Data Science Foundation

Unlock the power of data and pave your way to success with the Data Science Foundation course. Prepare for the exam while engaging in interactive lessons, quizzes, test preps, and hands-on labs that will equip you with the skills to analyse, manipulate and present data effectively. Become a sought-after data science practitioner and bring invaluable insights to your organization’s decision-making processes.

What will you get:

Step into the world of data science and become a sought-after professional with the Certified Data Science Practitioner (CDSP) exam. In today’s data-driven landscape, businesses rely on skilled individuals who can effectively analyse, manipulate and present data. This exam will test your abilities to extract valuable insights, make informed decisions, and contribute to the success of any organisation.

Lessons 1: About This Course

  • 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: Analysing Data

  • Topic A: Examine Data
  • Topic B: Explore the Underlying Distribution of Data
  • Topic C: Use Visualizations to Analyse 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

Analysing Data

  • Examining Data
  • Exploring the Underlying Distribution of Data
  • Analysing Data Using Histograms
  • Analysing Data Using Box Plots and Violin Plots
  • Analysing Data Using Scatter Plots and Line Plots
  • Analysing Data Using Bar Charts
  • Analysing Data Using HeatMaps
  • Handling Missing Values
  • Applying Transformation Functions to a Dataset
  • Encoding Data
  • Discretising 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 Na├»ve 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

Finalising a Data Science Project

  • Building an ML Pipeline

Exam FAQs

FAQ's are not Available for this course.

Summary

Standard:

Lessons:

9+ Lessons

Delivery Method:

Online

Language:

English

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