ADVANCED STATISTICAL ANALYSIS WITH PYTHON AND R

Tickets
From ₦ 210,000.00 to ₦ 280,000.00
Tickets ×
Live-Online Training:
₦ 210,000.00 210000.0 NGN
Classroom Training:
₦ 280,000.00 280000.0 NGN

Live-Online Training: N 210,000.00
Classroom Training: N 280,000.00
3 - 4 participants: 5% discount
5 or more participants: 10% discount

(Available also for Customised Training by Duration, Venue & Fee)

Course Description:

This intensive 3-day course is designed for professionals who want to deepen their knowledge of statistical analysis using Python and R. Participants will learn advanced statistical methods, data analysis techniques, and how to implement these using Python and R for robust data-driven decision-making.

 

Course Objectives:

  • Understand advanced statistical concepts and methods.
  • Perform data cleaning and preprocessing in Python and R.
  • Conduct inferential statistics and hypothesis testing.
  • Build and evaluate regression models.
  • Integrate machine learning algorithms with statistical analysis.
  • Apply advanced statistical techniques to real-world data sets.

Contents

Day One

Fundamentals and Setup

·        Welcome and Course Overview

o    Introduction to the course objectives and schedule

o    Importance of advanced statistical analysis in various fields

·        Introduction to Python and R

o    Overview of Python and R for statistical analysis

o    Installation and setup of Python and R environments (Anaconda, RStudio)

·        Basic Syntax and Data Structures

o    Basic syntax and data structures in Python (lists, dictionaries, NumPy arrays, Pandas DataFrames)

o    Basic syntax and data structures in R (vectors, lists, data frames, matrices)

·        Data Import and Cleaning

o    Importing data from various sources (CSV, Excel, databases) in Python and R

o    Data cleaning and preprocessing techniques

·        Case Study: Data Cleaning

o    Hands-on exercise cleaning a dataset using Python and R

o    Discussion on challenges and best practices

 

Day Two

Exploratory Data Analysis and Statistical Methods

·        Exploratory Data Analysis (EDA)

o    Techniques for EDA in Python (using Pandas, Matplotlib, Seaborn)

o    Techniques for EDA in R (using dplyr, ggplot2)

·        Descriptive Statistics

o    Calculating descriptive statistics (mean, median, standard deviation) in Python and R

o    Visualizing data distributions

·        Hypothesis Testing

o    Introduction to hypothesis testing (t-tests, chi-square tests)

o    Performing hypothesis tests in Python (using SciPy) and R

·        Case Study: Hypothesis Testing

o    Hands-on exercise conducting hypothesis tests on a dataset

o    Interpreting results and drawing conclusions

 

Day Three

Advanced Statistical Modeling

·        Linear and Non-linear Regression

o    Performing linear regression in Python (using Statsmodels) and R

o    Introduction to non-linear regression and logistic regression

·        Time Series Analysis

o    Basics of time series analysis (ARIMA models, seasonality)

o    Implementing time series analysis in Python (using statsmodels) and R

·        Machine Learning for Statistical Analysis

o    Introduction to machine learning algorithms (decision trees, random forests, SVM)

o    Applying machine learning techniques in Python (using Scikit-learn) and R (using caret)

·        Case Study: Predictive Modeling

o    Hands-on exercise building a predictive model

o    Evaluating model performance and tuning

·        Wrap-Up and Review

o    Review of key concepts covered in the course


Date & Time
Wednesday
April 2, 2025
Start - 8:30 AM
Friday
April 4, 2025
End - 3:00 PM Africa/Lagos
Location

Tom Associates Training

5/7, Alade Lawal Street, Opposite Anthony Police Station, Off Ikorodu Road, Anthony Village,
NG-LA
Nigeria
+234 817 859 1654
+234 809 276 3968 | +234 810 365 2225
tomassociatestraining@yahoo.com | info@tomassociatesng.com
Get the direction
Organizer

Tom Associates Training

+234 817 859 1654
+234 809 276 3968 | +234 810 365 2225
tomassociatestraining@yahoo.com | info@tomassociatesng.com