Back

Probability and Statistics for Data Analysis

Participants will gain valuable skills applicable to real-world data analysis scenarios.

Explore courses
Register Now
Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Probability and Statistics for Data Analysis course provides a comprehensive introduction to essential probability and statistical techniques for data analysis.

  2. Tailored for both novices and experienced professionals, it aims to improve data interpretation and decision-making abilities.

  3. Key topics include probability distributions, hypothesis testing, and inferential statistics, emphasizing their relevance in data science and analytics.

  4. The curriculum combines theoretical knowledge with practical exercises utilizing tools such as Python, R, or Excel.

  5. Participants will gain valuable skills applicable to real-world data analysis scenarios.

CERTIFICATION:

  1. Certificate of Achievement

  2. Validates proficiency in statistical and probabilistic methods for data analysis

  3. Confirms expertise in data analytical techniques

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp essential principles of probability and statistics.

  2. Evaluate data distributions and compute important statistical metrics.

  3. Utilize various probability distributions, including normal, binomial, and Poisson.

  4. Conduct hypothesis tests and develop confidence intervals.

  5. Apply statistical techniques for data analysis and informed decision-making using tools such as Python (NumPy, SciPy, pandas), R, or Excel.

Enroll Now

Course Curriculum

Introduction to Probability and Statistics
  1. Role of statistics in data analysis
  2. Basics of probability: Definitions and rules
  3. Types of data: Quantitative vs. qualitative
Descriptive Statistics
  1. Measures of central tendency (mean, median, mode)
  2. Measures of variability (range, variance, standard deviation)
  3. Data visualization techniques (histograms, box plots, scatter plots)
Probability Concepts
  1. Basic probability principles: Addition and multiplication rules
  2. Conditional probability and Bayes’ Theorem
  3. Random variables and expectation
  4.  
Probability Distributions
  1. Discrete distributions: Binomial, Poisson
  2. Continuous distributions: Normal, exponential
  3. Applications of probability distributions in data analysis
Inferential Statistics
  1. Sampling methods and the Central Limit Theorem
  2. Confidence intervals and margin of error
  3. Hypothesis testing: Null and alternative hypotheses, p-values, and significance
Advanced Statistical Techniques
  1. Correlation and regression analysis
  2. Analysis of variance (ANOVA)
  3. Chi-square tests and non-parametric tests
Statistical Applications in Data Analysis
  1. Real-world case studies in finance, healthcare, and marketing
  2. Statistical insights for decision-making
Final Project
  1. Perform a complete statistical analysis on a dataset.
  2. Example: Analyze customer data to derive insights and test hypotheses.

Training Features

Hands-On Projects

Work on real-world datasets to apply statistical methods.

Tool-Specific Training

Practical exercises using Python (NumPy, SciPy, pandas), R, or Excel.

Interactive Simulations

Visualize and understand probability and statistical concepts.

Case Studies

Explore industry-specific applications of probability and statistics.

Customizable Learning Path

Adapt content to focus on areas of personal or professional interest.

Certification

A globally recognized certificate upon completing the course.

Get in Touch

    Our Relevant Courses list