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LearnerBox Infosystems LLP

Applied Statistics & Data Science

Build the statistical and analytical foundation for AI, Machine Learning, Data Science, NLP, and Research.

PROGRAM 4

Statistics & Data Science for AI, Machine Learning & Research

Develop strong statistical thinking and practical data analysis skills using real-world datasets, R, SPSS, Excel, and professional analytical workflows.

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Statistics for AI

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7 Free Modules

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15 Premium Modules

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R & SPSS Practice

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Real Datasets

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Professional Certificate

Program Overview

Statistics is the mathematical language of Artificial Intelligence, Machine Learning, Data Science, and Research. While many AI courses focus on using AI tools, this program helps learners understand the statistical reasoning that powers intelligent systems.

Students develop practical skills in statistical thinking, data analysis, visualization, hypothesis testing, regression, and predictive modelling using real-world datasets and professional statistical software.

The program bridges the gap between mathematics and modern AI, enabling learners to interpret data confidently and support evidence-based decision making across research, business, and technology.

Who Should Enroll?

  • University students and researchers
  • Beginners in Data Science
  • AI and Machine Learning learners
  • Data analysts and business analysts
  • PhD scholars and research professionals
  • Professionals who want to become data literate
  • Quality analysts and professionals working with AI and data

Learning Outcomes

By completing this program, learners will be able to:

  • Understand core statistical concepts
  • Interpret datasets correctly
  • Perform descriptive statistical analysis
  • Create meaningful data visualizations
  • Conduct hypothesis testing and statistical inference
  • Apply correlation and regression analysis
  • Use Excel, SPSS, R, and RStudio for analysis
  • Work with public datasets from Kaggle, UCI, WHO, World Bank, and open-data sources
  • Support AI and Machine Learning workflows with statistical reasoning

Free Track: Statistics & Data Literacy

The free track helps learners develop statistical reasoning, interpret data correctly, perform basic statistical analysis, and build the mathematical foundation required for AI, Machine Learning, Data Science, Business Analytics, and Research.

Module 1: Foundations of Statistics

Learn types of data, variables, measurement, population and sample, descriptive versus inferential statistics, sources of data, and introduction to AI datasets.

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Module 2: Descriptive Statistics

Explore frequency tables, mean, median, mode, variance, standard deviation, percentiles, and outlier detection using Excel, SPSS, and R.

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Module 3: Data Visualization

Create bar charts, pie charts, histograms, boxplots, scatterplots, heatmaps, and learn how to choose the right visualization.

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Module 4: Probability & Statistical Distributions

Understand probability, random variables, normal and binomial distributions, sampling distributions, and the Central Limit Theorem.

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Module 5: Statistical Inference

Learn confidence intervals, hypothesis testing, statistical power and sample size, p-values, z-tests and t-tests, and how to choose the correct test.

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Module 6: Analysis of Variance

Learn foundations and assumptions of ANOVA, one-way and two-way ANOVA, post-hoc tests, and how to choose the correct test.

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Module 7: Correlation, Regression & AI Datasets

Study correlation, simple and multiple regression, model interpretation, prediction, and introductory machine learning datasets.

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Premium Track: Applied Statistics & Data Science

The premium track develops advanced statistical, analytical, and computational skills required for Machine Learning, Deep Learning, Natural Language Processing, Research, Artificial Intelligence, and Business Analytics.

  • Mathematical foundations for data science
  • Probability theory and Monte Carlo simulation
  • Research design and sampling
  • Exploratory data analysis and data profiling
  • Statistical programming with R
  • SPSS for professional data analysis
  • Advanced hypothesis testing
  • Regression analysis and model diagnostics
  • Categorical data analysis
  • Multivariate statistics
  • Time series analysis and forecasting
  • Statistics for Machine Learning
  • Statistics for Deep Learning and NLP
  • Applied data science projects using public datasets
  • Professional data analysis capstone

Learners will work with public datasets from Kaggle, UCI Machine Learning Repository, WHO, World Bank, government open-data sources, and domain-specific datasets in health, finance, education, and business.

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Career Outcomes

Potential roles include:

  • Data Analyst
  • Business Analyst
  • Research Analyst
  • Statistical Analyst
  • Quantitative Research Assistant
  • Machine Learning Analyst
  • AI Data Specialist
  • Data Science Associate
  • Market Research Analyst
  • Quality & Process Analyst
  • AI Research Assistant
  • Clinical Data Analyst