AI for Data Analysts Training Course
This intensive 5-day training program is designed for data analysts looking to leverage AI tools for faster, more accurate data analysis. The course covers essential AI algorithms, programming with Python, data modeling techniques, predictive analytics, and data visualization using AI-powered tools.
Day 1: Introduction to AI and Its Role in Data Analysis (8 Hours)
Morning Session (4 Hours)
- Introduction to AI for Data Analytics
- Overview of AI and its applications in data analysis
- Key AI techniques: Machine learning, deep learning, NLP
- AI vs. traditional statistical methods in data analytics
- Fundamentals of Machine Learning (ML) for Data Analysts
- Supervised vs. unsupervised learning
- Key ML algorithms for data analysis: Regression, classification, clustering
- Practical applications of ML in business analytics
Afternoon Session (4 Hours)
- Introduction to Python for AI and Data Analytics
- Setting up a Python environment (Jupyter Notebook, Anaconda)
- Python libraries for AI and data analysis: NumPy, Pandas, Scikit-learn
- Hands-on: Data cleaning and preprocessing with Python
- Workshop: Exploratory Data Analysis (EDA) Using Python
- Data visualization techniques with Matplotlib and Seaborn
- Identifying trends, patterns, and outliers in datasets
Day 2: Advanced Machine Learning Techniques (8 Hours)
Morning Session (4 Hours)
- Feature Engineering and Data Preparation
- Handling missing data, data transformation, feature scaling
- Feature selection techniques for better model performance
- Building and Evaluating ML Models
- Training and testing datasets
- Model evaluation metrics: Accuracy, precision, recall, F1-score
- Cross-validation techniques
Afternoon Session (4 Hours)
- Predictive Analytics with AI
- Time-series forecasting and trend prediction
- AI for customer behavior analysis and risk assessment
- Workshop: Building a Predictive Model with Python
- Hands-on exercise: Developing a predictive model using real-world datasets
Day 3: Deep Learning and Neural Networks for Data Analysis (8 Hours)
Morning Session (4 Hours)
- Introduction to Deep Learning for Data Analytics
- Difference between machine learning and deep learning
- Neural networks and their role in data analysis
- TensorFlow and Keras for deep learning
- Building a Deep Learning Model for Data Analysis
- Understanding layers, activation functions, and optimization techniques
- Hands-on: Creating a simple neural network for predictive analytics
Afternoon Session (4 Hours)
- Natural Language Processing (NLP) for Text Data Analysis
- Tokenization, sentiment analysis, named entity recognition
- AI-powered chatbots and recommendation systems
- Workshop: Text Analytics Using NLP
- Hands-on: Using NLP tools to analyze customer feedback and social media data
Day 4: AI-Powered Data Visualization and Business Intelligence (8 Hours)
Morning Session (4 Hours)
- AI in Data Visualization
- How AI improves data visualization and reporting
- Tools: Power BI, Tableau, Python visualization libraries
- Creating dynamic dashboards with AI-powered insights
- Business Intelligence and AI-Driven Decision Making
- How AI enhances data-driven business strategies
- Real-world case studies of AI-powered business intelligence
Afternoon Session (4 Hours)
- Big Data Analytics and AI
- Understanding big data and AI’s role in handling large datasets
- AI-driven tools for big data processing: Hadoop, Spark
- Workshop: Creating an AI-Powered Business Intelligence Dashboard
- Hands-on: Building a real-time analytics dashboard with AI-driven insights
Day 5: Implementing AI Solutions in Data Analytics (8 Hours)
Morning Session (4 Hours)
- AI Model Deployment and Automation
- Model deployment strategies using cloud services (AWS, Google Cloud, Azure)
- Automating AI-driven data analytics processes
- Evaluating AI Solutions for Business Applications
- Measuring the effectiveness of AI models in decision-making
- AI ethics and regulatory considerations
Afternoon Session (4 Hours)
- Final Capstone Project: Solving a Real-World Data Analytics Problem
- Individual/group project: Applying AI techniques to analyze business data
- Presentation of findings and recommendations
- Final Q&A and Certification Ceremony
- Addressing participant questions
- Course wrap-up and awarding of completion certificates
Who Should Attend?
- Data analysts and business intelligence professionals
- Data scientists and AI enthusiasts
- Marketing and finance professionals working with large datasets
- Anyone looking to integrate AI into data analytics
This course provides a hands-on approach to applying AI in data analytics, helping professionals gain practical skills to enhance data-driven decision-making.
8th Floor ZB Chambers
15 George Silundika Avenue,
Harare, Harare 263 15 George Silundika Avenue,
Zimbabwe
Email: info@dataanalysis.co.zw
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