Python for AI & Data Science: Essential Programming & Libraries Training Course

Introduction

In the rapidly evolving landscape of Artificial Intelligence (AI) and Data Science, proficiency in Python programming is not just an asset-it's a fundamental requirement. This Python for AI & Data Science: Essential Programming & Libraries Training Course is meticulously designed to equip aspiring data scientists, AI engineers, and machine learning practitioners with the robust coding skills and comprehensive understanding of the key Python libraries that underpin modern data-driven innovations. From foundational syntax to advanced data manipulation and visualization, this program ensures you can effectively harness Python's power for complex analytical tasks and intelligent system development.

Participants will dive deep into indispensable Python libraries such as NumPy for numerical computing, Pandas for efficient data handling and analysis, and Matplotlib and Seaborn for compelling data visualization. The curriculum also explores core programming concepts, file handling, and API interactions, all crucial for building scalable AI and data science solutions. By mastering these essential tools and techniques, you will be prepared to tackle real-world data analytics challenges, preprocess large datasets, and lay the groundwork for developing sophisticated machine learning models.

Target Audience

• Aspiring Data Scientists and Data Analysts.
• Individuals pursuing a career in Artificial Intelligence or Machine Learning.
• Software Developers looking to transition into AI or Data Science.
• Researchers and Academics requiring Python for data analysis.
• Professionals seeking to automate tasks and extract insights from data.
• Anyone with a basic understanding of computing interested in learning Python for data applications.

Duration

10 days

Course Objectives

1. Master Python programming fundamentals, including syntax, data types, and control structures.
2. Develop proficiency in using NumPy for numerical computations and array manipulations.
3. Gain expertise in Pandas for efficient data manipulation, analysis, and cleaning.
4. Apply Matplotlib and Seaborn for creating insightful and effective data visualizations.
5. Understand file handling operations for importing and exporting various data formats.
6. Learn to interact with web APIs for data collection and integration.
7. Build foundational skills for data preprocessing, exploration, and feature engineering.
8. Prepare for advanced studies in machine learning and AI model development using Python.

Course Content

Module 1. Python Programming Fundamentals

• Introduction to Python: Environment setup, basic syntax, and first programs
• Variables, data types, and operators in Python
• Control flow: Conditional statements (if/elif/else) and loops (for/while)
• Functions: Defining custom functions, arguments, and return values
• Object-Oriented Programming (OOP) basics: Classes, objects, and methods

Module 2. Essential Python Data Structures

• Lists: Creation, manipulation, and common list methods
• Tuples: Understanding immutable sequences and their uses
• Dictionaries: Working with key-value pairs for flexible data storage
• Sets: Managing unique elements and set operations
• Choosing the right data structure for specific data science tasks

Module 3. Numerical Computing with NumPy

• Introduction to NumPy arrays: Creation, indexing, and slicing
• Array operations: Element-wise, broadcasting, and aggregation functions
• Linear algebra operations with NumPy
• Reshaping and manipulating array dimensions
• Practical applications of NumPy in data science computations

Module 4. Data Manipulation with Pandas

• Introduction to Pandas DataFrames and Series
• Loading and saving data from various sources (CSV, Excel, SQL, JSON)
• Data inspection: Head, tail, info, describe, and unique values
• Data cleaning: Handling missing values, duplicates, and inconsistent data
• Data transformation: Filtering, sorting, grouping, and merging DataFrames

Module 5. Advanced Data Manipulation & Feature Engineering

• Applying custom functions to Pandas DataFrames
• Working with time series data: Indexing, resampling, and shifting
• String manipulation techniques for text data
• Feature engineering basics: Creating new features from existing ones
• Data pivoting and melting for data reshaping

Module 6. Data Visualization with Matplotlib and Seaborn

• Introduction to Matplotlib for basic plotting
• Creating line plots, scatter plots, bar charts, and histograms
• Customizing plots: Titles, labels, legends, and styles
• Seaborn for enhanced statistical data visualization
• Creating complex visualizations: Heatmaps, box plots, and pair plots

Module 7. File I/O and Data Persistence

• Reading from and writing to text files
• Working with CSV and Excel files for data input/output
• Serialization with JSON and Pickle for Python objects
• Introduction to interacting with databases (e.g., SQLite) using Python
• Best practices for managing data files in projects

