Study Roadmap for Beginners

“Embark on your journey in data engineering with our specially curated study roadmap for beginners. The following are different guides written by people to help learners out. They provide a structured path for newcomers, covering foundational concepts, essential tools, and key technologies in the field. Ideal for those starting out, these roadmaps are your first step towards mastering data engineering and other data roles. Good luck!”

Roadmaps

Data Engineering by Sandy

DataEngineerRoadmap_Notion - Data Engineering roadmap with a variety of course options from free to paid.

Roadmap.sh

SQL Roadmap - A guide for becoming proficient in SQL.
PostgreSQL DBA Roadmap - A roadmap for those aspiring to become PostgreSQL Database Administrators.
Python Roadmap - A detailed path for learning Python programming.
Backend Development Roadmap - A guide for becoming a Backend Developer.
AI & Data Scientist Roadmap - A comprehensive path for aspiring AI and Data Scientists.

I.AM.AI

Fundamentals Roadmap - A guide for understanding the fundamentals necessary in AI and machine learning.
Data Science Roadmap - A comprehensive guide to becoming a Data Scientist.
Machine Learning Roadmap - A detailed pathway for learning Machine Learning.
Deep Learning Roadmap - A structured guide for mastering Deep Learning.
Data Engineer Roadmap - A roadmap for becoming a Data Engineer.
Big Data Engineer Roadmap - A guide for those looking to specialize in Big Data Engineering.

Surfalytics

Ultimate Data Analytics Career Roadmap - From Data Analyst to Data Engineer as Individual Contributor (IC).

Data Camp Roadmaps By Nicksy

These are specific courses from Data Camp curated by Nicksy.

Data Engineering

Foundational Data Engineering Skills

Understanding Data Engineering (Beginner)
Introduction to Data Visualization (Beginner)
Understanding Cloud Computing (Beginner)
Introduction to Git (Beginner)
Introduction to Shell (Beginner)
Project: Designing a Bank Marketing Database (Project)

SQL Data Management

Introduction to SQL (Beginner)
Intermediate SQL (Intermediate)
Joining Data in SQL (Intermediate)
Introduction to Relational Databases in SQL (Intermediate)
Database Design (Advanced)
Streamlined Data Ingestion with pandas (Intermediate)

Python Programming and Data Handling

Introduction to Python (Beginner)
Intermediate Python (Intermediate)
Introduction to Importing Data in Python (Intermediate)
Data Manipulation with pandas (Intermediate)
Joining Data with pandas (Intermediate)
Python Data Science Toolbox (Part 1) (Intermediate)
Python Data Science Toolbox (Part 2) (Intermediate)
Software Engineering Principles in Python (Intermediate)
Cleaning Data in Python (Intermediate)
Data Types for Data Science in Python (Intermediate)
Writing Efficient Python Code (Advanced)

Data Analyst

Data Analyst in SQL Path

Introduction to SQL (Beginner)
Intermediate SQL (Intermediate)
Joining Data in SQL (Intermediate)
Data Manipulation in SQL (Intermediate)
PostgreSQL Summary Stats and Window Functions (Advanced)
Functions for Manipulating Data in PostgreSQL (Advanced)
Data-Driven Decision Making in SQL (Advanced)
Exploratory Data Analysis in SQL (Advanced)
Project: When Was the Golden Age of Video Games? (Project)

Data Analyst in Python Path

Introduction to Python (Beginner)
Intermediate Python (Intermediate)
Data Manipulation with pandas (Intermediate)
Introduction to Data Science in Python (Intermediate)
Introduction to Data Visualization with Seaborn (Intermediate)
Introduction to Statistics in Python (Intermediate)
Joining Data with pandas (Intermediate)
Sampling in Python (Advanced)
Hypothesis Testing in Python (Advanced)
Exploratory Data Analysis in Python (Advanced)

Data Analyst in R Path

Introduction to R (Beginner)
Intermediate R (Intermediate)
Introduction to the Tidyverse (Intermediate)
Data Manipulation with dplyr (Intermediate)
Introduction to Data Visualization with ggplot2 (Intermediate)
Introduction to Statistics in R (Intermediate)
Joining Data with dplyr (Intermediate)
Sampling in R (Advanced)
Hypothesis Testing in R (Advanced)
Exploratory Data Analysis in R (Advanced)

Globe.engineer

You can use this to generate your own custom study roadmap.

Data Engineering 101

Check this to Read more about Data Engineering 101.

Philippines Skills Framework

Below is an on-going project to propose a professional skills framework for data, analytics, and AI careers in the Philippines.

