About DASCA SDS Exam

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How to choose the three versions of SDS exam dumps

Many candidates find that our DASCA SDS exam dumps have PDF version, SOFT (PC Test Engine) and APP (Online Test Engine). Even after they try the free demo download, they are still not sure how to choose. If you are purchasing for your company I will advise you purchase all the three versions of SDS exam dumps. Each candidate has their own study methods and habits. If you are purchasing for yourself, you can pick one version as you like.

PDF version ---- this version of SDS exam dumps is convenient for printing out, writing and studying on the paper. If you just want to know the exam collection materials or real SDS exam questions, this version is useful for you.

SOFT (PC Test Engine) ---- this version of SDS exam dumps is available for being installed on the Windows operating system and running on the Java environment. You can not only know the SDS exam collections materials or real exam questions but also test your own exam simulation test scores. It boosts your confidence while real exam.

APP (Online Test Engine) ---- this version of SDS exam dumps is the update of Software version. Online Test Engine supports Windows / Mac / Android / iOS, etc. It can be installed in all electronics. It contains all uses of Software version. After downloading it also support offline operate. You can study wherever you want.

DASCA SDS Exam Syllabus Topics:

Section	Weight	Objectives
Advanced Statistical Modeling	20-25%	- Statistical Inference 1. Hypothesis Testing 2. Confidence Intervals 3. Bayesian Methods - Regression Analysis 1. Linear and Logistic Regression 2. Generalized Linear Models 3. Regularization Techniques
Big Data Engineering	15-20%	- Distributed Computing 1. Apache Spark 2. Data Pipelines 3. Hadoop Ecosystem - Cloud Computing for Data Science 1. Serverless Architectures 2. AWS/Azure/GCP Services 3. Containerization
Foundations of Data Science	15-20%	- Data Science Ethics 1. Bias and Fairness 2. Privacy and Confidentiality 3. Transparency and Explainability - Data Science Lifecycle 1. Deployment and Monitoring 2. Model Building and Evaluation 3. Data Collection and Preparation 4. Problem Formulation
Data Visualization and Communication	10-15%	- Tools and Technologies 1. Tableau/Power BI 2. Python Visualization Libraries - Visualization Principles 1. Storytelling with Data 2. Dashboard Design
Machine Learning at Scale	25-30%	- Supervised Learning 1. Ensemble Methods 2. Time Series Forecasting 3. Support Vector Machines - Unsupervised Learning 1. Clustering Algorithms 2. Anomaly Detection 3. Dimensionality Reduction - Deep Learning Fundamentals 1. Convolutional Networks 2. Recurrent Networks 3. Neural Networks