Arbor Academy

Data Science

Overview
Training Features

Course Overview

The Data Science course at Arbor Academy is a deep dive into the world of data-driven technologies, combining programming, statistics, and machine learning to prepare you for high-demand roles such as Data Scientist, ML Engineer, or AI Analyst. This program equips you to analyze complex data, build predictive models, and uncover insights that help businesses make informed decisions. No prior coding experience is required—this course is designed to take you from beginner to job-ready.

Description

The course begins by building a strong foundation in Python for data handling, moving into statistical analysis and visual storytelling. You'll gain expertise in cleaning and transforming datasets, performing exploratory data analysis (EDA), and applying ML algorithms for predictions. With hands-on labs, assignments, and capstone projects, you’ll work with real industry datasets such as e-commerce, healthcare, banking, or HR analytics.

This program is aligned with industry standards and uses popular tools and libraries, ensuring that you not only understand the theory behind data science but can apply it practically in interviews and on the job. The course also includes coding challenges, mock interviews, and resume workshops tailored for data science roles.

Course Objectives

By completing this course, you’ll be able to:

Write Python code to clean, manipulate, and analyze data
Use NumPy and Pandas to work with arrays, series, and dataframes
Apply statistical methods (mean, median, standard deviation, correlation, regression)
Create data visualizations using Matplotlib and Seaborn
Build supervised ML models (linear/logistic regression, decision trees)
Work with unsupervised learning models (K-means, clustering, dimensionality reduction)
Evaluate model accuracy using confusion matrices, ROC curves, and cross-validation
Design end-to-end data science workflows using real business case studies
Prepare a complete data science project portfolio for job interviews

Prerequisites

- Basic understanding of mathematics and logical reasoning

- No prior programming or machine learning knowledge required

- Willingness to work on structured problem-solving and data experimentation

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Course Curriculum

Introduction to Excel
- Entering and Editing Text and Formulas Working with Basic
- Excel Functions
Data Analysis Techniques
- Statistical Analysis using Excel
- Graphical Techniques using Excel Hypothesis Testing using Excel
Data Management
- Working with an Excel List
- Data Validation Pivot Tables
ChatGPT for Excel and Data Analysis
- Introduction and Integration of ChatGPT for Excel
- Utilizing ChatGPT for Advanced Data Analysis and Insights

Introduction to Python
- Python Basics
- Python Packages
Data Handling with Python
- Pandas: Series, DataFrames
- Numpy: Arrays, Matrices, Broadcasting
Python and MySQL Integration
- Python Connectivity to MySQL
- SQL Operations: INSERT, READ, DELETE, UPDATE, COMMIT, ROLLBACK using Python
Data Visualization
- Matplotlib
- Seaborn Data
- Visualizations using Python Package (Line, Bar, Histogram, Pie, Box, Scatter Plots)
- Additional Visualization Packages (Bokeh, ggplot, Plotly)
Development Environments
- Jupyter Notebooks
- Python Notebooks

Introduction to Databases
- What is a Database?
- Types of Databases
- DBMS vs RDBMS DBMS
- Architecture
SQL Fundamentals
- SQL Data Types
- SQL Commands
- SQL Operators
Database Management
- Installing PostgreSQL
- Installing MySQL
- DBMS Language
Advanced SQL
- SQL Keys
- SQL Joins
- GROUP BY, HAVING, ORDER BY
- Views in SQL
- SQL Functions

Introduction to Power BI
- What is Power BI?
- Overview and Architecture
- Installation and Plans
Data Transformation
- Importing Data
- Data Types
- Basic Transformations
- Managing Query Groups
- Splitting and Changing Data Types
- Working with Dates
- Connecting to Files
Data Visualization
- Introduction to Visualization
- Introduction to DAX
- Writing DAX
- Types of Functions in DAX
- Creating Calculated Measures
Charts and Visuals
- Pie and Doughnut Charts
- Treemap
- Combo Charts
- Filters and Slicers
- Focus Mode and Data Viewing
- Tables and Matrices
- Gauges, Cards, and KPIs
- Custom Visuals
Integration and Analytics
- Python and R Integration Analytics Panel
Dashboards
- Overview and Creation Uploading and Sharing Quick Insights
Additional Features
- Q&A and Featured Questions In-Focus Mode
Summary and Wrap-Up
- Course Summary

