CertNexus

Data Science Foundation

Dive into the world of Data, its implications, limitations, and business impact. Transform your career!

(DSP-110.AK1) / ISBN : 978-1-64459-424-7
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Data is transforming the job market and creating new opportunities. Tap into this realm of opportunities with our Data Science Foundation course that guides you to become a certified Data Science Practitioner. 

Enroll for our hands-on training course with in-depth explanations, practice questions, and exam-taking tips, and get ready to pass the CDSP DSP-110 exam. The course content is closely aligned with the exam objectives covering the entire data science lifecycle. All essential topics like Data extraction and manipulation, visualization, ML model, and model deployment are covered in detail. 

Habilidades que obtendrás

  • Expert with data extraction, loading, cleaning, and processing
  • Ability to manipulate data for visualization, with statistical techniques and feature engineering
  • Ability to work with Machine Learning (ML) models - selection, training, evaluation, and tuning
  • Understanding of classification techniques like Logistic Regression, Decision Trees, and more
  • Knowledge of regression techniques like Linear Regression, Polynomial Regression, and more
  • Familiarity with clustering techniques like K-Means, Hierarchical Clustering, and more
  • Ability to break down problems into smaller manageable parts
  • Ability to work with data science tools and libraries

 

Descargar el esquema del curso

1

About This Course

  • Course Description
  • Course Objectives
2

Addressing Business Issues with Data Science

  • Topic A: Initiate a Data Science Project
  • Topic B: Formulate a Data Science Problem
  • Summary
3

Extracting, Transforming, and Loading Data

  • Topic A: Extract Data
  • Topic B: Transform Data
  • Topic C: Load Data
  • Summary
4

Analyzing Data

  • Topic A: Examine Data
  • Topic B: Explore the Underlying Distribution of Data
  • Topic C: Use Visualizations to Analyze Data
  • Topic D: Preprocess Data
  • Summary
5

Designing a Machine Learning Approach

  • Topic A: Identify Machine Learning Concepts
  • Topic B: Test a Hypothesis
  • Summary
6

Developing Classification Models

  • Topic A: Train and Tune Classification Models
  • Topic B: Evaluate Classification Models
  • Summary
7

Developing Regression Models

  • Topic A: Train and Tune Regression Models
  • Topic B: Evaluate Regression Models
  • Summary
8

Developing Clustering Models

  • Topic A: Train and Tune Clustering Models
  • Topic B: Evaluate Clustering Models
  • Summary
9

Finalizing a Data Science Project

  • Topic A: Communicate Results to Stakeholders
  • Topic B: Demonstrate Models in a Web App
  • Topic C: Implement and Test Production Pipelines
  • Summary

1

Extracting, Transforming, and Loading Data

  • Reading Data from a CSV File
  • Extracting Data with Database Queries
  • Consolidating Data from Multiple Sources
  • Handling Irregular and Unusable Data
  • Correcting Data Formats
  • De-duplicating Data
  • Handling Textual Data
  • Loading Data into a Database
  • Loading Data into a DataFrame
  • Exporting Data to a CSV File
2

Analyzing Data

  • Examining Data
  • Exploring the Underlying Distribution of Data
  • Analyzing Data Using Histograms
  • Analyzing Data Using Box Plots and Violin Plots
  • Analyzing Data Using Scatter Plots and Line Plots
  • Analyzing Data Using Bar Charts
  • Analyzing Data Using HeatMaps
  • Handling Missing Values
  • Applying Transformation Functions to a Dataset
  • Encoding Data
  • Discretizing Variable
  • Splitting and Removing Features
  • Performing Dimensionality Reduction
3

Developing Classification Models

  • Training a Logistic Regression Model
  • Training a k-NN Model
  • Training an SVM Classification Model
  • Training a Naïve Bayes Model
  • Training Classification Decision Trees and Ensemble Models
4

Developing Regression Models

  • Training a Linear Regression Model
  • Training Regression Trees and Ensemble Models
  • Tuning Regression Models
  • Evaluating Regression Models
5

Developing Clustering Models

  • Training a k-Means Clustering Model
  • Training a Hierarchical Clustering Model
  • Tuning Clustering Models
  • Evaluating Clustering Models
6

Finalizing a Data Science Project

  • Building an ML Pipeline

¿Alguna pregunta? Consulta las preguntas frecuentes

Still have doubts? Read this section to find out more about our Data Science course and the CDSP DSP-110 certification exam.

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It is a certification exam conducted by CertNexus to validate your skills for analyzing, understanding, and presenting data in a structured framework. You’ll have to pass the DSP-110 exam to be a Certified Data Science Practitioner (CDSP).

All those wanting to learn how to effectively use data for extracting meaningful insights and passing the CertNexus DSP-110 certification exam should do this course.

There are no formal prerequisites. However, it is recommended to have a high-level of understanding for data science fundamentals.

This exam has 100 questions in multiple choice/single response format.

The exam duration is 120 minutes.

You have to score at least 70% to pass this exam.

Yes, this certification is valid for 3 years. For continuity, you’ll have to retake the most recent CDSP exam before the expiry.

With the industry-recognized CDSP certification, you can apply for many exciting job roles like:

  • Data Scientist / Analyst
  • Machine Learning Engineer
  • Business Analyst
  • Data Analyst
  • Data Engineer / Architect

Your All-In-One CDSP Certification Guide

  From essential topics to Lab projects, and practice questions. We’ve got you covered!

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