Master Data Science: Become a Certified Practitioner

(CDSP™)

All our courses are 100% teacher-led by our authorized instructors and include certification exams.
Getting certified, you can also have the possibility to join Be Licensed’s recruitment portal.

Course Overview

Completing this course gives data science professionals a competitive edge by showcasing their ability to apply data science principles in real-world scenarios. Participants will learn how to analyze, manipulate, and present data to drive smarter decisions and improve business operations. With expertise in key techniques like machine learning model design and insight extraction, professionals will be prepared to tackle complex business problems. Gain the skills employers are looking for and elevate your career with this comprehensive course.

Course outcome

By completing this course, individuals will gain comprehensive expertise in data science, focusing on key areas such as data extraction, transformation, and analysis, alongside essential skills in machine learning model development and evaluation. Participants will also learn to operationalize data pipelines, ensure ethical data governance, and effectively communicate insights to stakeholders. Additionally, the course emphasizes data preprocessing, bias mitigation, and the integration of data from various sources using APIs and web scraping techniques. This rigorous training prepares individuals to confidently write the associated exam.

Your new skills

Data extraction, transformation, and loading (ETL).
Exploratory data analysis.
Data cleaning and bias mitigation.
Feature engineering.
Model building and evaluation (e.g., regression, classification, clustering).
Statistical analysis.
Machine learning algorithm application (e.g., Random Forest, XGBoost).
Data visualization and reporting.
Hypothesis testing and A/B testing.
Pipeline operationalization (e.g., deploying models with tools like AWS SageMaker, Docker).
Data governance and ethics (e.g., GDPR, HIPAA compliance).
Communication of data findings to stakeholders.
Data preprocessing (e.g., normalization, scaling).
Use of APIs and web scraping for data collection.
Data democratization and fostering data literacy within organizations.

Prerequisites:

Knowledge:

To succeed in the course, the following background knowledge is recommended:

Applied Mathematics and Math Theory, and Statistical Modeling Procedures: linear algebra, sampling, hypothesis testing, randomness, multivariate calculus, random forest, XGBoost, probability distributions like Poisson, normal, binomial, etc.
Data Science and Machine Learning Processes: formulating the problem; collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning, and evaluating a model; and finalizing a model.
Programming Abilities for ML, Statistics, and Querying: Python® and R, and SQL.
Data Visualisation Skills: Graphs, plots, charts, and other methods of visual data analysis.

Hardware: A computer (PC running Windows 10 is preferred, but Mac can be used) capable of hosting a Virtualbox Linux VM, with the following minimum specifications:

2GHz 64-bit (x64) processor that supports the VT-x and AMD-V virtualization instruction set and Second Level Address Translation (SLAT)
8 GB RAM
20 GB storage space free

Software: Anaconda and Jupyter Notebook run in the Linux VM.

Access: Internet access - a stable fiber connection of at least 10 Mbps up and down speeds is recommended.

What is included in the price?

Fully led by an authorized instructor.

All materials needed to complete the course are included in the course fee. This includes course literature, exercise assignments and fees for the certification exam.

The course is held remotely in a virtual classroom. All lectures are recorded and are available as reference material for two weeks after course completion.