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Education experience

Education experience

Owned by Giedrius Naruševičius
Last updated: Sept 09, 2024 by Maria Czarniecka
8 min read

For LinkedIn, it's essential to have the technologies you have experience with listed. Both HR people and technical managers often glance through LinkedIn to see if there are relevant keywords there. As a most basic example - if you don't have "Python" explicitly mentioned in your LinkedIn, most companies that require it will dismiss you immediately.

You can add more details about what you’re learning at Turing College in the “Education” section on LinkedIn. To do this, simply click the plus button to add Turing College. Below, you will find key information to help you successfully fill all the fields such as grade, activities and societies, description and skills.

Grade

If you have already graduated, include the average score from all your project reviews.

Example: 95/100 (average score from all project reviews)

Activities and societies

Share key information about the activities you are/were involved in during your studies. This may include the hours spent learning, the number of completed projects, the number of peer reviews, your involvement in building the community on Discord, etc.

Example: Throughout the 600+ hour program, I completed 15 projects, all reviewed by peers and mentors (STLs - Senior Team Leads), achieving an average score of 95 points across 29 reviews.

The number of hours differs per program. You can find the specific number for your course on your certificate.

Description & skills

Each course covers broad topics and offers specializations tailored to specific areas of expertise, so it’s important to highlight only the most relevant information. Focus on key areas that align with your career goals and target positions. This way, you showcase your strengths without cluttering your profile with unnecessary details. We suggest using the following structure for your description:

  • Key areas covered

  • Specializations

  • Tools

Example of description (Digital Marketing course)

Key areas covered:

  1. Fundamentals of digital marketing, advertising types, customer lifecycle management.

  2. Google Marketing Solutions: Google Ads, Google Analytics 4, YouTube, and App campaigns.

  3. AI and Martech

  4. SEO: Basics of search engine optimization, keyword strategy, and technical SEO.

  5. Social Media Marketing: social media strategies, paid and organic marketing, ad metrics.

  6. Partnership Marketing

  7. Marketing Analytics: data manipulation, KPI tracking, and visualization with spreadsheets.

  8. Conversion Rate Optimization (CRO)

Specialization: Advanced Marketing Analytics

Tools: Excel/ Google Sheets, Google Analytics (GA4), Google Ads, Google Trends, Meta Ads, Google Tag Manager, PowerBI, SQL, BigQuery, MarTech

 

Build your own description by following the sections below. Choose your course, review the content and copy-paste the parts that are relevant to you.

Key areas covered:

You can remove details if a particular area is not highly relevant to your job hunt.

  • Fundamentals of digital marketing, advertising types, customer lifecycle management.

  • Google Marketing Solutions: Google Ads, Google Analytics 4, YouTube, and App campaigns.

  • AI and Martech

  • SEO: Basics of search engine optimization, keyword strategy, and technical SEO.

  • Social Media Marketing: social media strategies, paid and organic marketing, ad metrics.

  • Partnership Marketing: influencers, affiliates, and strategic partnership management.

  • Marketing Analytics: data manipulation, KPI tracking, and visualization with spreadsheets.

  • Conversion Rate Optimization: A/B testing, user behavior analysis, and CRO tools.

  • Databases: SQL, MySQL, Relational databases, BigQuery

  • Data Visualization: Google Spreadsheets, Dashboards, Data storytelling, Data presenting, PowerPoint, one of: Looker Studio / Tableau / PowerBI

  • Analytical Methods: Data cleaning, Cohort analysis, Retention analysis, Churn analysis, Funnel Analysis, Customer segmentation analysis, RFM & CLV

  • Statistics/Machine Learning: A/B testing, Linear regression, Logistic regression

  • Programming with Python: Python, Object-Oriented Programming, Pandas, Numpy, Matplotlib, Seaborn, Plotly, EDA

  • Software Engineering: Python, PEP8, Docker, Object-Oriented Programming, Numpy, Pandas, MyPy

  • Databases: Spark, MySQL

  • Data Visualisation: Seaborn, Matplotlib, Tableau, Dashboards, Data storytelling, Charting

  • Analytical Methods: Data cleaning, Data Wrangling, EDA, LIME, SHAP, PCA, Gaussian Mixture Models

  • Machine Learning: Linear regression, Logistic regression, Multilevel models, Marginal models, KNNs, Decision trees, Random forests, Support vector machines, XGBoost, Feature engineering, Dimensionality reduction, Clustering, Handling imbalanced data, Hyperparameter tuning, Convolutional & recurrent neural networks, Tensorflow, NLP, Transformer architectures

