta: categorical, numerical, and how to measure them.
Master data visualization techniques (bar charts, line charts, pie charts, histograms, box plots, and more).
Grasp key statistical measures such as mean, median, mode, variance, and standard deviation.
Analyze central tendency, spread, and outliers to summarize and interpret data effectively.
Measure relationships using correlations, covariance, and heatmaps.
Dive deep into hypothesis testing with real-world applications of t-tests, chi-square tests, and more.
Learn to implement linear regression models and interpret p-values, coefficients, and R-squared values.
Use Statsmodels, a powerful Python library, to perform statistical analysis and modeling in your projects.

Requirements

No prior statistics knowledge required — everything is taught from the ground up.
Basic understanding of Python (lists, functions, loops) is helpful.
A willingness to learn through hands-on examples and real datasets.
Access to a computer with internet connection (you'll install Jupyter Notebook, use Google Colab) or VScode.

Description

If you're stepping into the world of data science or analytics, mastering statistics isn't optional—it's essential. But learning stats doesn't have to be dry or overly academic. This course takes a fresh, practical approach to statistics by using real-world case studies, powerful data visualizations, and step-by-step Python examples to make every concept click.Welcome to Statistics for Data Science: Solve Real Problems with Real Data—a course that transforms abstract statistical ideas into tangible skills you can use in data science, business intelligence, machine learning, and research.This isn't just theory. You'll apply statistical techniques using real datasets and Python libraries like Statsmodels, gaining hands-on experience from the start. Whether it's understanding data distributions, comparing groups, measuring relationships, or building regression models—you'll learn by doing.Are you pursuing a career in data science, analytics, or machine learning? Struggling to understand core statistical concepts or how to apply them in real-world datasets? This comprehensive course, Master Statistics For Data Science: Statistics Case Studies, is your complete guide to mastering the statistics you need for modern data science roles.Whether you're a beginner or looking to strengthen your statistical foundation, this hands-on, example-driven course takes you through real-world case studies and practical examples that make learning statistics both engaging and effective. No more dry theory — just the essential concepts, applied directly to real data.

What you'll learn

:Understand different types of dаta: categorical, numerical, and how to measure them.Master data visualization techniques (bar charts, line charts, pie charts, histograms, box plots, and more).Grasp key statistical measures such as mean, median, mode, variance, and standard deviation.Analyze central tendency, spread, and outliers to summarize and interpret data effectively.Measure relationships using correlations, covariance, and heatmaps.Dive deep into hypothesis testing with real-world applications of t-tests, chi-square tests, and moreLearn to implement linear regression models and interpret p-values, coefficients, and R-squared values.Use Statsmodels, a powerful Python library, to perform statistical analysis and modeling in your projects.Why Take This Course?Statistics is the foundation of data science. Without a clear understanding of statistical principles, you'll find it hard to trust, interpret, or communicate your results. This course gives you everything you need:Clear explanations of key concepts.Step-by-step tutorials using Python and Statsmodels.Case-study-driven learning – Learn how to analyze real data.Downloadable resources, quizzes, and summaries to reinforce your learning.Designed for self-paced study with short, engaging lectures.Course Content Includes:Introduction to Statistics for Data ScienceWhat is data? Understanding graphs and charts.Role of statistics in data science.Data Visualization Techniques

Overview

of visualization tools.How to use bar, line, pie, histogram, and box plots.Analyzing data distribution effectively.Data Analysis EssentialsMeasures of central tendency (mean, median, mode).Spread of data (range, IQR, standard deviation).Detecting and analyzing outliers.Understanding Data RelationshipsCorrelations and heatmaps.Computing covariance and interpreting variability.Hypothesis Testing and Statistical InferenceKey concepts: null vs. alternative hypothesis.One-sample, two-sample t-tests, paired t-tests.Chi-square test for independence.Statistical significance and p-values explained simply.Regression Analysis with StatsmodelsBuilding linear regression models.Understanding coefficients, residuals, R-squared.Applying regression for prediction and insights.Here's what you'll explore:Visual storytelling with data – Learn how to create and interpret bar charts, line graphs, pie charts, histograms, box plots, and distribution plots that communicate insights clearly and powerfully.Summarizing data with statistics – Dive into measures of central tendency and spread, uncover outliers, and describe datasets in meaningful ways.Finding relationships in data – Use heatmaps and correlation matrices to discover how variables interact. Understand covariance and the math behind relationships.Statistical testing that matters – Go beyond theory with hypothesis testing: one-sample, two-sample, paired t-tests, and chi-square tests. Learn how to draw reliable conclusions from your data.Predictive modeling with regression – Build and evaluate linear regression models using Statsmodels. Understand coefficients, p-values, residuals, and how to assess a model's accuracy with R-squared.Every section is carefully designed to help you build real-world data instincts. With short, focused video lessons, visual guides, and coding exercises, you'll move from learning to applying—fast.And yes, everything is taught from the ground up—you don't need to be a math genius or a stats professor. If you can write basic Python and are curious about how data works, you're ready to go.What makes this course stand out?It's hands-on and practical: You're not just watching lectures—you're coding, visualizing, and testing data.It's tailored for data science applications: All examples are taken from real-world problems and data science workflows.It's fast-paced but beginner-friendly: Concepts are broken down into bite-sized lessons you can easily digest and revisit anytime.By the end of this course, you'll confidently:Analyze any dataset with statistical tool. Visualize data to discover patterns and trends. Make data-driven decisions using hypothesis tests.Predict outcomes with regression models.Build a strong foundation for advanced machine learning.If you want to become the kind of data professional who not only works with data but understands it deeply—this is the course for you.Join today and start building the statistical mindset every data scientist needs.Let me know if you'd like me to also generate:A compelling promo video script.High-converting ad copy for Facebook, Instagram, or LinkedIn.A version tailored for Kaggle users or bootcamp students.I'm here to help make this course a success!

Who this course is for

Aspiring data scientists who want to build a strong statistical foundation.
Business analysts and professionals looking to level up their data interpretation skills.
Students and graduates from non-technical backgrounds entering data-focused roles.
Programmers and developers transitioning into data science or analytics.
Anyone preparing for data science interviews or technical case studies.
Homepage:

https://www.udemy.com/course/statistics-for-data-science-solve-real-world-data-problems/

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