Online Data Engineer Training – Free Tableau / MS Power BI

Module 1.1: Introduction to Python Programming

Purpose: Build a strong foundation in Python syntax and programming concepts essential for data analysis.

Topics Covered

• Introduction to Python What is Python and why it’s popular for Data Analysis Installing Python, Jupyter Notebook, and Anaconda
• Python Basics Data Types: int, float, str, bool Variables and Constants Input and Output operations Type Conversion and Casting
• Operators Arithmetic, Logical, Comparison, and Assignment Operators
• Control Flow Conditional Statements: if, elif, else Loops: for and while Loop Control Statements: break, continue, pass

Module 1.2: Data Structures in Python

Purpose: Learn how to store, organize, and manipulate data efficiently using Python’s built-in structures.

Topics Covered

• Core Data Structures Lists, Tuples, Sets, Dictionaries
• Operations Indexing and Slicing Adding, Updating, and Removing Elements
• Comprehensions List Comprehension Dictionary Comprehension
• Common Built-in Functions len(), max(), min(), sum(), sorted(), type()

Module 1.3: Functions, Modules, and Packages

Purpose: Learn how to write reusable and modular Python code for analytical workflows.

Topics Covered

• Functions
  • Defining and Calling Functions
  • Return Statements and Parameters
  • Default, Positional, and Keyword Arguments
• Modules
  • Using Built-in Modules (math, datetime, os, random)
  • Creating and Importing Custom Modules
• Packages Installing and Managing Packages using pip

Module 1.4: Working with Files

Purpose: Learn how to import, read, write, and manage external datasets effectively.

Topics Covered

• File Handling Basics — open(), read(), write(), close()
• Reading and Writing Files (CSV, TXT)
• Working with File Paths using os and pathlib
• Writing Processed Data to Files

Module 1.5: Introduction to Data Analysis with Pandas

Purpose: Master the most widely used Python library for real-world data manipulation and analysis.

Topics Covered

• Pandas Basics Introduction to Series and Data Frame
• Reading & Writing Data read_csv(), to_csv(), read_excel(), to_excel()
• Exploring Data head(), .tail(), .info(), .describe()
• Selecting & Filtering Data .loc[], .iloc[] for row/column selection Conditional filtering (df[df[‘column’] > value])

Data Cleaning

• Handling Missing Data (fillna, dropna)
• Adding, Renaming, and Dropping Columns
• Sorting and Grouping Data (sort_values, groupby)

Module 1.6: Data Visualization with Matplotlib & Seaborn

Purpose: Develop the ability to present data visually for better insights and reporting.

Topics Covered

• Introduction to Matplotlib – Basic Plot Types: Line, Bar, Pie, Histogram, Scatter Customizing Visuals (titles, labels, colors, legends)
• Seaborn for Statistical Visualization – Plot Types: barplot, countplot, boxplot, heatmap, pairplot
• Styling and Aesthetics
• Exporting Charts Saving visuals in .png and .pdf formats

Module 1.7: Data Cleaning & Preprocessing

Purpose: Learn how to prepare raw datasets for analysis — an essential industry skill.

Topics Covered

• Handling Missing Values
• Detecting & Removing Duplicates
• Changing Data Types (astype())
• String Operations on Columns (.str.upper(), .str.strip(), .str.replace())
• Date Conversion using pd.to_datetime()
• Identifying and Removing Outliers
• Basic Feature Engineering for better model performance

Module 1.8: Introduction to NumPy

Purpose: Learn efficient numerical computing — the backbone of Python data analytics.

Topics Covered

• NumPy Basics
• Arrays vs Lists
• Creating and Manipulating Arrays
• Array Operations
• Indexing, Slicing, and Broadcasting Vectorized Computations
• Mathematical Functions mean(), median(), std(), sum(), sqrt(), dot()
• Random Module Generating Random Numbers (np.random.rand(), np.random.randint())

Module 1.9: Mini Projects & Practical Assignments

Purpose: Apply your learning to hands-on, real-world projects.

