
Data Science Foundation
(DSP-110.AK1)/ISBN:978-1-64459-424-7
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 OutlineLessons 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