#sample resume format

[cvmantra001]

Live Online Apache Spark Course for Data Science

Duration: 6 Weeks | Total Time: 36 Hours

Format: Live online sessions using Google meet or MS Teams with hands-on coding, mini-projects, and a capstone project by an industry expert. Target Audience: College Students, Professionals in Finance, HR, Marketing, Operations, Analysts, and Entrepreneurs Tools Required: Laptop with internet Trainer: Industry professional with hands on expertise

Week 1: Introduction & Foundations (6 hrs)

Introduction to Big Data & Spark (2 hrs)

Evolution from Hadoop to Spark

Why Spark for Data Science?

Spark ecosystem overview (Spark Core, SQL, MLlib, Streaming, GraphX)

Real-world use cases

2. Spark Architecture & Setup (2 hrs)

Spark architecture (Driver, Executors, Cluster Manager)

RDD vs DataFrames vs Datasets

Installing & running Spark (Standalone, YARN, Databricks, Google Colab, Jupyter)

3. Hands-on with Spark Shell & PySpark (2 hrs)

Spark Shell (Scala/Python) basics

Using PySpark with Jupyter Notebook

Simple Spark applications

Week 2: Spark Core — RDD Operations (6 hrs)

RDD Basics (2 hrs)

Creating RDDs

Transformations & Actions

Lazy evaluation & DAG

2. Advanced RDD Operations (2 hrs)

Map, FlatMap, Filter, ReduceByKey, GroupByKey

Joins & Aggregations

Persisting & caching RDDs

3. Hands-on RDD Case Study (2 hrs)

Word Count Example

Log File Analysis

Performance tuning with RDDs

Week 3: DataFrames & Spark SQL (6 hrs)

Introduction to DataFrames (2 hrs)

Creating DataFrames from files (CSV, JSON, Parquet)

Schema & Data types

DataFrame operations (select, filter, groupBy, join, agg)

2. Spark SQL (2 hrs)

Registering DataFrames as SQL tables

Writing SQL queries in Spark

Integration with BI tools

3. Hands-on Data Analysis with Spark SQL (2 hrs)

Case study: Analyzing large dataset with DataFrames & SQL

Optimization techniques (Catalyst Optimizer, Tungsten)

Week 4: Machine Learning with MLlib (6 hrs)

Introduction to Spark MLlib (2 hrs)

Machine Learning in Spark

MLlib vs Scikit-learn

Pipelines & Transformers

2. Supervised Learning with MLlib (2 hrs)

Regression & Classification (Linear Regression, Logistic Regression, Decision Trees, Random Forest)

Model training & evaluation

3. Unsupervised Learning with MLlib (2 hrs)

Clustering (K-Means, Gaussian Mixture)

Dimensionality Reduction (PCA)

Hands-on project with MLlib

Week 5: Spark Streaming & Real-Time Analytics (6 hrs)

Introduction to Spark Streaming (2 hrs)

Batch vs Streaming

DStreams & Structured Streaming basics

Streaming architecture

2. Structured Streaming Operations (2 hrs)

Reading real-time data (Kafka, Socket, Files)

Window operations

Aggregations & checkpoints

3. Hands-on Streaming Project (2 hrs)

Real-time Twitter sentiment analysis / Log monitoring

Building streaming pipeline

Week 6: Capstone Project & Deployment (6 hrs)

GraphX & Advanced Topics (2 hrs)

Basics of GraphX

Graph analysis use cases in Data Science

2. Capstone Project Work (2 hrs)

End-to-end project (e.g., Movie Recommendation, Customer Churn Prediction, Real-time Fraud Detection)

Data ingestion → Processing → ML pipeline → Results

3. Deployment & Wrap-up (2 hrs)

Deploying Spark jobs (Standalone / Cluster)

Integrating with Hadoop, AWS EMR, Databricks

Best practices & course recap

Outcome: By the end of this course, learners will be able to:

Build and optimize Spark applications

Perform large-scale data analysis using Spark SQL

Train ML models using Spark MLlib

Work with streaming data in real-time

Deploy Spark solutions in production

Here you can see Important Links:-

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