Analytics and Data Science
• Overview of Data Analytics, Data Science, and Machine Learning
• Roles and responsibilities of Data Scientists, Analysts, and Machine Learning Engineers
• Key differences between Data Analytics and Data Science
• Introduction to Python and Power BI

Module 2: Python for Data Analytics
• Introduction to Python Programming: Data Types, Variables, and Syntax
• Working with Python Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Data Loading, Cleaning, and Preprocessing with Pandas
• Exploratory Data Analysis (EDA) and Visualization

Module 3: Statistical Analysis and Probability
• Descriptive Statistics: Mean, Median, Mode, Standard Deviation
• Inferential Statistics: Hypothesis Testing, p-values, Confidence Intervals
• Probability Distributions: Normal, Poisson, Binomial
• Data Visualization: Histograms, Boxplots, and Scatter Plots

Module 4: Introduction to Machine Learning
• Overview of Machine Learning Algorithms: Supervised vs Unsupervised Learning
• Introduction to Scikit-learn and key machine learning algorithms
• Preparing data for machine learning: Feature Scaling, Encoding Categorical Variables
• Building and evaluating models: Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN)

Module 5: Supervised Learning
• Understanding and implementing Linear Regression and Logistic Regression
• Decision Trees and Random Forest Classifiers
• Model Evaluation: Accuracy, Precision, Recall, F1-Score, Cross-Validation
• Hyperparameter Tuning and Grid Search

Module 6: Unsupervised Learning
• Clustering with K-Means and Hierarchical Clustering
• Dimensionality Reduction with PCA (Principal Component Analysis)
• Anomaly Detection and its Applications
• Market Basket Analysis using Association Rule Learning

Module 7: Machine Learning Model Deployment
• Introduction to Model Deployment with Python
• Creating RESTful APIs using Flask for Model Integration
• Introduction to Docker for Packaging Models
• Deploying models on cloud platforms (AWS, Azure)

Module 8: Power BI for Data Visualization
• Introduction to Power BI: Interface and Features
• Importing Data into Power BI and Data Transformation
• Designing Interactive Dashboards: Filters, Slicers, and Drill-through
• Advanced Visualizations: Geospatial, KPI, and Custom Visuals
• Power BI Reports: Publishing, Sharing, and Collaboration

Module 9: Advanced Analytics with Power BI
• Integrating Python Scripts in Power BI for Advanced Analytics
• Real-time Data Visualization with Power BI Streaming Datasets
• Building Predictive Analytics Dashboards in Power BI
• Power BI with Machine Learning Models: Visualizing Predictions and Insights

Module 10: Final Project and Case Studies
• End-to-End Project: Data Preprocessing, Model Building, and Visualization
• Case Studies from Industry: Healthcare, Finance, Retail
• Capstone Project: Developing a Complete Data Science Solution with Python and Power BI

Participant Outcomes
Upon successful completion of this course, participants will:
• Be proficient in using Python for data analysis, statistical modeling, and machine learning.
• Understand key concepts of Data Science, including data preprocessing, feature engineering, and model evaluation.
• Gain hands-on experience with Power BI for creating interactive dashboards and integrating machine learning models.
• Build and deploy machine learning models using Python, Flask, and Docker.
• Develop end-to-end data science solutions, from data cleaning to visualization and model deployment.
•Gain the skills necessary to solve real-world data challenges in various industries like healthcare, finance, and retail.

Data Analytics & Business Intelligence

Synchronizing Systems

Data Analytics & Business Intelligence