Clustering Segmentation
A. Clustering Segmentation
- Basics of Clustering
- Types of Clustering
- Distance Metrics
- Elbow Method for Optimal Clusters
- Silhouette Analysis
- Case Studies and Applications
B. Clustering Segmentation
- K-means Clustering
- Hierarchical Clustering
- DBSCAN
- Gaussian Mixture Models
- Evaluating Clustering Performance
- Clustering for Market Segmentation
Dimension Reduction PCA & SVD
- Principal Component Analysis (PCA)
- Eigenvalues and Eigenvectors
- Explained Variance
- Singular Value Decomposition (SVD)
- Matrix Factorization
- Applications of PCA and SVD
- Visualization Techniques
Association Rules
- Basics of Association Rules
- Support, Confidence, and Lift
- Apriori Algorithm
- Market Basket Analysis
- Applications of Association Rules
Recommendation Engine
- Collaborative Filtering
- Content-based Filtering
- Evaluating Recommendation Systems
- Case Studies and Applications
Network Analytics
- Basics of Network Analytics
- Graph Theory Concepts
- Centrality Measures
- Degree, Betweenness, Closeness, Eigenvector Centrality
- Community Detection
- Network Visualization
- Applications in Social Networks
Text Mining
- Text Preprocessing
- Tokenization
- Stop Words Removal
- Lemmatization and Stemming
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Text Classification
- Sentiment Analysis Topic Modeling LDA (Latent Dirichlet Allocation)
- Applications of Text Mining
Naive Bayes
- Basics of Naive Bayes Classifier
- Gaussian Naive Bayes
- Advantages and Disadvantages
- Applications in Spam Filtering, Text Classification
KNN (k-Nearest Neighbors)
- Basics of k-Nearest Neighbors
- Distance Metrics
- Choosing the Value of K
- Weighted KNN
- Advantages and Disadvantages
- Applications in Pattern Recognition
Decision Tree
- Basics of Decision Trees
- Information Gain and Gini Index
- Pruning Methods
- Overfitting and Underfitting
- Decision Tree for Classification and Regression
- Applications and Case Studies
Ensemble Models
- Basics of Ensemble
- Learning Bagging
- Random Forest
- Boosting
- AdaBoost, Gradient Boosting
- Stacking
- Blending
- Applications and Advantages
Simple Linear Regression
- Basics of Linear Regression
- Least Squares Method
- Assumptions of Linear Regression
- Residual Analysis
- Evaluating Model Performance
- Applications
Multiple Linear Regression
- Basics of Multiple Linear Regression
- Multicollinearity
- Feature Selection
- Model Evaluation
- Interaction Effects
- Applications
Logistic Regression
- Basics of Logistic Regression
- Sigmoid Function
- Odds and Log Odds
- Assumptions of Logistic Regression
- Model Evaluation
- Applications in Binary Classification
Survival Analytics
- Basics of Survival Analysis
- Kaplan-Meier Estimator
- Cox Proportional Hazards Model
- Survival and Hazard Functions
- Applications in Medical Research
- Case Studies
Forecasting
- Time Series Analysis
- ARIMA Models
- Exponential Smoothing
- Seasonal Decomposition
- Prophet
- Applications in Demand Forecasting
Exam / Assignment / Project
- Basics of Logistic Regression
- Sigmoid Function
- Odds and Log Odds
- Assumptions of Logistic Regression
- Model Evaluation
- Applications in Binary Classification

Deep Learning & AI - CNN (Convolutional Neural Networks)
- Basics of CNN
- Convolutional Layers
- Pooling Layers
- Fully Connected Layers
- Applications in Image Recognition
- Case Studies
Deep Learning & AI - RNN (Recurrent Neural Networks)
- Basics of RNN
- Long Short-Term Memory (LSTM)
- Gated Recurrent Units (GRU)
- Sequence Modeling
- Applications in Time Series, Natural Language Processing
- Case Studies
Natural Language Processing (NLP)
- Basics of NLP
- Named Entity Recognition (NER)
- Part-of-Speech Tagging
- Text Generation
- Applications in Chatbots, Machine Translation

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Who can learn this course

This course is a great fit for:

Students and graduates from STEM backgrounds (BSc, MSc, BCA, BTech, etc.)
IT professionals or developers shifting toward AI/ML careers
Working professionals in operations, finance, or consulting wanting data upskilling
Analysts or BI professionals who want to transition into Data Science roles
Anyone looking to enter the AI and Machine Learning job market

Training Features

Comprehensive Curriculum

Master web development with a full-stack curriculum covering front-end, back-end, databases, and more.

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Hands-On Projects

Apply skills to real-world projects for practical experience and enhanced learning.

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Expert Instructors

Learn from industry experts for insights and guidance in full-stack development.

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Job Placement Assistance

Access job placement assistance for career support and employer connections.

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Certification upon Completion

Receive a recognized certification validating your full-stack development skills.

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24/7 Support

Access round-the-clock support for immediate assistance, ensuring a seamless learning journey.

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