Spark Master | Combo Course

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Tecklearn’s Spark Master Combo Course includes the following Courses: • Apache Spark and Scala Certification • Data Science Training using R This Combo Training Course includes Data Science with R concepts such as data visualization, data manipulation, machine learning algorithms, charts, hypothesis testing, etc. through industry use cases, and real-time examples. Spark training lets you master real-time data processing using Spark streaming, Spark SQL, Spark RDD and Spark Machine Learning libraries (Spark MLlib, Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark....

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Why you take this Combo Training?

The Average salary of a Data Scientist in R is $123k per annum –

A recent market study shows that the Data Analytics Market is expected to grow at a CAGR of 30.08% from 2020 to 2023, which would equate to $77.6 billion.

The average salary for Apache Spark developer ranges from approximately $93,486 per year for Developer to $128,313 per year for Data Engineer. –


• What is Scala
• Why Scala for Spark
• Scala in other Frameworks
• Scala REPL
• Basic Scala Operations
• Variable Types in Scala
• Control Structures in Scala
• Loop, Functions and Procedures
• Collections in Scala
• Array Buffer, Map, Tuples, Lists

• Functional Programming
• Higher Order Functions
• Anonymous Functions
• Class in Scala
• Getters and Setters
• Custom Getters and Setters
• Constructors in Scala
• Singletons
• Extending a Class using Method Overriding

• Introduction to Spark
• How Spark overcomes the drawbacks of MapReduce
• Concept of In Memory MapReduce
• Interactive operations on MapReduce
• Understanding Spark Stack
• HDFS Revision and Spark Hadoop YARN
• Overview of Spark and Why it is better than Hadoop
• Deployment of Spark without Hadoop
• Cloudera distribution and Spark history server
[curriculum_content question="Basics of Spark"]
• Spark Installation guide
• Spark configuration and memory management
• Driver Memory Versus Executor Memory
• Working with Spark Shell
• Resilient distributed datasets (RDD)
• Functional programming in Spark and Understanding Architecture of Spark

• Challenges in Existing Computing Methods
• Probable Solution and How RDD Solves the Problem
• What is RDD, It’s Operations, Transformations & Actions Data Loading and Saving Through RDDs
• Key-Value Pair RDDs
• Other Pair RDDs and Two Pair RDDs
• RDD Lineage
• RDD Persistence
• Using RDD Concepts Write a Wordcount Program
• Concept of RDD Partitioning and How It Helps Achieve Parallelization
• Passing Functions to Spark

• Creating a Spark application using Scala or Java
• Deploying a Spark application
• Scala built application
• Creating application using SBT
• Deploying application using Maven
• Web user interface of Spark application
• A real-world example of Spark and configuring of Spark

• Concept of Spark parallel processing
• Overview of Spark partitions
• File Based partitioning of RDDs
• Concept of HDFS and data locality
• Technique of parallel operations
• Comparing coalesce and Repartition and RDD actions

• Why Machine Learning
• What is Machine Learning
• Applications of Machine Learning
• Face Detection: USE CASE
• Machine Learning Techniques
• Introduction to MLlib
• Features of MLlib and MLlib Tools
• Various ML algorithms supported by MLlib

• Why Kafka, what is Kafka and Kafka architecture
• Kafka workflow and Configuring Kafka cluster
• Basic operations and Kafka monitoring tools
• Integrating Apache Flume and Apache Kafka

• Why Streaming is Necessary
• What is Spark Streaming
• Spark Streaming Features
• Spark Streaming Workflow
• Streaming Context and DStreams
• Transformations on DStreams
• Describe Windowed Operators and Why it is Useful
• Important Windowed Operators
• Slice, Window and ReduceByWindow Operators
• Stateful Operators

• Learning about accumulators
• The common performance issues and troubleshooting the performance problems

• Need for Spark SQL
• What is Spark SQL
• Spark SQL Architecture
• SQL Context in Spark SQL
• User Defined Functions
• Data Frames and Datasets
• Interoperating with RDDs
• JSON and Parquet File Formats
• Loading Data through Different Sources

• Concept of Scheduling and Partitioning in Spark
• Hash partition and range partition
• Scheduling applications
• Static partitioning and dynamic sharing
• Concept of Fair scheduling
• Map partition with index and Zip
• High Availability
• Single-node Recovery with Local File System and High Order Functions

• Need for Data Science
• What is Data Science
• Life Cycle of Data Science
• Applications of Data Science
• Introduction to Big Data
• Introduction to Machine Learning
• Introduction to Deep Learning
• Introduction to R&R-Studio
• Project Based Data Science

• Introduction to R
• Data Exploration
• Operators in R
• Inbuilt Functions in R
• Flow Control Statements & User Defined Functions
• Data Structures in R

• Need for Data Manipulation
• Introduction to dplyr package
• Select (), filter(), mutate(), sample_n(), sample_frac() & count() functions
• Getting summarized results with the summarise() function,
• Combining different functions with the pipe operator
• Implementing sql like operations with sqldf()

• Loading different types of dataset in R
• Arranging the data
• Plotting the graphs

• Types of Data
• Probability
• Correlation and Co-variance
• Hypothesis Testing
• Standardization and Normalization

• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning algorithm: Linear Regression and Logistic Regression

• Intro to Logistic Regression
• Simple Logistic Regression in R
• Multiple Logistic Regression in R
• Confusion Matrix
• ROC Curve

• What are classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Naive Bayes?
• Support Vector Machine: Classification

• Decision Tree in R
• Information Gain
• Gini Index
• Pruning

• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Types of Recommendations
• User-Based Recommendation
• Item-Based Recommendation
• Difference: User-Based and Item-Based Recommendation
• Recommendation use cases

• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Training Option

Self-Paced Learning


  • Learn at your convenient time and pace
  • Gain on-the-job kind of learning experience through high quality Videos built by industry experts.
  • Interactive Sessions as good as Classroom experience.
  • Learn end to end course content that is similar to instructor led virtual/classroom training.
  • Cost Effective as well as Convenient.

