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Scala Programming for Data Science in collaboration with IBM

  1. 4.5
  2. (535 ratings)
  • 1200+ Learners
  • English

Scala Programming for Data Science in collaboration with IBM

  1. $572
    1200+ Learners
Features
  • 100+ hours of learning
  • Practice Test Included
  • Certificate of completion
  • Skill level

What you'll learn

You will learn to make use of the Scala language to access databases, clean, analyze, and visualize data with Scala. Through our guided lectures and access to labs, you will get hands-on experience tackling fascinating data issues. This's an action-packed learning path for data science enthusiasts who wish to work on real-life problems with Scala.

Universally Recognized Certificates

From IBM and DataTrained

Capstone and Real Life Projects

Access to 15 real life projects and a capstone project

Analytics Jobs Placement Assistance

Access to analyticsjobs.in curated jobs

Access to in-demand Tools

IBM Watson labs and $1200 equivalent Cloud Credits

Programming Languages and Tools Covered

Detailed Syllabus of Scala Programming for Data Science Course

Learn the foundations of the language for developers and data scientists interested in using Scala for data analysis. Tackle data analysis problems involving Big Data, Scala and Spark. Get a solid understanding of the fundamentals of the language, the tooling, and the development process. Develop a good appreciation of more advanced features.

Course Content
Module 1 - Introduction
  • Introduction to Scala
  • Creating a Scala Doc
  • Creating a Scala Project
  • The Scala REPL
  • Scala Documentation
Module 3 - Case Objects and Classes
  • Companion Objects
  • Case Classes and Case Objects
  • Apply and Unapply
  • Synthetic Methods
  • Immutability and Thread Safety
Module 5 - Idiomatic Scala
  • 1. For expressions
  • 2. Pattern Matching
  • 3. Handling Options
  • 4. Handling Failures
  • 5. Handling Futures
Module 2 - Basic Object Oriented Programming
  • Classes
  • Immutable and Mutable Fields
  • Methods
  • Default and Named Arguments
  • Objects
Module 4 - Collections
  • Collections overview
  • Sequences and Sets
  • Options
  • Tuples and Maps
  • Higher Order Functions

Learn the history of Apache Spark™, how to build applications with Spark, how to establish an understanding of RDDs and Data Frames, and other advanced Spark topics.

  • Be prepared to leverage the core RDD and DataFrame APIs to perform analytics on datasets with Scala.
  • Get an overview of Spark and its associated ecosystem.
  • Gain enough skills to leverage the Map-Reduce framework with the Scala language.
Course Content
Module 1 - What is Spark? Module 3 - Introduction to Data Frames Module 5 - Introduction to Spark MLlib
Module 2 - Introduction to RDDs Module 4 - Advanced Spark Topics

In this course you will learn about Basic statistics and data types, Preparing data, Feature engineering, Fitting a model and Pipelines and grid search. Apache Spark™ is a fast and general engine for large-scale data processing, with built-in modules for streaming, machine learning and graph processing. This course shows you how to use Sparks' machine learning pipelines to fit models and search for optimal hyperparameters using a Spark cluster.

Course Content
Module 1 - Basic Statistics and Data Types
  • Vectors and Labelled Points
  • Local and Distributed Matrices
  • Summary Statistics, Correlations, and Random Data
  • Sampling
  • Hypothesis Testing
Module 2 - Preparing Data
  • Statistics, Random data and Sampling on Data Frames
  • Handling Missing Data and Imputing Values
  • Transformers and Estimators
  • Data Normalization
  • Identifying Outliers
Module 3 - Feature Engineering
  • Feature Vectors
  • Categorical Features
  • Using Explode, User Defined Functions, and Pivot
  • Principal Component Analysis (PCA) in Feature Engineering
  • Formulas
Module 5 - Pipeline and Grid Search
  • Predicting Grant Applications: Introduction
  • Predicting Grant Applications: Creating Features
  • Predicting Grant Applications: Building a Pipeline
  • Predicting Grant Applications: Cross Validation and Model
  • Tuning Predicting Grant Applications: Wrapping up
Module 2 - Preparing Data
  • Statistics, Random data and Sampling on Data Frames
  • Handling Missing Data and Imputing Values
  • Transformers and Estimators
  • Data Normalization
  • Identifying Outliers
Module 4 - Fitting a Model
  • Decision Trees
  • Random Forests
  • Gradient-Boosting Trees
  • Linear Methods
  • Evaluation

Comprehensive Curriculum

The curriculum has been designed by faculty from IITs, IBM and Expert Industry Professionals.

