Curriculum

CURRICULUM

Data Science

Module 1: Excel For Data Science

Introduction to Excel
- Overview of Excel interface and navigation
- Creating and saving workbooks
- Entering and editing data
- Basic formatting techniques
Essential Functions and Formulas
- Understanding basic arithmetic functions (SUM, AVERAGE, etc.)
- Logical functions (IF, AND, OR)
- Lookup and reference functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
- Date and time functions (TODAY, DATE, YEAR, MONTH, DAY)
Data Management and Analysis
- Sorting and filtering data
- Conditional formatting
- Data validation and error checking
- Using tables for structured data management
Advanced Formulas and Functions
- Text functions (LEFT, RIGHT, CONCATENATE)
- Statistical functions (COUNTIF, SUMIF, AVERAGEIF)
- Array formulas and handling array operations
- Mathematical and trigonometric functions
Data Visualization
- Creating and customizing charts (bar, line, pie, scatter)
- Using PivotTables and PivotCharts for data analysis
- Advanced charting techniques (combo charts, sparklines)
- Data analysis with What-If Analysis tools
Working with Large Datasets
- Handling large datasets efficiently
- Using filters and slicers for data analysis
- Importing data from external sources (text files, CSV)
Advanced Data Analysis
- Goal Seek and Solver for optimization problems
- Statistical analysis using Data Analysis ToolPak
- Advanced data modeling techniques
- Using scenario manager and forecasting tools
Collaboration and Sharing
- Sharing workbooks and managing shared workbooks
- Protecting data and workbook structure
- Reviewing and tracking changes
- Using Excel in collaborative environments (OneDrive, SharePoint)
Excel Tips and Tricks
- Efficiency tips and keyboard shortcuts
- Customizing Excel settings and options
- Troubleshooting common issues
- Best practices for using Excel in business contexts

Module 2: Tableau for Data Science

Introduction to Tableau
- Overview of Tableau: Purpose, features, and benefits
- Understanding Tableau workspace: Data pane, marks card, and shelves
- Connecting to data sources: Excel, CSV, databases, and cloud platforms
- Basic visualization types: Bar charts, line graphs, scatter plots, and pie charts
- Applying filters, sorting, and grouping data
Intermediate Tableau
- Advanced chart types: Heat maps, tree maps, box plots, and dual-axis charts
- Working with calculated fields and parameters
- Tableau dashboards: Design principles and best practices
- Interactive features: Tooltips, actions, and highlighters
- Using sets, groups, and hierarchies for data analysis
Advanced Tableau Techniques
- Advanced calculations: LOD expressions (Level of Detail)
- Mapping in Tableau: Geo-spatial data visualization and custom territories
- Integration with R and Python for advanced analytics
- Implementing table calculations and trend lines
- Dashboard interactivity: Parameters, actions, and filters
Tableau Server and Online
- Deploying Tableau workbooks on Tableau Server and Tableau Online
- Managing permissions and access control
- Scheduling and refreshing extracts
- Collaboration and sharing: Workbooks, views, and subscriptions
- Monitoring and performance optimization
Advanced Data Visualization Strategies
- Designing effective dashboards for different audiences
- Visual analytics: Exploring trends, outliers, and correlations
- Storytelling with data: Using Tableau stories for impactful presentations
- Integration with Big Data platforms and real-time data sources
- Best practices in data visualization and dashboard design