  • Mathematics & Statistics: Linear Algebra, Sampling, Statistical distributions, Statistical inference, Confidence intervals, A/B Testing, Hypothesis testing, Statistical modeling, Bayesian Statistics

  • Programming Languages: Python, JavaScript, TypeScript

  • Software Engineering: algorithms, Data structures, profiling, debugging, OOP, asynchronous programming

  • Version Control and CI/CD: Git, GitHub, GitHub Actions, monorepos

  • Front-end Development: HTML, CSS, responsive design, Bootstrap, Single Page Applications, Vue.js

  • Back-end Development: Node.js, Express.js, tRPC, RESTful APIs, RPC APIs, Cookies, JWT, Authentication, Authorization

  • Databases: Relational databases, SQL, SQLite, PostgreSQL, ORMs, TypeORM, Database Design

  • Testing: Test-Driven Development (TDD), Unit tests, E2E testing, Dependency Injection, Playwright, Vitest (Jest)

  • Code Quality: linting, formatting, type checking, type safety

  • Deployment: Docker, Containers, Cloud Services, AWS

  • Python: Advanced concepts, data models, sequences, modular coding, Asynchronous programming, context managers, metaprogramming

  • Version Control: Git, GitHub Linux Shell

  • SQL and RDBMS: SQL, RDBMS, MySQL, advanced SQL techniques, database setup and optimization

  • Data Warehousing: dbt

  • Apache Airflow: ETL pipeline construction, workflow automation, task testing, and security

  • Data Pipeline Technologies: ETL, ELT, and data ingestion technologies

  • Docker: Containerization, Dockerfiles, Docker Compose Kubernetes: orchestration with Kubernetes

  • Data Mesh: Principles, architecture, governance, and observability

  • Security and Privacy: serialization and compression

  • GCP, AWS, Azure: Data storage, data pipelines, machine learning workflows

  • Data Warehousing: data modeling, data governance

Specializations:

Choose those that apply for you

  • Advanced Marketing Analytics

  • Advanced Conversion Rate Optimization (CRO)

  • Social Media

  • Affiliate and Partnerships

  • Product Analytics

  • Marketing Analytics

  • Payments Analytics

  • Monetization Analytics

  • Risk Analyst

  • Financial Analyst

  • LLM Engineering Fundamentals (AI Specialization)

  • Computer Vision (AI Specialization)

  • Back-End Developer (Node.js)

    • Streams, Buffers, Queues

    • OOP and FP patterns

    • WebSockets

    • NoSQL and MongoDB

  • Back-End Developer (Symfony)

    • PHP

    • OOP patterns in PHP

    • Symfony

    • MVC

    • Composer

  • Front-End Developer (React)

    • React

    • State management, Redux

    • React Hooks

    • WebSockets

    • Next.js

  • Cloud Data Engineering with GCP

  • Cloud Data Engineering with AWS

  • Cloud Data Engineering with Azure

  • Big Data with Spark & Hadoop

Tools:

Choose tools that you have had experience with during your course.

  • Google Sheets

  • Google Analytics (GA4)

  • Google Ads

  • Meta Ads

  • Google Tag Manager

  • PowerBI/Tableau/Looker Studio

  • SQL

  • BigQuery

  • CRM software

  • Marketing automation tools

  • Excel/Google Sheets

  • SQL (BigQuery)

  • Tableau/Power BI

  • PowerPoint/Google Slides

  • Python

  • Pandas

  • Matplotlib

  • Seaborn

  • Git

  • Python

  • Pandas

  • NumPy

  • Pyspark

  • SQL (BigQuery)

  • Matplotlib

  • Seaborn

  • Plotly

  • Tableau/Looker Studio/Power BI

  • Statsmodels

  • Scipy

  • Scikit-Learn

  • XGBoost

  • PyTorch

  • Docker

  • Python

  • JavaScript

  • TypeScript

  • HTML

  • CSS

  • Git

  • SQL

  • Node.js

  • Git

  • GitHub

  • Linux Shell

  • SQL

  • RDBMS

  • GCP

  • AWS

  • Azure

  • RDBMS

  • Docker

  • Kubernetes

  • Mesh

Skills:

Add the most relevant skills. These are the skills that recruiters look for, so make sure to use keywords that match the job postings  you're interested in and be mindful about your choices.