Mini Projects

• Sales Data Analysis (Pandas + Matplotlib) Cleaning and visualizing monthly sales data
• COVID Data Dashboard Using Pandas and Seaborn to visualize cases and recovery trends
• Customer Segmentation (CSV File Analysis) Filtering, grouping, and reporting based on demographic data

Module 3.1: Introduction to Machine Learning

Purpose: Understand the foundations, workflow, and use cases of Machine Learning in the real world.

Topics Covered

• What is Machine Learning?
• Difference between AI, ML, and Deep Learning

Types of Machine Learning:

• Supervised
• Unsupervised
• Reinforcement Learning (overview)

Real-world Applications of ML (Finance, Healthcare, Retail, etc.)

Machine Learning Workflow:

Problem Definition → Data Collection → Preprocessing → Modeling → Evaluation → Deployment

Module 3.2: Python for Machine Learning

Purpose: Build programming readiness for ML with essential Python tools.

Topics Covered

• Quick Revision of Python Data Analysis
• Key Libraries for ML:

NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn

• Data Importing and Exploration
• Descriptive Statistics & Correlation Analysis
• Feature Selection and Importance Overview

Module 3.3: Data Preprocessing & Feature Engineering

Purpose: Prepare raw data for training accurate and reliable models.

Topics Covered

• Handling Missing Values (SimpleImputer, fillna)
• Removing Duplicates and Outliers
• Encoding Categorical Data:
  • One-Hot Encoding
  • Label Encoding
• Feature Scaling:
  • Standardization (Z-score)
  • Normalization (MinMaxScaler)
• Splitting Data (Train-Test Split)

Module 3.4: Supervised Learning — Regression Algorithms

Purpose: Build models that predict continuous outcomes.

Topics Covered

• Simple Linear Regression Understanding line of best fit Evaluation metrics: MAE, MSE, RMSE, R²
• Multiple Linear Regression Handling multiple predictors

Module 3.5: Supervised Learning — Classification Algorithms

Purpose: Train models to classify or categorize data points.

Topics Covered

• Logistic Regression Sigmoid function and decision boundary
• K-Nearest Neighbors (KNN) Distance metrics and K-value selection
• Decision Trees Gini impurity and information gain
• Random Forest Classifier Ensemble learning and feature importance
• Support Vector Machines (SVM) Hyperplanes and kernel tricks
• Naïve Bayes Classifier Probability-based classification

Module 3.6: Unsupervised Learning Algorithms

Purpose: Discover hidden patterns and relationships in unlabeled data.

Topics Covered

• Clustering
  • K-Means Clustering
  • Hierarchical Clustering (Dendrograms)
  • DBSCAN (Density-Based Clustering)
• Dimensionality Reduction PCA (Principal Component Analysis) t-SNE (for visualization)
• Association Rule Learning Apriori Algorithm Market Basket Analysis

Module 3.7: Model Evaluation, Optimization & Validation

Purpose: Improve model accuracy and generalization.

Topics Covered

• Train-Test Split and Cross Validation (K-Fold CV)
• Bias-Variance Tradeoff
• Grid Search & Random Search for Hyperparameter Tuning
• Ensemble Learning Concepts: Bagging and Boosting

Module 3.8: Advanced Topics in Machine Learning

Purpose: Get exposure to advanced tools and concepts used in modern ML.

Topics Covered

• Introduction to Pipelines in Scikit-learn
• Feature Selection Techniques (Recursive Feature Elimination)
• Time Series Forecasting (Intro)
• Handling Large Datasets
• Saving and Loading Models (pickle, joblib)

Module 3.9: Model Deployment (Basics)

Purpose: Learn how ML models are integrated into real-world systems.

Topics Covered

• Exporting Models with joblib and pickle
• Deploying Models Locally or on Web
• Introduction to APIs and Integrations

Module 3.10: Real-World Machine Learning Projects

Purpose: Apply all concepts to realistic datasets and business problems.

Projects

• Predictive Analytics (Regression) Predict House Prices based on area, location, and features.
• Classification Project Credit Card Fraud Detection or Employee Attrition Prediction.
• Unsupervised Learning Customer Segmentation using K-Means and PCA.

