Data Science with AI masters program

• Creating multi-dimensional arrays
• Shape, size, and dimensions of arrays
• Array reshaping and resizin

Array Operations

• Element-wise operations
• Aggregation and reduction functions
• Array broadcasting and vectorization

Array Manipulation

• Adding and removing elements in arrays
• Stacking and concatenating arrays
• Splitting arrays

Indexing and Slicing

• Boolean indexing and masking
• Fancy indexing and advanced slicing

Array Math and Statistics

• Mathematical functions in NumPy
• Statistical functions (mean, median, variance, etc.)
• Linear algebra with NumPy (dot product, matrix operations)

Random Number Generation

• Generating random data with NumPy
• Seeds and reproducibility

Handling Missing Data with NumPy

• Dealing with NaN values in NumPy arrays

File Input and Output

• Loading and saving NumPy arrays from/to files (CSV, text, binary)

Introduction to Data Science and Machine Learning

• Understanding the data science workflow
• Overview of machine learning and its applications
• Types of machine learning: supervised, unsupervised,
• and reinforcement learning

Data Preprocessing

• Data cleaning and handling missing values.
• Feature engineering and selection
• Data transformation and normalization

Supervised Learning Algorithms

• Linear regression
• Logistic regression
• Decision trees and random forests
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)
• Naive Bayes
• Gradient Boosting algorithms (e.g., XGBoost, LightGBM)

• K-means clustering
• Hierarchical clustering
• Dimensionality reduction techniques (e.g., Principal Component
• Analysis – PCA)

Model Evaluation and Validation

• Cross-validation techniques
• Performance metrics: accuracy, precision, recall, F1-score, etc.
• Overfitting and underfitting

Model Selection and Hyperparameter Tuning

• Grid search and randomized search for hyperparameter optimization

Ensemble Methods

• Bagging and bootstrap aggregating
• Stacking and blending

Introduction to Recommender Systems

• Collaborative filtering
• Content-based filtering

Introduction to Time Series Analysis

• Time series data handling
• Time series forecasting using machine learning

• Basics of neural networks: Neurons, activation functions, and backpropagation

• Introduction to TensorFlow and Keras libraries

• Building and training deep learning models for image classification and

• regression tasks

• Convolutional Neural Networks (CNN) for image analysis

• Recurrent Neural Networks (RNN) for sequential data processing

Natural Language Processing (NLP)

• Introduction to NLP and its applications

• Text preprocessing: Tokenization, stop word removal, stemming, and lemmatization

• Bag-of-Words (BoW) and TF-IDF representation

• Sentiment analysis and text classification using NLP techniques

Data Cleaning and Preparation

• Removing duplicates
• Handling missing values
• Data validation and data integrity
• Text-to-columns and data formatting
• Data transformation techniques

Advanced Formulas and Functions

• Logical functions (IF, AND, OR, etc.)
• Lookup and reference functions (VLOOKUP, HLOOKUP, INDEX-MATCH)
• Text functions (CONCATENATE, LEFT, RIGHT, MID, etc.)
• Date and time functions
• Statistical functions (AVERAGEIF, COUNTIF, SUMIF, etc.)

Data Analysis Tools

• PivotTables and PivotCharts
• Data filtering and sorting
• Subtotals and grouping data
• What-If analysis (Data tables, Goal Seek, Scenario Manager)

Data Visualization

• Creating charts and graphs (line charts, bar charts, pie charts, etc.)
• Customizing chart elements
• Advanced charting techniques (combination charts, sparklines, etc.)

Data Analysis with Excel

• Descriptive statistics (mean, median, standard deviation, etc.)
• Histograms and frequency distributions
• Correlation and regression analysis
• Data analysis using Excel’s Data Analysis ToolPak add-in

Data Import and Export

• Importing data from external sources (CSV, text files, databases, etc.)
• Exporting data to different file formats (CSV, PDF, etc.)

Automating Tasks with Macros

• Introduction to VBA (Visual Basic for Applications)
• Recording and running macros
• Creating custom functions and procedures

Data Integration with Excel

• Combining data from multiple sources
• Data consolidation techniques

Excel and Data Visualization Tools Integration

• Linking Excel data with Power BI or Tableau
• Importing and exporting data between Excel and Python/R

Excel Efficiency and Productivity

• Keyboard shortcuts and time-saving tips
• Excel add-ins and productivity tools

Introduction to SQL

• Overview of SQL and its applications in data science.
• Relational databases and their components.
• Basic SQL syntax and commands.

Querying Data

• SELECT statement and retrieving data from tables.
• Filtering data with the WHERE clause.
• Sorting data with the ORDER BY clause.
• Limiting results with the LIMIT clause.

Filtering and Combining Data

• Using logical operators (AND, OR, NOT) to create complex queries.
• Working with NULL values.
• Combining data from multiple tables with JOIN operations
• (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).

Data Aggregation

• Understanding aggregate functions (SUM, AVG, COUNT, MIN, MAX).
• GROUP BY clause and grouping data based on specific criteria.
• HAVING clause for filtering aggregated data.

Data Manipulation

• Inserting, updating, and deleting data in a database.
• Transactions and their importance.

Data Analysis with SQL

• Subqueries and their applications.
• Common table expressions (CTEs).
• Window functions for advanced data analysis.

Advanced SQL Techniques

• Working with date and time data.
• String manipulation and regular expressions.
• Conditional expressions and CASE statements.

Performance Optimization

• Understanding indexes and their impact on query performance.
• Query optimization techniques.

Data Import and Export

• Importing data into a SQL database.
• Exporting query results to external files.

Working with Large Datasets

• Partitioning data to improve performance.
• Using temporary tables and table variables.

Crop Yield Prediction for Precision Agriculture

Summary: Leveraging machine learning and historical agricultural data, this project predicts crop yields for different regions and seasons. It aids farmers in optimizing resource allocation and improving overall crop productivity. 

Domain: Agriculture, Remote Sensing.

Recommender System for E-commerce

Summary: Implement a personalized recommender system that suggests products to users based on their past behaviour and preferences. This project aims to enhance user engagement and increase sales in e-commerce platforms. 

Domain: Recommendation Systems.

Air Quality Prediction

Summary: Develop a machine learning model to predict air quality levels in each area based on historical data and environmental factors. This project aims to provide early warnings for potential air pollution events.
Domain: Environmental Sciences.

Natural Language Generation for Chatbots

Summary: Implement a natural language generation model to improve the conversational abilities of chatbots. The system will generate human-like responses, enhancing user interactions.
Domain: Natural Language Processing (NLP) and Chatbots.

Language Translation using Sequence-to-Sequence Models

Summary: This project involves implementing a sequence-to-sequence model for language translation tasks. By leveraging recurrent neural networks or transformers, the model can translate text from one language to another, facilitating cross-lingual communication in the natural language processing domain.

Climate Change Impact Analysis using Machine Learning

Summary: Build a machine learning model to analyse climate data and predict the potential impact of climate change on specific regions. The project contributes to climate research, environmental planning, and disaster management. 

Domain: Environmental Science, Climate Change Analysis