Data Science & Analytics for AI & Emerging Tech Training Course

Introduction

In today's rapidly evolving digital landscape, data is the new currency, and the ability to extract meaningful insights from it is paramount. The Data Science & Analytics for AI & Emerging Tech Training Course is specifically designed to empower professionals with the critical skills needed to transform raw data into actionable knowledge. This program delves into the core methodologies of data science and analytics, providing a robust foundation for anyone aiming to leverage data-driven decision-making in the age of Artificial Intelligence (AI) and other emerging technologies.

Participants will gain hands-on experience with techniques for data collection, cleaning, visualization, statistical analysis, and predictive modeling. The curriculum emphasizes practical application, ensuring learners can effectively interpret complex datasets, identify trends, and communicate findings that fuel innovation across various sectors. Mastering these analytical capabilities is crucial for individuals who aspire to play a pivotal role in digital transformation, enabling them to contribute strategically to the development and deployment of intelligent systems.

Target Audience

• Business Analysts and Strategists looking to enhance data-driven insights.
• Project Managers involved in data-intensive initiatives.
• Aspiring Data Scientists and Data Analysts.
• Professionals seeking to understand the analytical backbone of AI.
• Decision-makers who need to interpret complex reports and trends.
• Anyone interested in building a career at the intersection of data, analytics, and emerging technologies.

Duration

10 days

Course Objectives

1. Understand the foundational concepts of data science and the analytics lifecycle.
2. Apply key statistical methods for data interpretation and analysis.
3. Master techniques for effective data cleaning, preprocessing, and management.
4. Develop proficiency in creating compelling data visualizations for various audiences.
5. Gain an introduction to predictive modeling and basic machine learning concepts for analytics.
6. Learn to derive actionable business intelligence from complex datasets.
7. Recognize the symbiotic relationship between data analytics and Artificial Intelligence.
8. Discuss ethical considerations and best practices in data handling and privacy.

Course Content

Module 1. Foundations of Data Science and Analytics

• Introduction to Data Science: Definition, scope, and key phases of the analytics lifecycle
• The role of data in the era of AI and emerging technologies
• Types of data: Structured, unstructured, semi-structured, and their characteristics
• Key roles in data teams: Data Scientist, Data Analyst, Data Engineer, ML Engineer
• Overview of popular tools and platforms for data analytics

Module 2. Statistical Foundations for Data Analysis

• Descriptive Statistics: Measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation)
• Inferential Statistics: Understanding sampling, estimation, and confidence intervals
• Probability theory basics: Random variables, distributions (normal, binomial)
• Hypothesis testing fundamentals: Null and alternative hypotheses, p-values
• Correlation and causation: Distinguishing relationships in data

Module 3. Data Collection and Management

• Understanding various data sources: Internal databases, external APIs, web scraping
• Introduction to database concepts: Relational (SQL) and NoSQL databases
• Data warehousing and data lakes: Concepts and use cases
• Principles of data governance and data quality management
• Data acquisition strategies for diverse analytical needs

Module 4. Data Cleaning and Preprocessing

• Identifying and handling missing data: Imputation techniques
• Detecting and treating outliers in datasets
• Data transformation techniques: Scaling, normalization, standardization
• Data type conversion and consistency checks
• Feature engineering basics: Creating new variables for enhanced insights

5. Exploratory Data Analysis (EDA)

• Techniques for understanding data distribution and relationships
• Data profiling: Summarizing key characteristics of a dataset
• Univariate, bivariate, and multivariate analysis
• Identifying patterns, trends, and anomalies through statistical methods
• Formulating initial hypotheses based on data exploration

6. Data Visualization for Insights

• Principles of effective data visualization: Clarity, accuracy, impact
• Choosing the right chart type for different data stories (bar, line, scatter, pie, histogram)
• Using visualization tools: Introduction to Tableau, Power BI, and Python libraries (Matplotlib, Seaborn)
• Designing interactive dashboards and reports
• Storytelling with data: Communicating insights effectively

7. Predictive Analytics and Modeling Introduction

• Understanding predictive modeling: Forecasting future outcomes
• Introduction to supervised learning: Regression (e.g., linear regression) and Classification (e.g., logistic regression)
• Model evaluation metrics: R-squared, MAE, accuracy, precision, recall
• Overfitting and underfitting: Basic concepts in model building
• Introduction to simple time series forecasting techniques

8. Business Intelligence (BI) and Reporting

• The role of Business Intelligence in modern organizations
• Key BI tools and platforms for data aggregation and reporting
• Designing effective BI dashboards and interactive reports
• Automating reporting processes for efficiency
• Leveraging BI for real-time decision-making and performance monitoring

9. AI Synergy: Data for Machine Learning

• How prepared data fuels Machine Learning models
• The importance of data quality and quantity for AI performance
• Feature selection and engineering for optimal ML model training
• Understanding the data pipeline from raw data to ML-ready datasets
• Case studies showing how data transforms into AI-driven solutions

10. Data Ethics, Privacy, and Governance

• Ethical considerations in data collection and usage
• Data privacy regulations: Overview of GDPR, CCPA, and similar frameworks
• Principles of data security and access control
• Best practices for anonymization and pseudonymization
• Establishing a robust data governance framework within an organization

11. Big Data Concepts for Analytics

• Introduction to Big Data: Volume, Velocity, Variety, Veracity
• Overview of distributed computing frameworks: Hadoop and Spark ecosystems
• Understanding the benefits and challenges of Big Data analytics
• Data streaming and real-time analytics concepts
• Cloud Big Data services: AWS, Azure, Google Cloud offerings

12. Capstone Project: Real-World Data Analysis

• Defining a real-world data analysis problem
• Data acquisition, cleaning, and preparation for the project
• Performing exploratory data analysis and visualization
• Applying basic predictive modeling techniques (if applicable)
• Presenting findings, insights, and recommendations from the analysis

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