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Python for Statistics and Probability

Master data analysis through our in-depth course on Python for Statistics and Probability.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Master data analysis through our in-depth course on Python for Statistics and Probability.

  2. Explore statistical techniques and probability models using Python’s robust libraries.

  3. Gain valuable insights to enhance your data-driven decision-making skills.

CERTIFICATION:

  1. Achieve a Certified in Python for Statistical and Probabilistic Analysis credential upon successful completion.

  2. Demonstrate your expertise with this recognized certification.

  3. Enhance your professional profile with this valuable qualification.

LEARNING OUTCOMES:

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

  1. Conduct hypothesis testing and accurately estimate confidence intervals.

  2. Effectively analyze data distributions, correlations, and relationships.

  3. Leverage Python’s statistical libraries for practical applications.

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Course Curriculum

Introduction to Statistics and Probability
  1. Basic Concepts
    • Overview of statistics and probability.
    • Importance of statistical analysis in data science.
  2. Types of Data
    • Categorical vs. numerical data.
    • Scales of measurement: Nominal, ordinal, interval, ratio.
Python Basics for Statistics and Probability
  1. Introduction to Python
    • Python syntax, variables, and data types.
  2. Essential Libraries
    • NumPy, pandas, SciPy, Matplotlib, Seaborn, and statsmodels.
  3. Data Manipulation
    • Loading datasets, cleaning data, and handling missing values using pandas.
Descriptive Statistics
  1. Measures of Central Tendency
    • Mean, median, and mode.
  2. Measures of Dispersion
    • Range, variance, and standard deviation.
  3. Data Visualization
    • Histograms, box plots, and scatter plots using Matplotlib and Seaborn.
  4. Correlation and Covariance
    • Understanding relationships between variables.
Probability Fundamentals
  1. Basic Probability
    • Definitions, axioms, and rules of probability.
    • Independent and dependent events.
  2. Probability Distributions
    • Discrete distributions: Binomial, Poisson.
    • Continuous distributions: Normal, exponential, uniform.
  3. Bayesian Probability
    • Bayes’ theorem and applications in real-world problems.
Inferential Statistics
  1. Sampling
    • Random sampling, sample size, and sampling bias.
  2. Hypothesis Testing
    • Null and alternative hypotheses.
    • t-tests, chi-square tests, and ANOVA.
  3. Confidence Intervals
    • Constructing and interpreting confidence intervals.
Advanced Statistical Techniques
  1. Regression Analysis
    • Simple linear regression and multiple regression.
    • Evaluating regression models.
  2. Time Series Analysis
    • Trends, seasonality, and forecasting.
  3. Monte Carlo Simulation
    • Generating random variables and simulating scenarios.
Real-World Applications
  1. Descriptive Statistics for Business
    • Analyzing sales data and customer behavior.
  2. Hypothesis Testing in Marketing
    • A/B testing for campaigns.
  3. Predictive Analytics
    • Using regression for forecasting sales or growth trends.
Capstone Project
  1. Data Analysis Project
    • Analyze a real-world dataset using Python.
    • Perform descriptive and inferential statistics.
    • Visualize insights and generate a report.
  2. Example: Analyze customer data to predict buying behavior.

Training Features

Hands-On Learning

Coding exercises and real-world datasets.

Visualization Focus

Create compelling visuals for statistical insights.

Industry-Relevant Applications

Apply statistical techniques to real-world scenarios.

Interactive Tools

Jupyter Notebook and Google Colab for interactive coding.

Comprehensive Projects

End-to-end data analysis project to reinforce learning.

Certification

A certificate of completion demonstrating proficiency in Python for statistics and probability.

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