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.

Duration: 1 day
Fee: Request
Start Date: Request

Work Like a Data Scientist

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;

Set up a development environment and install required software libraries
Use version control to maintain projects
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

Introduction – Development tools are commonly used by Data Scientists and essential for effective coding.
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.
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.
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.
Package Management – Package management is the system for keeping connected systems in line. This is essential for keeping large integrated systems operational through upgrades.
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.

Hear from Our Alumni

The training has benefited me a lot with the skills required to be a data analyst and has upskilled me in data analysis. What I like about the program is where real life problems in the office is used as the materials during the trainings which provides more understanding of the topics. Eventually, it has broaden up my market in the career development.

The program started by setting an expectation on Day 1, that it is not a Read-And-Pass syllabus. The beauty of this program is that it requires the trainees to get their hands dirty and do the actual work throughout the course. It is indeed very different from a lot of programs in the market which just produces a certificate in the end without the trainee actually able to do some real stuff. The course is definitely benefiting anyone who really want to do Machine Learning and Data Science, by creating talents who are able to connect human thought with the machine algorithm, being the bridge within the corporate, into the digital worlds.

The training was beneficial as it trains myself to think from another perspective - one that may not be new, but wasn't practiced as often as I'd like. It was fun and interactive thanks to the facilitator, Jay, too. He was very helpful to answer any questions the attendees have. Believe that this training gives the attendees an extra edge by giving them the knowledge and perspective on how to better utilize data to what they want/need to do - for the organization, the business, and even for personal use, of which I believe will bring myself further in my career.

The training has been an eye opener for me. As someone who is not of programming background, it introduces me to the possibilities of what data can do. It has also helped me bridge the difference in thinking between the Business Team and the Data Science Team. Had a great time learning!

This training enhance on my skill of analyzing of using another tools of programming instead of excel and how to model and visualized our data in a better way instead of our traditional way. Especially when involve with a large scale of data. If using excel, there will a lot of time consuming there instead of R. For me, in market nowadays of big data, this course will brings more value and create another point of view on how to visualize our data especially in making any decision. And of course will enhance our career development.

Register Interest

Data Science Development Tools

Data Science Development Tools

Work Like a Data Scientist

Learning outcome: