This course provides a hands-on approach to data analysis using Python, focusing on the tools and techniques that make Python a powerful language for data analysis. Participants will learn how to manipulate, analyze, and visualize data effectively, leveraging libraries such as NumPy, pandas, Matplotlib, and Seaborn. By the end of the training, participants will be equipped to handle real-world datasets, derive insights, and communicate their findings.

Target Participants

This training is designed for Data analysts, statisticians, researchers, and professionals interested in leveraging Python for statistical modeling and data-driven decision-making.

What You Will Learn

By the end of this course, participants will be able to:

• Understand the fundamentals of statistical modeling and its applications.
• Gain proficiency in using Python libraries for statistical analysis, including NumPy, pandas, SciPy, and Statsmodels.
• Perform exploratory data analysis (EDA) to uncover insights from datasets.
• Develop and evaluate statistical models for predictive and inferential analysis.
• Apply advanced statistical techniques, including hypothesis testing, regression analysis, and time series modeling.
• Understand best practices for interpreting and presenting statistical findings.

Course Duration

• Classroom Training – 5 Days
• Online Training – 7 Days

Course Outline 

Introduction to Python Programming

• Installing Python and setting up the environment (Jupyter Notebook, Anaconda)
• Basic Python syntax, data types, and operations
• Working with lists, dictionaries, and tuples
• Python for data analysis

Essential Python Libraries for Data Analysis

• Overview of key libraries: NumPy, pandas, Matplotlib, Seaborn, and Scikit-learn
• Installing and importing libraries

Data Analysis Workflow

• The steps of a data analysis project
• Loading datasets and exploring data structures

Data Manipulation in Python

• Introduction to pandas
• Series and DataFrame: Core data structures in pandas
• Loading and saving data (CSV, Excel, JSON)
• Inspecting datasets: head(), .info(), and .describe()

Data Cleaning and Preprocessing

• Handling missing data (imputation, dropping rows/columns)
• Filtering, sorting, and subsetting data
• String operations and data transformations

NumPy for Numerical Data

• Creating and manipulating arrays
• Basic mathematical operations and aggregations
• Broadcasting and vectorized computations

Exploring Data

• Descriptive analysis
• Summarizing and grouping data
• Aggregation and pivot tables

Data Visualization

• Creating line plots, bar charts, histograms, and scatter plots
• Customizing visualizations (titles, labels, legends)
• Advanced visualizations with Seaborn: Heatmaps, pair plots, and box plots

Comparison Tests

• Understanding parametric vs non-parametric tests
• Choosing a test – decision tree
• Tests for Parametric and Non-parametric data
• Tests for Proportions

Tests of Association

• Introduction to correlation analysis
• Parametric data
• Nonparametric data

Predictive Modeling using Python

• Introduction to regression analysis
• Model diagnostics and assumptions
• Parametric data – linear regression analysis (simple and multiple regression)
• Non-parametric data – non-parametric regression
• Categorical dependent variables – logit and probit regression models

Automating Data Tasks with Python

• Writing reusable functions
• Automating repetitive data preprocessing tasks

Training Approach

This course is delivered by our seasoned trainers who have vast experience as expert professionals in their respective fields of practice. The course is taught through a mix of practical activities, presentations, group works, and case studies. Training manuals and additional reference materials are provided to the participants.

Certification

Upon successful completion of this course, participants will be issued a certificate.

Course Booking

Please use the “book now” or “inquire” buttons on this page to either book your space or make further enquiries.