2+ End-to-End ML Projects

Machine Learning Interview Questions

Introduction to Neural Networks

• Perceptrons
• Neural Networks
• Hidden Layers
• Keras
• Forward & Backward Propagation
• Multilayer Perceptrons (MLP)
• Callbacks
• Tensorboard
• Optimization
• Hyperparameter tuning
• Computer Vision

Convolutional Neural Nets

• Data Augmentation
• Transfer Learning
• CNN
• CNN Hyperparameters Tuning & BackPropagation
• CNN Visualization
• Popular CNN Architecture – Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
• Object Segmentation, Localisation, & Detection
• Natural Language Processing

Text Processing & Representation

• Tokenization, Stemming, Lemmatization
• Vector space modelling, Cosine Similarity, Euclidean Distance
• POS tagging, Dependency Parsing
• Topic Modelling, Language Modelling
• Embeddings
• Recurrent Neural Nets
• Information Extraction
• LSTM
• Named Entity Recognition)

Course on Large Language Models and Generative AI

• Introduction to Large Language Models
• Generative Pre-Trained Transformer(GPT)
• OpenAI ChatGPT
• Vector Databases
• Langchain
• Stable Diffusion
• Prompt Engineering
• Gen AI with APIs

Mini Project

Module 1: Data Warehousing Concepts (Foundation)

• OLTP vs OLAP
• Data Warehouse vs Data Lake vs Data Lakehouse
• Cloud Data Warehouse Architecture
• Dimensional Modelling (Star vs Snowflake Schema)
• Fact & Dimension Tables
• Slowly Changing Dimensions (SCD Types 0–6)
• Data Mart Concepts
• Data Governance Basics

Module 2: Introduction to Snowflake

• What is Snowflake? Why Snowflake?
• Snowflake Cloud Architecture (Hybrid Shared-Disk + Shared-Nothing)
• Snowflake Editions & Cloud Platforms (AWS/Azure/GCP)
• Virtual Warehouse vs Database vs Schema Design
• Storage, Compute & Services Layer
• Snowflake UI (Web, Snowsight)
• Pricing & Billing Model

Module 3: Snowflake Database Essentials

• Creating Databases, Schemas, Tables
• Clustering & Micro-partitioning
• Data Types & Constraints
• Roles, Users & Access Control
• Time Travel & Fail-safe

Module 4: Data Loading & Unloading

• Data Staging (Internal/External)
• File Formats — CSV, JSON, Parquet, Avro, ORC
• COPY INTO commands
• Snowpipe (Real-time ingestion)
• Integration with AWS S3 / Azure Blob / GCP Storage
• Bulk & Incremental Data Loads

Module 5: Data Warehouse Modeling in Snowflake

• Fact & Dimension Table Implementation
• SCD Implementation (Types 1, 2, 3 with Merge)
• Hierarchies & Aggregation Tables
• Handling Slowly-Changing Fact Data

Module 6: Semi-Structured Data & JSON Handling

• VARIANT, OBJECT, ARRAY Data Types
• Flattening JSON
• Working with XML, Parquet
• Query Performance on Semi-structured data

Module 7: Querying, Performance Tuning & Optimizations

• Query Profiling & EXPLAIN plans
• Warehouse Scaling / Auto-suspend / Auto-resume
• Materialized Views
• Caching Layers (Result/Metadata/Local Cache)
• Partitioning & Clustering keys

Module 8: Data Sharing & Collaboration

• Secure Data Sharing
• Reader Accounts
• Marketplace Integration & Data Exchange
• Data Masking Policies

Module 9: Snowflake Security & Administration

• RBAC framework (Roles & Privileges)
• Network Policies & MFA
• Encryption & Key Management
• Resource Monitoring & Cost Optimization
• Metadata Management

Module 10: ETL/ELT Integration

• Using DBT for transformations
• Integrating with Informatica, Talend, ADF, Glue
• Performance Best Practices in ELT pipelines
• Stream & Tasks — automation scheduling

Module 11: Advanced Features

• Zero-Copy Cloning
• Multi-Cluster Warehouses
• Failover & Replication
• Snowpark (Python) — Intro
• Stored Procedures & UDFs
• Workload Management