Advertising and Acquisition

  • Digital Advertising

  • Ad Campaign Strategy

  • Customer Acquisition

  • Cross-Channel Marketing

  • Display Advertising

  • Paid Media Strategy

  • PPC

  • Programmatic Advertising

  • Media Buying

Customer Lifecycle Management

  • Customer Journey Mapping

  • Retention Strategy

  • Lead Nurturing

  • Upselling & Cross-Selling

  • Customer Segmentation

  • Lifecycle Marketing

  • Customer Retention & Churn Reduction

  • Loyalty Programs

Customer Relationship Management (CRM)

  • CRM Strategy

  • Personalized Customer Engagement

  • CRM Integration

  • Client Communication

  • Relationship Building

MarTech

  • Marketing Technology Stack

  • Marketing Automation Tools

  • Martech Integration

  • Martech Tools Implementation

Google Analytics 4

  • GA4 Setup & Implementation

  • Website Traffic Analysis

  • User Behavior Analysis

  • Custom Dashboards & Reports

  • Data-Driven Decision Making

  • Audience Segmentation

Google Keyword Planner

  • Keyword Research

  • SEO Keyword Strategy

  • Paid Search Strategy

  • PPC Campaign Optimization

  • Keyword Trend Analysis

Google Trends

  • Market Trend Analysis

  • Competitor Analysis

  • Keyword Research

  • Trend Forecasting

Google Tag Manager

  • Tag Implementation & Management

  • Event Tracking

  • Conversion Tracking

  • Website Tagging

  • Custom Tags Creation

Google Search Ads

  • PPC Campaigns

  • Search Engine Advertising

  • Ad Copy Optimization

  • Google Ads Bidding Strategies

  • Search Ad Retargeting

Google Performance Max Campaigns

  • Automated Campaign Management

  • Multi-Channel Advertising

  • AI-Powered Campaign Optimization

  • Ad Personalization

  • Cross-Platform Ad Management

Google Ads Display Campaigns

  • Display Network Advertising

  • Visual Ad Creation

  • Display Campaign Strategy

  • Retargeting & Remarketing

  • Ad Placement Optimization

Google AI-Powered Performance Ads

  • AI in Advertising

  • Automated Bidding & Targeting

  • Machine Learning for Ad Optimization

  • Ad Personalization

Google Ads Apps

  • App Advertising

  • Mobile User Acquisition

  • In-App Advertising

  • App Store Optimization (ASO)

  • App Downloads Campaigns

Advertising on YouTube

  • Video Marketing

  • YouTube Ads Strategy

  • Video Content Creation

  • YouTube Audience Targeting

  • Video Ad Performance Metrics

Social Media Marketing with Meta

  • Meta Ads (Facebook & Instagram)

  • Social Media Campaigns

  • Facebook Ads Manager

  • Instagram Ads

  • Audience Targeting

  • Retargeting on Meta Platforms

  • Paid Social Strategy

Affiliate Marketing

  • Affiliate Program Management

  • Partner Marketing

  • Commission-Based Marketing

  • Affiliate Sales Growth

  • Influencer Partnerships

  • Affiliate Link Tracking

Marketing Analytics with Google Sheets

  • Data Analysis

  • Marketing KPI Tracking

  • Google Sheets Automation

  • Custom Reporting

  • Marketing Performance Metrics

Conversion Rate Optimization (CRO)

  • A/B Testing

  • Funnel Optimization

  • User Experience (UX) Testing

  • Landing Page Optimization

  • Conversion Metrics Analysis

Search Engine Optimization (SEO)

  • On-Page SEO

  • Off-Page SEO

  • Link Building

  • Technical SEO

  • Keywords Optimization

  • SERP Analysis

  • Content SEO Strategy

Databases

  • SQL

  • MySQL

  • Relational Databases

  • BigQuery

Data Visualization

  • Google Spreadsheets

  • Dashboards

  • Data Storytelling

  • Data Presenting

  • PowerPoint

  • Looker Studio

  • Tableau

  • Power BI

Analytical Methods

  • Data Cleaning

  • Cohort Analysis

  • Retention Analysis

  • Churn Analysis

  • Funnel Analysis

  • Customer Segmentation Analysis

  • RFM (Recency, Frequency, Monetary) Analysis

  • Customer Lifetime Value (CLV) Prediction

Statistics/Machine Learning

  • A/B Testing

  • Linear Regression

  • Logistic Regression

Programming with Python

  • Python

  • Object-Oriented Programming (OOP)