100+

Hours of Content

80+

Live Sessions

15

Tools and Software

Instructors

Learn from India’s leading Software Engineering faculty and Industry leaders

Saeed Aghabozorgi - Data Scientist, IBM

Saeed Aghabozorgi

Data Scientist, IBM

Saeed Aghabozorgi, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge.

Rav Ahuja - Senior Manager, IBM

Rav Ahuja

Senior Manager, IBM

Rav Ahuja is a Senior Manager with IBM Canada Lab specializing in AI, Data Science and Big Data analytics. He is part of the Emerging Technologies team and is involved in incubating solutions for Data Scientists and Analytics Professionals.

Raul F. Chong- Senior Program Manager, IBM

Raul F. Chong

Senior Program Manager, IBM

Raul F. Chong is a Senior Program Manager based at the IBM Toronto Laboratory. Raul joined IBM in 1997 and has held numerous positions in the company. Raul has taught many DB2 workshops, has published numerous articles, and has contributed to the DB2 Certification exam tutorials.

Grant Hutchison - Senior Engineer, IBM

Grant Hutchison

Senior Engineer, IBM

Grant Hutchison had worked with IBM for 18 years as Senior Engineer and Manager at IBM Canada (1991-2009). He held various roles including: Software Development, Support, Quality Assurance, Marketing, Sales, Training, and Product Management.

Frequently Ask Questions

Yes, there are multiple Scala certifications available. However, we provide IBM Scala certification at affordable prices. Our mission is to provide quality education at an affordable with the best counseling session and industry interface.

You’ll get 3 certifications after completing this course that will add an edge to your resume.

  • Learning path and certification from IBM
  • Course completion certification from DataTrained
  • Project completion certification from DataTrained

Our job is to provide you with quality education and assist your new journey. We want our students to get a job before they finish the course for that we provide career assistance.

Scala is a high-level programming language that combines object-oriented and functional programming. Scala's static focus on enhancing complicated applications avoids bugs, and its JVM and JavaScript runtimes allow you to construct high-performance systems with simple access to a vast library ecosystem.

However, Apache Spark is one of the most compelling reasons to learn Scala for machine learning. Scala may be used in combination with Apache Spark to handle enormous amounts of data, often known as Big Data.

The learning path of Scala should be in steps such as learning basics, its applications, jargon, etc. Our IBM scala course is structured and defines a straight path to learn. Let's have a look at the module of our IBM Scala course

  • Course 1. Scala Fundamentals
  • Course 2. Spark Overview for Scala Analytics
  • Course 3. Data Science for Scala

IBM Developer Skills Network Badge Program is the initiative of awarding students with a digital badge who pass their exam. A digital badge is a global recognition of technical skills that can be shared on social, and professional networking sites.

Advantages of digital badges:

  • High employment opportunities
  • Globally recognized
  • Easy sharing

Getting a badge from a reputed organization that has a foot in the global market will make a strong impression on your career and your resume.

Scala is good for data science. However, Python and R libraries are comprehensive but scala has its own advantages over python and R. These are advantages are:

  • Scala is 10 times faster than python
  • Scala’s framework and libraries are more powerful
  • Scala is good for working with big data
  • Scala makes use of spark effectively
  • It is easy to find complied errors in Scala

The length of time required to learn Scala is affected by whether or not you are already familiar with Java. Scala will take you around a month to learn if you already know Java. It will take you two to three months to grasp Scala if you are not already familiar with it.

According to several software professionals, Scala has a high learning curve. In Scala, there are numerous ways to achieve things, and it's a language that encourages creativity and flexibility. If you're a skilled programmer, and specifically you're familiar with Java, this may be quite useful. Working with a language that offers several ways of achieving the same thing might be tough if you are just getting started with programming.

Scala certification cost depends on the location and academy. Our IBM Scala certification is very cheap and affordable, however, we have not compromised on quality. Our Scala certification cost ₹19999.

Our mission is to provide quality education at an affordable with the best counseling session and industry interface. The benefits of enrolling in our course :

  • Universally recognized certification from IBM and DataTrained
  • Access to 15 real-life project
  • Job placement assistance
  • Access to in-demand tools like IBM Watson

Yes, Scala is used for big data, In fact, Scala is specially designed for big data. Scala has libraries and tools for big data. Apache spark and apache Kafka are coded in Scala.