Module 3: SQL for Data Analysis

Introduction to SQL
- Overview of relational databases: Tables, rows, columns, and relationships
- Introduction to SQL: History, standards, and common database systems
- Basic SQL commands:
  - SELECT statement: Retrieving data from a single table
  - INSERT statement: Adding new records to a table
  - UPDATE statement: Modifying existing records in a table
  - DELETE statement: Removing records from a table
- Filtering data: WHERE clause, comparison operators, and logical operators
- Sorting and limiting results: ORDER BY, LIMIT, OFFSET
Advanced SQL Queries
- Joins and relationships:
  - INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
  - Self-joins and cross joins
- Subqueries and derived tables:
  - Scalar subqueries
  - Correlated subqueries
  - Common table expressions (CTEs)
- Set operations:
  - UNION, UNION ALL, INTERSECT, EXCEPT
- Aggregation functions:
  - SUM, AVG, MIN, MAX, COUNT
  - GROUP BY and HAVING clauses
Data Manipulation and Transaction Control
- String and date functions:
  - String manipulation functions (CONCAT, SUBSTRING)
  - Date and time functions (DATE, TIME, TIMESTAMP)
- Updating and deleting data:
  - UPDATE and DELETE statements with conditions
  - Handling NULL values with COALESCE and IS NULL/IS NOT NULL
- Transactions and concurrency control:
  - ACID properties of transactions
  - COMMIT, ROLLBACK statements
  - Locking mechanisms and isolation levels
SQL Optimization and Performance Tuning
- Indexing and query optimization:
  - Creating and managing indexes
  - Analyzing query execution plans
  - Using hints (INDEX, OPTIMIZE FOR) to improve performance
- Partitioning strategies:
  - Range, list, and hash partitioning
  - Managing partitioned tables
- Optimizing joins and subqueries:
  - Rewriting queries for performance
  - Using EXISTS and NOT EXISTS for efficient subquery evaluation
Advanced SQL Topics
- Stored procedures and functions:
  - Creating and managing stored procedures
  - Using parameters and variables in procedures
  - Error handling and transaction control within procedures
- Triggers and events:
  - Defining triggers for automated actions
  - Trigger types: BEFORE, AFTER, INSTEAD OF
- Dynamic SQL:
  - Generating and executing SQL statements dynamically
  - Building flexible queries based on runtime conditions
- Security and permissions:
  - Granting and revoking privileges
  - Managing roles and users
  - Implementing row-level security with views and policies

Module 4: Power BI

Introduction to Power BI
- Overview of Power BI: Components and capabilities
- Installing and setting up Power BI Desktop
- Connecting to various data sources: Excel, databases, web data
- Understanding Power BI Service and Power BI Mobile
Power BI Basics
- Building your first report: Creating visuals and dashboards
- Working with Power Query Editor: Data loading, transformation, and cleaning
- Creating calculated columns and measures using DAX
- Using Power BI visuals: Bar charts, line charts, maps, and more
Advanced Power BI Features
- Advanced data modeling with relationships: One-to-one, one-to-many, many-to-many
- Using DAX functions for complex calculations:
  - Aggregation functions: SUM, AVERAGE, COUNT, MIN, MAX
  - Time intelligence functions: YTD, QTD, MTD, DATESYTD, PARALLELPERIOD
  - Filter functions: CALCULATE, ALL, FILTER, RELATEDTABLE
- Advanced visualization techniques:
  - Custom visuals and marketplace integrations
  - Drill-down and drill-through capabilities
  - Bookmarks, tooltips, and interactive features
Power BI Data Analysis and Reporting
- Advanced data transformation with Power Query Editor:
  - Merging queries, conditional columns, unpivoting data
  - Advanced data cleaning techniques
- Data analysis with Power BI:
  - Statistical analysis: Regression analysis, clustering, and forecasting
  - Integrating R and Python scripts for advanced analytics
Power BI Deployment and Administration
- Deploying Power BI reports to Power BI Service
- Managing datasets, workspaces, and permissions
- Scheduling data refresh and optimizing performance
- Security considerations in Power BI: Row-level security and encryption
Real-World Applications and Best Practices
- Building end-to-end solutions with Power BI: From data ingestion to visualization
- Case studies and industry-specific applications
- Best practices for designing efficient and effective Power BI reports
- Collaboration and sharing insights with Power BI Service