Proposed Professional Skills Framework ANALYTICS & ARTIFICIAL INTELLIGENCE.

Formal and Continuing Education

This list provides options for formal education in the Philippines with respect to data and technology programs.

Recommended Traditional Degrees

Here are a few recommended degrees that serve as good foundation for these type of work. Note that this is not a critical requirement as anyone can career shift to any role given enough time and upskilling. Please don’t treat this as a restriction but more of a guide on the relevant skillsets or learning outcomes that you would want when taking on these roles.

1. Data Analyst

Bachelor of Science in Statistics - Central to understanding data analysis, this degree equips students with essential skills in data interpretation, probability, and statistical analysis.
Bachelor of Science in Mathematics - Provides comprehensive training in analytical thinking and problem-solving, which are critical for analyzing and deriving insights from data.
Bachelor of Science in Computer Science - Teaches programming, algorithms, and data structures, which are crucial for data manipulation and analysis.

2. Data Engineer

Bachelor of Science in Computer Science - Offers essential knowledge in software development, algorithms, and systems design necessary for building and optimizing data systems.
Bachelor of Science in Electrical Engineering - Includes training in digital systems and circuit design, which can be crucial for understanding the hardware aspect of data processing and storage.
Bachelor of Science in Information Technology - Focuses on database management, system administration, and networking, foundational for maintaining robust data pipelines and architectures.

3. Data Steward

Bachelor of Science in Information Systems - Emphasizes the management of information systems and data governance, which align well with the responsibilities of data stewardship.
Bachelor of Science in Business Administration - With a focus on management information systems, this degree helps in understanding the business implications of data management.
Bachelor of Science in Library Science - Although less common, this degree covers data curation and management, critical for overseeing data lifecycle management and governance.

4. Data Scientist

Bachelor of Science in Statistics - Provides a strong statistical background necessary for data modeling, statistical testing, and data-driven decision-making.
Bachelor of Science in Mathematics - Essential for understanding the underlying algorithms used in data science, including linear algebra and numerical methods.
Bachelor of Science in Computer Science - Helps in mastering the technical and computational skills needed to handle large datasets and perform complex data analysis.

5. AI / ML Engineer

Bachelor of Science in Computer Science - Fundamental for understanding algorithms, machine learning, and software development, which are core to AI/ML engineering.
Bachelor of Science in Mathematics - Key for developing algorithms and models in AI and ML, especially through courses in statistics, probability, and abstract math.
Bachelor of Science in Cognitive Science - Offers interdisciplinary insights into how the human mind works, which can be invaluable in developing AI that mimics human decision-making processes.

Open Universities

An open university is a university with an open-door academic policy, with minimal or no entry requirements. Open universities may employ specific teaching methods, such as open supported learning or distance education.

Expanded Tertiary Education Equivalency and Accreditation(ETEEAP)

The ETEEAP is a comprehensive educational assessment program at the tertiary level that recognizes, accredits and gives equivalencies to knowledge, skills, attitudes and values gained by individuals from relevant work.

Internships and Work Opportunities in the Philippines

This list provides curated links to sites offering internship and work opportunities in the Philippines, focusing on various sectors and fields.

Internships in the Philippines

Here are some valuable resources for finding internships across a wide range of industries:

Work in the Philippines

Explore these links for career opportunities and job postings in the Philippines, particularly for startup and technology-related positions:

Finding Creative Work Experience

For individuals interested in data-related fields, finding the right internships and experience-building opportunities can greatly enhance their career prospects.

Hackathons and Data Competitions: Engage in local hackathons or online data competitions focused on Filipino concerns or sponsored by local companies. Try to start here and here.
Volunteering for Nonprofits: Offer your data analysis skills to local nonprofits in the Philippines, this includes NGOs, Churches, and Community Orgs. You can start here.
University Research Projects: If you’re a student or have connections with educational institutions, consider joining research projects at universities which frequently conduct studies requiring significant data analysis. You can start here.
Freelance Projects: Filipino data enthusiasts can find freelance data gigs on platforms like Onlinejobs.ph and Freelancer.ph, which cater to local freelancers and often have postings for data analysis projects. I suggest connecting with the r/buhaydigital group and starting with their list here.
Open Source Project Contributions: Contributing to open source projects that benefit the local community or address specific Filipino issues can be particularly rewarding. This not only builds your portfolio but also helps address local challenges through technology. You can start by joining their group here.
Industry Conferences and Meetups: Participate in industry conferences or meetups in major cities like Manila, Cebu, or Davao. Events that provide networking opportunities with data professionals and can lead to internship offers. Orgs such as AAP and DEVCON are great places to start.
Online Internship Platforms Specific to Tech: Utilize platforms like kalibrr.com and jobstreet.com.ph, which frequently list internships and entry-level positions in tech-focused roles within the Philippines.
Government and Public Data Initiatives: Get involved with projects sponsored by the Philippine government. Places to start are PSA, DOSTA-ASTI, and DICT.
Corporate Summer Trainee Programs: Look into summer trainee programs in companies which have data-intensive roles focusing on business intelligence and customer data analytics. A place to start is this list.
Social Media Groups and Online Communities: Join local groups on social media such as r/TechCareerShifter, Linkedin Filipino Professionals, and Data Analyst Job Hiring Philippines in Facebook.

