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Statistics for Data Science

Course Description

Description

This self-paced, high-impact course is designed to help learners build a deep understanding of core statistical concepts that are essential in the field of data science. From descriptive statistics to hypothesis testing and regression analysis, this course combines theory with hands-on practice to develop critical thinking and data interpretation skills.

Built for aspiring data analysts, data scientists, and professionals in decision-making roles, the curriculum introduces practical statistical techniques and their real-world applications using tools like Python, R, and Excel. The course also reflects emerging trends in 2025, such as AI-powered statistical insights, Bayesian methods, and automated hypothesis generation.

What You Will Learn

By the end of this course, learners will:

  • Understand the role of statistics in the data science lifecycle

  • Explore types of data, scales of measurement, and data distributions

  • Use descriptive statistics to summarize and visualize data

  • Apply probability theory and statistical inference

  • Conduct hypothesis testing using t-tests, ANOVA, and chi-square tests

  • Analyze relationships using correlation and linear regression

  • Interpret statistical results for business and technical decisions

  • Apply concepts using Python (NumPy, SciPy, pandas) or R (ggplot2, dplyr)

  • Learn Bayesian thinking and real-world applications

  • Explore 2025 trends such as AI-assisted analysis, predictive statistics, and data ethics in statistical modeling

Course Curriculum

  • Introduction to Statistics in Data Science

    • Why statistics matter in data-driven roles

    • Statistical thinking and common use cases

  • Types of Data and Data Distributions

    • Qualitative vs. quantitative data

    • Normal, skewed, binomial, and Poisson distributions

    • Visualizing distributions using histograms and density plots

  • Descriptive Statistics

    • Measures of central tendency: mean, median, mode

    • Measures of dispersion: range, variance, standard deviation

    • Data visualization: box plots, bar charts, scatter plots

  • Probability and Statistical Inference

    • Fundamentals of probability

    • Conditional probability and Bayes’ Theorem

    • Sampling methods and the Central Limit Theorem

  • Hypothesis Testing

    • Null and alternative hypotheses

    • p-values and confidence intervals

    • t-tests, z-tests, ANOVA, chi-square test

    • Real-world business scenarios (A/B testing, surveys)

  • Correlation and Regression Analysis

    • Pearson and Spearman correlation

    • Simple and multiple linear regression

    • Evaluating regression models and assumptions

  • Introduction to Bayesian Statistics

    • Basics of Bayesian inference

    • Comparing Bayesian and frequentist approaches

    • Applications in forecasting and risk modeling

  • Statistics with Python or R (Hands-on)

    • Using Python (pandas, statsmodels, seaborn)

    • Or using R (tidyverse, ggplot2, caret)

    • Running statistical tests and interpreting outputs

  • Emerging Trends in Statistical Analytics (2025)

    • AI-assisted data summarization

    • Predictive modeling using statistical techniques

    • Data bias and ethics in statistical analysis

    • Tools like ChatGPT, Copilot, and AutoML in statistics workflows

  • Final Assessment and Certification

    • Mini-project using a real dataset

    • Apply statistical techniques to solve a problem

    • Submit analysis and receive a verified certificate

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Title From Date To Date Cost
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This Course Fee:

₹14,999.00

₹30,000.00
Course includes:
  • img Level
      Beginner Intermediate Expert
  • img Duration 9h
  • img Passing Marks 75
  • img Exam Duration 1h 30m
  • img Certifications Yes
  • img Language
      English German Arabic French Spanish
  • img Access 90 days
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