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  • Statistics for Data Science

Statistics for Data Science

  • By educlassixglobal
  • (1 Rating)
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    • Develop strong statistical foundations for data science. Learn probability, hypothesis testing, regression analysis, and data interpretation techniques used to make data driven decisions

      What Will You Learn?
      • Statistics fundamentals
      • Probability concepts
      • Hypothesis testing
      • Regression analysis
      • Data visualization
      • Data-driven decisions

      Material Includes

      • Get access to curated notes, guides, and reference materials that help you revise concepts anytime, even offline
      • Work on practical assignments and real-life scenarios to build job-ready skills and strengthen your portfolio
      • Test your understanding with interactive quizzes and module-based assessments to track your progress and reinforce learning
      • Practice what you learn with guided exercises that help you gain confidence in applying concepts independently

      Requirements

      • Basic understanding of mathematics (especially algebra and arithmetic).
      • Familiarity with Microsoft Excel or any basic data-handling tool (helpful but not mandatory).
      • No prior programming experience required — Python fundamentals will be introduced in the course.
      • A curious and analytical mindset with a willingness to learn data-driven problem-solving.

      Course Content

      Introduction to Data Science

      • What is Data Science?
      • Applications of Data Science
      • Data Science Process
      • Tools & Roles

      Python for Data Science

      • Python Basics
      • Jupyter Notebooks
      • NumPy
      • Pandas
      • Working with DataFrames

      Statistics and Probability

      • Descriptive Statistics
      • Inferential Statistics
      • Probability Distributions
      • Hypothesis Testing

      Data Wrangling and Cleaning

      • Handling missing data
      • Data transformation
      • Data encoding
      • Outliers and normalization

      Data Visualization

      • Matplotlib
      • Seaborn
      • Plotly (Line, Bar, Histogram, Heatmaps)
      • Dashboard introduction

      Exploratory Data Analysis (EDA)

      • EDA process
      • Correlation
      • Grouping
      • Aggregation
      • Feature extraction

      Machine Learning Basics

      • Supervised vs Unsupervised Learning
      • Regression
      • Classification
      • Clustering basics

      Model Evaluation and Selection

      • Train/Test split
      • Cross-validation
      • Confusion matrix
      • Accuracy
      • Precision
      • Recall
      • F1-score

      Big Data Concepts

      • Introduction to Hadoop
      • Spark basics
      • Cloud-based data tools (AWS/GCP)

      Capstone Project and Case Studies

      • Mini project covering data preprocessing to model deployment

      Tags

      • Health
      • Kitchen

      A course by

      E
      educlassixglobal

      Student Ratings & Reviews

      5.0
      Total 1 Rating
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      0 Rating
      2 years ago
      Clear, concise, and to the point.

      Course Includes:

      • Price:
        $699.00
      • Instructor:educlassixglobal
      • Duration: 40 hours
      • Lessons:41
      • Students:1
      • Level:Intermediate
      $699.00
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