  • Pandas

  • Numpy

  • Matplotlib

  • Seaborn

  • Plotly

  • Exploratory Data Analysis (EDA)

Software Engineering

  • MyPy

  • Type Checking

  • Python

  • PEP8

  • Docker

  • Object-Oriented Programming (OOP)

  • Numpy

  • Pandas

Databases

  • Apache Spark

  • SQL

  • MySQL

  • Relational Databases

Data Visualization

  • Seaborn

  • Matplotlib

  • Looker Studio

  • Tableau

  • Dashboards

  • Data Storytelling

  • Data Presenting

  • Charting

Analytical Methods

  • Data Cleaning

  • Data Wrangling

  • Exploratory Data Analysis (EDA)

  • LIME (Local Interpretable Model-agnostic Explanations)

  • SHAP (SHapley Additive exPlanations)

  • PCA (Principal Component Analysis)

  • Gaussian Mixture Models

Machine Learning

  • Linear Regression

  • Logistic Regression

  • Multilevel Models

  • Marginal Models

  • K-Nearest Neighbors (KNNs)

  • Decision Trees

  • Random Forests

  • Support Vector Machines (SVMs)

  • XGBoost

  • Feature Engineering

  • Dimensionality Reduction

  • Clustering

  • Handling Imbalanced Data

  • Model Selection

  • Optimization Algorithms

  • Hyperparameter Tuning

  • Convolutional Neural Networks (CNNs)

  • Computer Vision

  • Recurrent Neural Networks (RNNs)

  • TensorFlow

  • Natural Language Processing (NLP)

  • Transformer Architectures

Mathematics & Statistics

  • Sampling

  • Linear Algebra

  • Statistical Distributions

  • Statistical Inference

  • Confidence Intervals

  • A/B Testing

  • Hypothesis Testing

  • Statistical Modeling

  • Bayesian Statistics

Programming Languages

  • Python

  • JavaScript

  • TypeScript

Software Engineering

  • Algorithms

  • Data Structures

  • Profiling

  • Debugging

  • Object-Oriented Programming (OOP)

  • Asynchronous Programming

Version Control and CI/CD

  • Git

  • GitHub

  • GitHub Actions

  • Monorepos

Front-End Development

  • HTML

  • CSS

  • Responsive Design

  • Bootstrap

  • Single Page Applications (SPAs)

  • Vue.js

Back-End Development

  • Node.js

  • Express.js

  • tRPC

  • RESTful APIs

  • RPC APIs

  • Cookies

  • JWT (JSON Web Tokens)

  • Authentication

  • Authorization

Databases

  • Relational Databases

  • SQL

  • SQLite

  • PostgreSQL

  • ORMs (Object-Relational Mappers)

  • TypeORM

  • Database Design

Testing

  • Test-Driven Development (TDD)

  • Unit Tests

  • End-to-End (E2E) Testing

  • Dependency Injection

  • Playwright

  • Vitest (Jest)

Code Quality

  • Linting

  • Formatting

  • Type Checking

  • Type Safety

Deployment

  • Docker

  • Containers

  • Cloud Services

  • AWS

Python

  • Modular Coding

  • Asynchronous Programming

  • Context Managers

  • Metaprogramming

Version Control

  • Git

  • GitHub

  • Linux Shell

SQL and RDBMS

  • SQL

  • Relational Database Management Systems (RDBMS)

  • MySQL

Data Warehousing

  • dbt

  • Data Modeling

  • Data Governance

Apache Airflow

Data Pipeline Technologies

  • ETL

  • Data Ingestion Technologies

Docker

Kubernetes

Data Mesh

Security and Privacy

Cloud Platforms

  • CPC

  • AWS

  • Azure

  • Data Storage

  • Data Pipelines

  • Machine Learning Workflows

 

LinkedIn allows a maximum of 1,000 characters in the “description” box per entry in the Education section. To make the most out of this space, focus on highlighting the skills and tools that are most relevant to the jobs you're applying for.

Tips:

  • Prioritize: Choose skills and tools that match the job descriptions you're interested in. For example, if you're aiming for a Data Scientist role that doesn't require deep learning, you can skip mentioning deep learning libraries.

  • Be selective: Avoid listing every skill or tool you’ve encountered. Instead, focus on those where you have strong proficiency and that align with your career goals.

  • Relevance over quantity: Especially in fields like Digital Marketing, where skills like SEO or CRO might not apply to every role, avoid cluttering your profile with unrelated keywords. Tailor your profile to reflect the areas you're truly skilled in and passionate about.

 

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