Blended Learning

  • Everything in Self-Paced Plus
  • Learn in an instructor-led online training class
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Corporate Training

Customized to your team’s needs

  • Customized learning delivery model (self-paced and/or instructor-led)
  • Flexible pricing options
  • Enterprise grade learning management system (LMS)
  • Enterprise dashboards for individuals and teams
  • 24×7 learner assistance and support
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Course Description

Apache Spark and Scala Certification

This Spark certification training helps you master the essential skills of the Apache Spark open-source framework and Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. You will also understand the role of Spark in overcoming the limitations of MapReduce.  You will get an in-depth knowledge of these concepts and will be able to work on related demos. The course creates an understanding about how the industry uses Spark in Real-Time Projects.


The average salary for “apache spark developer” ranges from approximately $93,486 per year for Developer to $128,313 per year for Data Engineer. –

Wells Fargo, Microsoft, Capital One, Apple, JPMorgan Chase & many other MNC’s worldwide use Apache Spark across industries.

Global Spark market revenue will grow to $4.2 billion by 2022 with a CAGR of 67%

What you will Learn in this Course?

  • Apache Spark and Scala programming
  • Difference between Apache Spark and Hadoop
  • Scala and its programming implementation
  • Implementing Spark on a cluster
  • Writing Spark applications using Python, Java and Scala
  • RDD and its operation, along with the implementation of Spark algorithms
  • Defining and explaining Spark streaming
  • Scala classes concept and executing pattern matching
  • Scala–Java interoperability and other Scala operations
Data Science Training using R

Tecklearn’s Data Science using R Language Training develops knowledge and skills to visualize, transform, and model data in R language. It helps you to master the Data Science with R concepts such as data visualization, data manipulation, machine learning algorithms, charts, hypothesis testing, etc. through industry use cases, and real-time examples. Data Science course certification training lets you master data analysis, R statistical computing, connecting R with Hadoop framework, Machine Learning algorithms, time-series analysis, K-Means Clustering, Naïve Bayes, business analytics and more.

What you will Learn in this Course?

  • Introduction to Data Science and R Language
  • Data Manipulation and Data Visualization
  • Introduction to Machine Learning
  • Classification Techniques
  • Predictive analytics and segmentation using clustering
  • Roles and responsibilities of a Data Scientist
  • Using real-world data sets to deploy recommender systems

Key Features

Self-Paced Online Video

• Self-paced Videos: 54 Hrs
• Exercises & Project Work: 106 Hrs
• A 360-degree learning approach that you can adapt to your learning style

1 Year Unlimited Access

You get 1 Year unlimited access to LMS where presentations, quizzes, installation guide & class recordings are there.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket-based tracking system


Successfully complete your course and Tecklearn will provide you Course Completion Certificate.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Data Science and Spark Concepts.

Learn at your Convenience

• Certification and Job Assistance
• Flexible Schedule


Spark Master

Vijay Choudhary

Spark Master

Nisha Das

The content is very apt and upto the mark. All the assignments and practicals gives very good understanding of the concepts with the hands o... Read More

Spark Master


Spark Master

Md Parwez Alam

Course content is as per industry standards . Self Paced training with interacive learning did an excellent job of explaining the concepts a... Read More

Spark Master


I liked the concept of online learning. The training delivery is seamless and flawless - Thanks! Keep up the good work. I especially like th... Read More


This course is designed for clearing the following Certifications:

  • Apache Spark component of the Cloudera Spark and Hadoop Developer Certification (CCA175) exam.

As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly. Tecklearn’s Course Completion Certificate for the courses included in this combo course will be awarded upon the completion of the course.


  • To put your knowledge on into action, you will be required to work on various industry-based projects that discuss significant real-time use cases.
  • These projects are completely in-line with the modules mentioned in the curriculum and help you to clear the certification exam.

FAQ Content

You will never miss a lecture at Tecklearn. Tecklearn provides recordings of each class so you can review them as needed before the next session.

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover, the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

All the instructors at Tecklearn are practitioners from the Industry with minimum 10-15 years of relevant IT experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

Learning pedagogy has evolved with the advent of technology. Online training adds convenience and quality to the training module. With our 24x7 support system, our online learners will have someone to help them all the time even after the class ends. This is one of the driving factors to make sure that people achieve their end learning objective. We also provide life-time access of our updated course material to all our learners.

Tecklearn actively provides placement assistance to all learners who have successfully completed the training. We also help you with the job interview and resume preparation part as well.
  • 17,999.00
  • 10 years, 1 month
  • Course Certificate

Course Curriculum

No curriculum found !

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