Data Science Development Tools

Track: Associate Enterprise Data Analyst (AEDA)
Participants will learn how to create and execute programs on their computers, including package management, version control of code and how to use the command line to execute their scripts. Graduates will be able to do more in less time with reusable code.

Work Like a Data Scientist

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Data Science Development Tools provides an overview over the most common tools and software used by data scientists. Participants will learn how to create and execute programs on their computers, including package management, version control of code, and how to use the command line to execute their scripts. Graduates will be able to do more in less time with reusable code.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Set up a development environment and install required software libraries
  2. Use version control to maintain projects
  3. Use shell scripts to execute programs and create processing pipelines

Who should attend:

Business professionals and coders who want to improve their skills through best practices, tools and processes

1 day of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Interactive Lectures with Exercises.

CADS Certification​

Earn certification upon completion.

Pre-requisite:
Basic programming knowledge
Minimum Qualification:
High School Diploma

Training Track

Associate Enterprise Data Analyst (AEDA)

Data Science Development Tools is one of the modules under our Associate Enterprise Data Analyst (AEDA) programme. AEDA is a 13 to 18-days training program that provides analysts with the tools required for efficient Data Analysis.

Details of Subject

  1. Introduction –  Development tools are commonly used by Data Scientists and essential for effective coding.
  2. Executing code via the command line –  Participants learn to use the command line which automates analysis pipelines. This means more time for analysis and less for repetitive tasks.
  3. IDEs for Coding and Debugging –  IDEs are coding environments that assist in writing code by checking for errors. Fewer mistakes in codes saves time code maintenance.
  4. Version Control with Git –  GIT allows coders to make backups and track changes. This allows for extended teams to work on the same project with little overhead.
  5. Package Management –  Package management is the system for keeping connected systems in line. This is essential for keeping large integrated systems operational through upgrades.
  6. Data Science in the Cloud –  An overview of pre-built software and related cloud services. Pre-existing products don’t require building or maintenance.

Lead Instructor

Jan Sauer
A Data Scientist at The Center of Applied Data Science (CADS), Jan Sauer was a biostatistician in the field of deep learning and image/pattern recognition. He has a master’s degree in physics and have extensive experience as a software developer. Throughout his career, he has been involved in different areas of data science, ranging from automated data collection and data analysis, data pipeline and database design, and advanced machine learning where he uses Tensorflow extensively in image processing.

CADS Certification

AEDA CADS Certified Associate Enterprise Data Analyst

Associate Enterprise Data Analyst Certification consists of 4 certification exams (DBMS, Data Preparation, Statistical Data Analysis, and Python or R) and a capstone. Graduates are recognized as having business-ready skills to analyse data from many sources and in many formats. Your ticket to a career as a data analyst.

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Data Science Development Tools