Module 8. Web Scraping and API Interaction

• Understanding HTTP requests and responses
• Using the requests library for interacting with Web APIs
• Parsing JSON and XML data from API responses
• Introduction to web scraping with BeautifulSoup (ethical considerations)
• Practical examples of gathering data from web sources

Module 9. Introduction to Statistical Analysis with Python

• Descriptive statistics: Mean, median, mode, standard deviation
• Probability distributions and random number generation
• Hypothesis testing basics: t-tests, chi-squared tests
• Correlation and covariance analysis
• Introduction to statistical modeling with SciPy and StatsModels

Module 10. Version Control with Git & GitHub for Data Science

• Introduction to Git: Why version control is essential
• Basic Git commands: clone, add, commit, push, pull
• Branching and merging workflows for collaborative projects
• Using GitHub for project hosting, collaboration, and portfolio
• Best practices for managing data science code with version control

Module 11. Essential Tools & Environments

• Mastering Jupyter Notebooks for interactive data analysis
• Using VS Code or other Integrated Development Environments (IDEs)
• Package management with Pip and Conda
• Virtual environments for managing project dependencies
• Debugging Python code effectively

Module 12. Building a Data Science Project in Python

• Project planning: Defining objectives and data sources
• Data acquisition and initial exploration
• Data cleaning and preprocessing pipeline development
• Exploratory Data Analysis (EDA) and visualization
• Presenting insights and preparing for advanced modeling

Course Content

Module 1. Understanding the Skills-Based Organization (SBO)

• Defining a skills-based organization: beyond job titles to capabilities
• The drivers for adopting an SBO model: agility, talent scarcity, future of work, employee engagement
• Benefits of a skills-first approach: enhanced internal mobility, better talent utilization, faster reskilling
• Comparison of traditional job-based vs. skills-based models
• Case studies of organizations successfully transitioning to an SBO

Module 2. Identifying & Mapping Organizational Skills

• Methodologies for conducting a comprehensive skills audit and inventory
• Developing a robust skills taxonomy and ontology
• Utilizing AI and machine learning for automated skill inference and mapping
• Identifying critical skills for current and future business needs
• The role of skills data in strategic workforce planning

Module 3. Redefining Talent Acquisition with a Skills Focus

• Shifting from role-based to skills-based job descriptions
• Sourcing and assessing candidates based on demonstrated skills and potential
• Leveraging skills data for more equitable and effective hiring
• Building a talent pipeline focused on critical capabilities, not just experience
• Integrating skills data from external talent marketplaces

Module 4. Skills-Driven Learning & Development

• Designing personalized learning pathways based on individual skill gaps and career aspirations
• Curating and delivering learning content aligned with specific skill development
• Measuring the ROI of L&D programs through demonstrable skill acquisition
• Upskilling and reskilling strategies for workforce transformation
• The role of internal mobility in developing new skills

Module 5. Skills-Based Performance & Career Management

• Shifting performance management from activities to skill application and growth
• Continuous feedback focused on skill development and mastery
• Creating dynamic career paths based on skill adjacency and progression
• Empowering employees to own their skill development journey
• Mentoring and coaching for skill enhancement

Module 6. Compensation, Rewards & Engagement in an SBO

• Exploring skills-based pay models and rewarding skill acquisition/mastery
• Designing transparent compensation frameworks that reflect market value of skills
• Non-monetary recognition for skill development and application
• Engaging employees through opportunities for diverse skill utilization
• The impact of skills-based approaches on employee satisfaction and retention

Module 7. Technology & Analytics for Skills-Based Operations

• Overview of skills intelligence platforms and their    functionalities
• Utilizing HR analytics for insights into workforce capabilities and gaps
• Integrating skills data across HRIS, LMS, ATS, and talent marketplaces
• Data governance and security considerations for sensitive skills data
• The future of AI in automating skill management and recommendations

Module 8. Implementing & Sustaining a Skills-Based Organization

• Building a comprehensive change management strategy for SBO adoption
• Gaining leadership buy-in and cross-functional collaboration
• Developing a phased implementation roadmap and pilot programs
• Communicating the value proposition of SBO to all employees
• Continuous monitoring, evaluation, and iteration of the skills-based operating model

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank