Python for Data Science and AI

DIXONTECH presents an advanced Python for Data Science and AI course designed to build practical programming and analytical skills for real-world applications. Participants learn to process, analyze, visualize, and model data using Python libraries and AI frameworks. The course connects theory with hands-on labs, empowering professionals to automate workflows, implement predictive models, and deploy AI solutions effectively.

TRAINING TOPICS

• PYTHON PROGRAMMING FOR DATA ANALYSIS
• NUMPY, PANDAS & DATA MANIPULATION TECHNIQUES
• DATA VISUALIZATION WITH MATPLOTLIB & SEABORN
• MACHINE LEARNING WITH SCIKIT-LEARN
• INTRODUCTION TO ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO:

• Leverage Python tools to analyze data and build intelligent models for business and research.
• Write Python programs for data extraction and analysis.
• Manipulate, clean, and process data efficiently.
• Visualize trends and build dashboards.
• Develop and train predictive machine learning models.
• Apply AI algorithms and neural networks.
• Automate data workflows using Python scripts.

TRAINING IS TAILORED TO

• Professionals and learners aiming to enhance their technical data analysis and AI capabilities.
• Data Scientists and Analysts.
• AI Engineers and Machine Learning Practitioners.
• Business Intelligence Professionals.
• Software Developers and IT Specialists.
• Researchers and Academics in analytics fields.
• Students pursuing data science careers.

TRAINING METHODOLOGY

The course uses coding workshops, practical datasets, and guided labs. Participants perform real coding tasks, develop predictive models, and visualize data using interactive notebooks and Python environments. The approach ensures immediate application of skills in professional settings.

DAY 1:

PYTHON PROGRAMMING FOR DATA ANALYSIS

• Python environment setup and essential tools.
• Variables, data types, and control structures.
• Functions, loops, and data collections.
• Working with files and input/output operations.
• Introduction to Jupyter Notebook and IDEs.
• Writing clean and efficient Python code.

DAY 2:

NUMPY, PANDAS & DATA MANIPULATION TECHNIQUES

• Introduction to Numpy arrays and vector operations.
• DataFrames, indexing, and slicing in Pandas.
• Handling missing data and outliers.
• Grouping, merging, and reshaping data.
• Performing descriptive statistics in Pandas.
• Building reusable data preparation pipelines.

DAY 3:

DATA VISUALIZATION WITH MATPLOTLIB & SEABORN

• Introduction to data visualization principles.
• Creating line, bar, scatter, and pie charts.
• Advanced visuals using Seaborn for analytics.
• Styling, themes, and interactive dashboards.
• Data storytelling and visual presentation.
• Exporting and sharing visual reports.

DAY 4:

MACHINE LEARNING WITH SCIKIT-LEARN

• Overview of machine learning in data science.
• Supervised vs unsupervised learning explained.
• Building regression and classification models.
• Model evaluation: accuracy, precision, recall.
• Hyperparameter tuning and cross-validation.
• Building an end-to-end ML pipeline.

DAY 5:

INTRODUCTION TO ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS

• Understanding AI and deep learning concepts.
• Neural networks fundamentals and architecture.
• Using TensorFlow and Keras basics.
• Image and text recognition use cases.
• AI in decision support and automation.
• Capstone project: develop a Python AI model.

FORMATTING NOTES:

• Font: Poppins
• Size: 12
• Headings: Uppercase + Bold
• DAY titles: Bold + separate line
• Main titles: Bold + separate line
• Bullets: Standard, one line each (5–10 words)
• Spacing and alignment: Clean professional layout.