Certified Data Science Practitioner (CDSP)

About the Course

For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and its direction. Not only can data reveal insights, but it can also inform—by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyse, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners.  

The field of Data Science has topped the Linked in Emerging Jobs list for the last 3 years with a projected growth of 28% annually, and the World Economic Forum lists Data Analytics and Scientists as the top emerging job for 2022.

Data can reveal insights and inform business—by guiding decisions and influencing day-to-day operations. This specialisation will impart delegates how to analyse, understand, manipulate, and present data within an effective and repeatable process framework. It will enable them to bring value to the business by putting data science concepts into practice.

This 5-day interactive Certified Data Science Practitioner (CDSP) training course is designed for business professionals who want to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing more excellent value to the business. The typical delegates in this programme will have several years of experience with computing technology, including some aptitude in computer programming.

Certified Data Science Practitioner (CDSP) will prepare learners for the CertNexus CDSP certification exam.

Core Objectives

The delegates will achieve the following objectives:

• Understand the use of data science principles to address business issues
• Apply the extract, transform, and load (ETL) process to prepare datasets
• Know how to use multiple techniques to analyse data and extract valuable insights
• Design a machine learning approach to address business issues
• Train, tune, and evaluate classification models
• Train, tune, and evaluate regression and forecasting models
• Train, tune, and evaluate clustering models
• Finalise a data science project by presenting models to an audience, putting models into production, and monitoring model performance

Training Approach

The delegates will be taught through learning techniques that ensure maximum understanding, comprehension, and retention of the information presented. The training methods will vary depending on their needs, from front-end to blended learning. It is divided through information received, participation, and Learning by Doing to provide the highest level of apprehension and retention of the presented material. They will be working on case studies and doing a hands-on project, and at the end of the training course, they will be assessed to determine the level of knowledge they have retained.

The Attendees

This training course is designed for business professionals who leverage data to address business issues. The delegates will have several years of experience with computing technology, including some aptitude in computer programming. 

However, there are more organisational roles that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analysing, and manipulating data through code or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, it targets someone who wants to learn how to extract insights more effectively from their work and leverage that insight in addressing business issues, thereby bringing more excellent value to the business. 

Likewise, it is designed to assist students in preparing for the CertNexus® Certified Data Science Practitioner (CDSP) (Exam DSP-110) certification.

Likewise, it will be valuable to the professionals but not limited to the following:

• Programmer
• Data Analyst
• IT Managers
• Data Scientists
• Anyone who would like to become certified in Data Science

Daily Discussion

DAY ONE: ADDRESS BUSINESS ISSUES WITH DATA SCIENCE

• Initiate a Data Science project
• Project Specification and Objectives
• Project Timeline and Deliverables
• Data Privacy and Security
• Data Access and Governance
• Formulate a Data Science problem
• The Data Science Process
• The Data Science Skillset
• Common Data Science Issues
• Randomness and Uncertainty

DAY TWO: EXTRACT, TRANSFORM, AND LOAD DATA

• Extract Data
• Structure of Data
• Big Data
• Data Sources
• Transform Data
• Data Preparation and Cleaning
• Types of Data and Data Operations
• Correction of Data Formats and Date Conversion
• Load Data
• Data Loading Considerations

DAY THREE: ANALYSE DATA

• Examine Data
• Analysis of Feature Types
• Representative Data and Sampling Techniques
• Explore the Underlying Distribution of Data
• Normal and Non-Normal Distributions
• Variability and Range Measures
• Use Visualisation to Analyse Data
• Scatter Plots, Line Plots, and Area Plots
• Preprocess Data
• Data Preprocessing

DAY FOUR: TRAIN MACHINE LEARNING MODELS

• Prepare to Train a Machine Learning Model
• Machine Learning Algorithms
• Develop Classification Models
• Various Types of Classification Models
• Develop Regression Models
• Various Types of Regression Models
• Develop Clustering Models
• Various Types of Clustering Models
• Which type is more suitable for me?
• How can you make a decision using these models?

DAY FIVE: FINALISE A DATA SCIENCE PROJECT

• Communicate Results to Stakeholders
• Audience and Insights
• Factors that Drive Outcomes
• Demonstrate Models in a Web App
• Web App
• HTML, CSS, and JavaScript
• Implement and Test Production Pipelines
• Data Pipelines
• Model Drift
• Monitor Models in Production