Module 5: Alteryx for Data Processing

Introduction to Alteryx
- Overview of Alteryx Designer interface
- Alteryx Designer tools and workflow concepts
- Connecting to data sources
Data Preparation and Blending
- Input and Output tools
  - Text Input, Excel Input/Output, Database Input/Output
- Data Cleansing tools
  - Data Cleansing, Select, Filter, Sort
- Join tools
  - Join, Union, Append, Join Multiple
Spatial and Demographic Analysis
- Spatial tools
  - Trade Area, Distance, Create Points, Spatial Match
- Demographic tools
  - Demographic Summary, Demographic Imputation, Address/Census Info
Parsing and Transforming Data
- Parsing tools
  - Text to Columns, Data Cleansing, RegEx
- Transform tools
  - Formula, Filter, Summarize, Multi-Field Formula
Advanced Data Blending and Analysis
- Data Investigation tools
  - Frequency, Histogram, Summarize
- Data Transformation tools
  - Cross Tab, Transpose, Dynamic Rename, Data Cleansing
Reporting and Visualization
- Reporting tools
  - Table, Layout, Charting
- Interactive tools
  - Filter, Drop Down, Radio Button, Action
Advanced Analytics and Predictive Tools
- Predictive tools
  - Predictive Grouping, Find Nearest, Predictive Tools
- Time Series Analysis
  - Time Series Charts, Time Series Formula, Forecast, ARIMA
Macros and Batch Processing
- Macro Basics
  - Macro Input/Output, Interface Tools, Action Tools
- Batch Processing
  - Batch Macro, Control Parameter
Advanced Techniques and Optimization
- Optimization tools
  - Cache, Sample, Summarize, Unique
Integration and Automation
- API tools
  - Download, Parse, Query
- Integration tools
  - R/Python, XML/JSON, API Connect
Debugging and Error Handling
- Debugging tools
  - Browse, Comment, Sample, Write Data
- Error Handling
  - Error Message, Log Message, Test
Best Practices and Efficiency Tips
- Efficiency tools
  - Cache, Sample, Summarize, Unique
- Best practices
  - Documentation, Annotation, Workflow Overview

Module 6: Python for Data Science

Basic Python for Data Science
- Introduction to Python
  - What is Python?
  - Installing Python and IDEs (Anaconda, Jupyter Notebooks)
  - Python syntax basics: variables, data types, operators
- Control Flow and Functions
  - Conditional statements: if, elif, else
  - Loops: for loops, while loops
  - Functions: defining functions, arguments, return statements
- Data Structures
  - Lists, tuples, dictionaries, sets
  - Indexing and slicing
  - List comprehensions
- NumPy for Numerical Computing
  - Introduction to NumPy arrays
  - Array creation, indexing, slicing
  - Array operations: arithmetic, broadcasting
  - Array methods: reshaping, stacking, splitting
Intermediate Python for Data Science
- Pandas for Data Manipulation
  - Introduction to Pandas DataFrames
  - Data ingestion: reading and writing data
  - Data cleaning and preprocessing
  - Indexing and selecting data
- Data Visualization with Matplotlib and Seaborn
  - Introduction to Matplotlib: basic plots (line plots, scatter plots, histograms)
  - Customizing plots: labels, titles, colors
  - Introduction to Seaborn: statistical visualization, pair plots
  - Plotting with Pandas and Seaborn
- Working with APIs and Web Scraping
  - HTTP requests: GET, POST
  - Introduction to JSON and XML
  - Accessing web APIs with Python
  - Web scraping using BeautifulSoup and requests
Advanced Python for Data Science
- Machine Learning with Scikit-Learn
  - Introduction to machine learning concepts
  - Supervised learning: regression, classification
  - Unsupervised learning: clustering, dimensionality reduction
  - Model evaluation and validation
- Advanced Data Analysis
  - Time series analysis
  - Handling missing data
  - Statistical methods and hypothesis testing
  - Advanced data manipulation with Pandas
- Integration with Big Data and Cloud Platforms
  - Introduction to Apache Spark with PySpark
  - Connecting Python with cloud platforms (AWS, Google Cloud)
  - Distributed computing and data processing
Capstone Project
- Applying Python and data science skills to a real-world project
- Data exploration, analysis, and visualization
- Presenting insights and findings

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Machine Learning

Module 1: Fundamentals of Machine Learning

Types of ML: Supervised, unsupervised, and reinforcement learning with examples
The ML workflow: Problem definition, data collection, preprocessing, modeling, and deployment
Training, validation, and test sets: Purpose and best practices for data splitting
Overfitting and underfitting: Causes, detection, and mitigation strategies
Bias-variance tradeoff: Understanding the fundamental tension in machine learning
Feature engineering and selection techniques
Introduction to deep learning and neural networks

Module 2: Supervised Learning

Linear and polynomial regression: Ordinary least squares, regularization (Ridge, Lasso)
Logistic regression: Binary and multiclass classification
Decision trees and random forests: Entropy, information gain, and ensemble methods
Support Vector Machines: Linear and non-linear kernels, margin optimization
K-Nearest Neighbors: Distance metrics, choosing k, and the curse of dimensionality
Naive Bayes classifiers: Gaussian, Multinomial, and Bernoulli variants
Gradient Boosting Machines: XGBoost, LightGBM, and CatBoost

