Python for Data Analytics and Machine Learning (Online Training Program)

8 Week Program; Starts 08 June to 31 July 2026

9:00–11:00 AM IST; Mon–Wed–Fri

Global Time: 🇦🇪 Dubai – 7:30 AM | 🇸🇦 KSA – 6:30 AM | 🇬🇧 London – 4:30 | 🇺🇸 New York – 11:30 PM

About the Course

This 8-week online training program provides a complete learning pathway from Python basics to advanced machine learning techniques. Participants will gain hands-on experience in data preprocessing, exploratory data analysis (EDA), visualization, and model building using industry-standard tools like NumPy, Pandas, Matplotlib, and Scikit-learn.

What You Will Learn

• Python programming fundamentals

• Data handling and preprocessing

• Exploratory data analysis (EDA)

• Data visualization techniques

• Machine learning concepts and workflows

• Supervised and unsupervised learning

• Model evaluation and optimization

• Real-world project development
  

Course Features

• Live interactive sessions (Mon–Wed–Fri)

• Lifetime access to recordings and PPTs

• 100% practical learning approach

• Mini project with final presentation

• ISO–IAF accredited certificate

Learning Outcomes

• Analyze and preprocess real-world datasets

• Perform EDA and generate insights

• Build and evaluate machine learning models

• Visualize data effectively

• Work with industry tools and libraries

• Solve real-world data-driven problems
  

Who Can Join

• Undergraduate students (BCA, BSc, BTech)

• Postgraduate students (MCA, MSc Data Science, Computer Science)

• Research scholars and early-career professionals

• Aspiring data analysts and data scientists

• Anyone interested in AI and machine learning

Course Fee

• Indian Participants: Rs 8000

• International Participants: $200 USD

Course Module

Week 1 – Introduction to Python Programming

• Introduction to Python and its applications in Data Analytics and AI

• Python installation and environment setup (Anaconda / Jupyter Notebook)

• Python basics: variables, data types, operators

• Control structures (if–else, loops)

• Functions and basic scripting

Week 2 – Python for Data Handling

• Introduction to NumPy

• Introduction to Pandas

• Data structures in Pandas (Series, DataFrames)

• Data loading and data manipulation

• Data cleaning and preprocessing

Week 3 – Exploratory Data Analysis

• Data summarization and descriptive statistics

• Handling missing values and outliers

• Data transformation techniques

• Introduction to exploratory data analysis

Week 4 – Data Visualization

• Visualization using Matplotlib

• Visualization using Seaborn

• Creating bar charts, histograms, scatter plots, and heatmaps

• Interpreting visual insights from datasets

Week 5 – Introduction to Machine Learning

• Overview of Machine Learning concepts

• Types of Machine Learning (Supervised and Unsupervised)

• Dataset splitting (training and testing)

• Introduction to Scikit-learn

Week 6 – Supervised Machine Learning

• Linear Regression

• Logistic Regression

• Model training and evaluation

• Performance metrics

Week 7 – Advanced Machine Learning Techniques

• Decision Trees

• Introduction to Clustering

• K-Means Clustering

• Model comparison and improvement

Week 8 – Mini Project

• Working with a real-world dataset

• Building a predictive model

• Model evaluation and visualization

• Project presentation and discussion

  Tools & Software Covered

  • Python

  • Jupyter Notebook / Anaconda / Google Colab

  • NumPy, Pandas

  • Matplotlib, Seaborn

  • Scikit-learn