CertNexus

Data Science Foundation

(DSP-110.AK1)/ISBN:978-1-64459-424-7

This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
Data Science Foundation

Lessons

9+ Lessons | 154+ Exercises | 64+ Quizzes | 247+ Flashcards | 247+ Glossary of terms

TestPrep

25+ Pre Assessment Questions | 2+ Full Length Tests | 25+ Post Assessment Questions | 50+ Practice Test Questions

Hand on lab

37+ LiveLab | 37+ Video tutorials | 01:50+ Hours

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Here's what you will learn

Download Course Outline

Lessons 1: About This Course

  • Course Description
  • Course Objectives

Lessons 2: Addressing Business Issues with Data Science

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

Lessons 3: Extracting, Transforming, and Loading Data

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

Lessons 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

Lessons 5: Designing a Machine Learning Approach

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

Lessons 6: Developing Classification Models

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

Lessons 7: Developing Regression Models

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

Lessons 8: Developing Clustering Models

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

Lessons 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

Hands-on LAB Activities

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

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

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

Developing Regression Models

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

Developing Clustering Models

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

Finalizing a Data Science Project

  • Building an ML Pipeline