Module 3: Unsupervised Learning

K-means clustering: Algorithm, choosing k, and silhouette analysis
Hierarchical clustering: Agglomerative and divisive approaches
Principal Component Analysis (PCA): Dimensionality reduction and feature extraction
t-SNE: Non-linear dimensionality reduction for data visualization
Association rule learning: Apriori algorithm and frequent itemset mining
Gaussian Mixture Models and Expectation-Maximization algorithm
Anomaly detection techniques: Isolation Forest and One-Class SVM

Module 4: Neural Networks and Deep Learning

Perceptrons and multilayer networks: Activation functions and forward propagation
Backpropagation algorithm: Chain rule and gradient descent optimization
Convolutional Neural Networks (CNNs): Convolution, pooling, and applications in computer vision
Recurrent Neural Networks (RNNs) and LSTMs: Sequential data processing and natural language tasks
Transfer learning: Fine-tuning pre-trained models for new tasks
Generative Adversarial Networks (GANs): Architecture and applications
Attention mechanisms and Transformers: BERT, GPT, and their variants

Module 5: Model Evaluation and Optimization

Evaluation metrics for classification: Accuracy, precision, recall, F1-score, ROC-AUC
Evaluation metrics for regression: MSE, MAE, R-squared, RMSE
Cross-validation techniques: k-fold, stratified k-fold, and leave-one-out
Hyperparameter tuning: Grid search, random search, and Bayesian optimization
Ensemble methods: Bagging, boosting, and stacking
Feature importance and selection: Filter, wrapper, and embedded methods
Model interpretation techniques: SHAP values and LIME

Artificial Intelligence

Module 1: Introduction to AI

History and evolution of AI: From early rule-based systems to modern deep learning
Types of AI: Narrow (weak) AI vs general (strong) AI, and superintelligence
AI paradigms: Symbolic AI (rule-based systems) vs machine learning (data-driven approaches)
Turing test and Chinese room argument: Philosophical debates on machine intelligence
Ethics and societal impact of AI: Bias, privacy, job displacement, and existential risks
AI in industry: Current applications and future trends
Challenges in AI development: Explainability, robustness, and alignment with human values

Module 2: Search and Problem Solving

Problem formulation and state space: Defining goals, actions, and transition models
Uninformed search strategies: Breadth-first search (BFS), depth-first search (DFS), and uniform cost search
Informed search: A* algorithm, admissible heuristics, and optimality
Constraint satisfaction problems: Backtracking, forward checking, and arc consistency
Game playing and minimax algorithm: Alpha-beta pruning and expectimax for probabilistic scenarios
Local search algorithms: Hill climbing, simulated annealing, and genetic algorithms
Planning under uncertainty: Markov decision processes and reinforcement learning

Module 3: Knowledge Representation and Reasoning

Propositional logic: Syntax, semantics, and inference rules
First-order logic: Quantifiers, predicates, and unification
Inference and resolution: Forward chaining, backward chaining, and resolution refutation
Probabilistic reasoning: Bayesian networks, inference in graphical models
Fuzzy logic: Membership functions, fuzzy set operations, and fuzzy inference systems
Ontologies and semantic web: RDF, OWL, and knowledge graphs
Reasoning under uncertainty: Dempster-Shafer theory and possibility theory

Module 4: Natural Language Processing

Text preprocessing and tokenization: Stemming, lemmatization, and stop word removal
Part-of-speech tagging and named entity recognition: HMMs and CRFs
Syntax and parsing: Context-free grammars, dependency parsing, and constituency parsing
Sentiment analysis: Lexicon-based and machine learning approaches
Machine translation: Statistical and neural machine translation models
Text generation: Language models, sequence-to-sequence models, and transformer architectures
Question answering and dialogue systems: Information retrieval and conversational AI

Module 5: Computer Vision

Image processing fundamentals: Filters, edge detection, and morphological operations
Feature detection and matching: SIFT, SURF, and ORB algorithms
Object detection and recognition: R-CNN family, YOLO, and SSD
Image segmentation: Thresholding, region-based, and semantic segmentation
Face recognition: Eigenfaces, local binary patterns, and deep learning approaches
3D computer vision: Stereo vision, structure from motion, and SLAM
Image segmentation
Face recognition

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