Build A Portfolio

Building a professional portfolio is crucial for showcasing your skills and projects to potential employers or clients. Here are some valuable resources to help you create and enhance your portfolio:

Data Engineer Portfolio Project Ideas - Provides a range of project ideas and guidance on building a portfolio that stands out for data engineering roles.
Data Engineering Projects for Beginners - Simplilearn - Offers tutorials and project ideas for beginners in data engineering, helping you to start building your portfolio with practical experience.

Data Engineering Projects

This list provides a selection of data engineering projects suitable for beginners to advanced learners, offering practical experience and skills development in various aspects of data engineering. We do not advise doing all of them but try to do AT LEAST ONE.

Beginner Projects

Start your data engineering journey with these projects, which are designed for those new to the field.

Intermediate Projects

These projects are designed for those who have some foundational knowledge and are looking to tackle more complex tasks.

Advanced Projects

For those ready to challenge themselves, these advanced projects require a solid understanding of data systems and engineering principles.

Data Engineering Project E2E
[Data Engineering Best Practices](https://www.startdataengineering.com

/post/de_best_practices/) - Trino Getting Started with Hive and MinIO - Magic The Gathering Data Project

Data Analysis and Visualization Projects

Adventure Works - Create your own version of the AdventureWorks analysis, use any tool for the analysis and visualization. The dataset is also available on the internet, an example is in Kaggle.
Northwind Traders - Create your own version of the Northwind Traders analysis, use any tool for the analysis and visualization. The dataset is also available on the internet, an example is in Kaggle.
Wide World Importers - Create your own version of the Wide World Importers analysis, use any tool for the analysis and visualization. The dataset is also available on the internet, an example is in Kaggle.

Data Science Projects

Project Guides From Community

You can check project suggestions and guides from the community here.

Getting Certificates

One of the common ways professionals use to validate their knowledge is thru the presentation of certs. Just know that experience and actual projects are still better than certificates! In general Certs != Jobs okay? Let’s get on by looking at the two kinds:

Aspect	Certificate	Certification
Definition	A document awarded after completing a specific course or program.	A credential awarded after passing an exam, showing proficiency in a field.
Scope	Focused on a specific subject or skill.	Covers a comprehensive range of skills or knowledge in a professional field.
Duration	Short-term, ranging from a few hours to several months.	Often requires ongoing education and re-certification to maintain validity.
Issued By	Educational institutions, online platforms, or professional training bodies.	Professional organizations or certification bodies that set industry standards.
Purpose	Educational, aimed at broadening skills and knowledge for personal or career development.	Validates professional expertise and typically required for certain jobs or career advancement.

Paid Certifications

Here are some examples of PAID certifications. These things cost time and money, but are pretty much industry standards and fairly popular and are usually supported by large vendors and companies.

Cloud and Platform Specific Certifications

Microsoft Certifications

Oracle and MySQL Certifications

Open Source and Other Databases

Specialty and Advanced Certifications

Certificates for Government Work

Civil Service Eligibility: This is usually required for government jobs. If you’ve graduated with honors or passed professional board exams, you might not need to take the general eligibility exam. Here are the qualifications and you can look for more information how to take the exam here.
EDPSE Certification: If you’re in IT, this certification is tailored for tech roles in the government and can replace the general eligibility exam. You can check the qualifications here and you can also take a look at the ICT Proficiency examination which can be found here.
Advanced Degrees: Earning a Master’s or Doctorate can really

help if you’re aiming for higher positions. You can check our recommended data related graduate programs here. 4. Webinars and Courses: Look out for free webinars and courses offered by TESDA some of which you can find here. In addition to this, DICT also has similar offerings found here.

Free Certificates

There are options for free certificates, which merely represent your participation and completion of training courses and programs. You can find our curated list here which we have filtered to those that are FREE and relevant to data careers.