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Scala Programming for Data Science

Expert instruction

2 skill-building courses

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Self-paced

Progress at your own speed

To light a fire, do you use a match, a lighter, or a torch? Depends on the size of the fire, much like the decisions that lead one to use Python, R, or Scala. Spark your interest in selecting the tools you need to tackle Big Data with ease, that will not just blow out.

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4577 ratings
16,452 already enrolled

What you will learn

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105+ hours of blended learning

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24 hours of self-paced, online learning

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Capstone and Real Life Projects

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Access to in-demand Tools

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Interactive learning

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Blended learning

Skills Covered

  • Scala

  • R

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Dedicated mentoring sessions from industry experts

About the Program

Lessson 01 - Basic Statistics and Data Types


  • Vectors and Labelled Points
  • Local and Distributed Matrices
  • Summary Statistics, Correlations, and Random Data
  • Sampling
  • Hypothesis Testing




Lesson 02 - Preparing Data


  • Statistics, Random data and Sampling on Data Frames
  • Handling Missing Data and Imputing Values
  • Transformers and Estimators
  • Data Normalization
  • Identifying Outliers




Lesson 03 - Feature Engineering


  • Feature Vectors
  • Categorical Features
  • Using Explode, User Defined Functions, and Pivot
  • Principal Component Analysis (PCA) in Feature Engineering
  • RFormulas




Lesson 04 - Fitting a Model


  • Decision Trees
  • Random Forests
  • Gradient-Boosting Trees
  • Linear Methods
  • Evaluation




Lesson 05 - Pipeline and Grid Search


  • Predicting Grant Applications: Introduction
  • Predicting Grant Applications: Creating Features
  • Predicting Grant Applications: Building a Pipeline
  • Prediciting Grant Applications: Cross Validation and Model Tuning
  • Predicting Grant Applications: Wrapping up





Tools Covered

Instructors

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

Course Content

Lessson 01 - Basic Statistics and Data Types


  • Vectors and Labelled Points
  • Local and Distributed Matrices
  • Summary Statistics, Correlations, and Random Data
  • Sampling
  • Hypothesis Testing




Lesson 02 - Preparing Data


  • Statistics, Random data and Sampling on Data Frames
  • Handling Missing Data and Imputing Values
  • Transformers and Estimators
  • Data Normalization
  • Identifying Outliers




Lesson 03 - Feature Engineering


  • Feature Vectors
  • Categorical Features
  • Using Explode, User Defined Functions, and Pivot
  • Principal Component Analysis (PCA) in Feature Engineering
  • RFormulas




Lesson 04 - Fitting a Model


  • Decision Trees
  • Random Forests
  • Gradient-Boosting Trees
  • Linear Methods
  • Evaluation




Lesson 05 - Pipeline and Grid Search


  • Predicting Grant Applications: Introduction
  • Predicting Grant Applications: Creating Features
  • Predicting Grant Applications: Building a Pipeline
  • Prediciting Grant Applications: Cross Validation and Model Tuning
  • Predicting Grant Applications: Wrapping up





WILL I GET CERTIFIED?

Upon successful completion of this Scala Programming for Data Science.

  • EARN YOUR CERTIFICATE

  • SHARE YOUR ACHIEVEMENT

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Enrolling Now

 
SELF-PACED LEARNING

₹ 9,999

₹ 19,999

PROGRAM BENEFITS

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Lifetime access to high-quality self-paced elearning content curated by industry experts

10 real-world projects guided by industry experts

48% annual growth in job openings

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24x7 learner assistance and support

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105+ hours of online learning content

Career Impact

Over 500 Careers Transformed

1/7
52%

Average Salary Hike

500+

Jobs Sourced

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22LPA

Highest Salary

300+

Hiring Partners

Frequently asked questions

Lessson 01 - Basic Statistics and Data Types


  • Vectors and Labelled Points
  • Local and Distributed Matrices
  • Summary Statistics, Correlations, and Random Data
  • Sampling
  • Hypothesis Testing




Lesson 02 - Preparing Data


  • Statistics, Random data and Sampling on Data Frames
  • Handling Missing Data and Imputing Values
  • Transformers and Estimators
  • Data Normalization
  • Identifying Outliers




Lesson 03 - Feature Engineering


  • Feature Vectors
  • Categorical Features
  • Using Explode, User Defined Functions, and Pivot
  • Principal Component Analysis (PCA) in Feature Engineering
  • RFormulas




Lesson 04 - Fitting a Model


  • Decision Trees
  • Random Forests
  • Gradient-Boosting Trees
  • Linear Methods
  • Evaluation




Lesson 05 - Pipeline and Grid Search


  • Predicting Grant Applications: Introduction
  • Predicting Grant Applications: Creating Features
  • Predicting Grant Applications: Building a Pipeline
  • Prediciting Grant Applications: Cross Validation and Model Tuning
  • Predicting Grant Applications: Wrapping up