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:
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
Marketing Analytics: data manipulation, KPI tracking, and visualization with spreadsheets.
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.
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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
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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
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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
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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
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Specializations:
Choose those that apply for you |
Advanced Marketing Analytics Advanced Conversion Rate Optimization (CRO) Social Media Affiliate and Partnerships
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Product Analytics Marketing Analytics Payments Analytics Monetization Analytics Risk Analyst Financial Analyst
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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
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Cloud Data Engineering with GCP Cloud Data Engineering with AWS Cloud Data Engineering with Azure Big Data with Spark & Hadoop
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Tools:
Choose tools that you have had experience with during your course. |
Excel/Google Sheets SQL (BigQuery) Tableau/Power BI PowerPoint/Google Slides Python Pandas Matplotlib Seaborn Git
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Python JavaScript TypeScript HTML CSS Git SQL Node.js
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Git GitHub Linux Shell SQL RDBMS GCP AWS Azure RDBMS Docker Kubernetes Mesh
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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 AcquisitionDigital Advertising Ad Campaign Strategy Customer Acquisition Cross-Channel Marketing Display Advertising Paid Media Strategy PPC Programmatic Advertising Media Buying
Customer Lifecycle ManagementCustomer Relationship Management (CRM)MarTechMarketing Technology Stack Marketing Automation Tools Martech Integration Martech Tools Implementation
Google Analytics 4GA4 Setup & Implementation Website Traffic Analysis User Behavior Analysis Custom Dashboards & Reports Data-Driven Decision Making Audience Segmentation
Google Keyword PlannerGoogle TrendsMarket Trend Analysis Competitor Analysis Keyword Research Trend Forecasting
Google Tag ManagerGoogle Search AdsGoogle Performance Max CampaignsAutomated Campaign Management Multi-Channel Advertising AI-Powered Campaign Optimization Ad Personalization Cross-Platform Ad Management
Google Ads Display CampaignsDisplay Network Advertising Visual Ad Creation Display Campaign Strategy Retargeting & Remarketing Ad Placement Optimization
Google AI-Powered Performance AdsGoogle Ads AppsAdvertising on YouTubeSocial Media Marketing with MetaAffiliate MarketingMarketing Analytics with Google SheetsConversion 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
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DatabasesSQL MySQL Relational Databases BigQuery
Data VisualizationGoogle Spreadsheets Dashboards Data Storytelling Data Presenting PowerPoint Looker Studio Tableau Power BI
Analytical MethodsData Cleaning Cohort Analysis Retention Analysis Churn Analysis Funnel Analysis Customer Segmentation Analysis RFM (Recency, Frequency, Monetary) Analysis Customer Lifetime Value (CLV) Prediction
Statistics/Machine LearningA/B Testing Linear Regression Logistic Regression
Programming with Python |
Software EngineeringDatabasesApache Spark SQL MySQL Relational Databases
Data VisualizationSeaborn Matplotlib Looker Studio Tableau Dashboards Data Storytelling Data Presenting Charting
Analytical MethodsData 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 LearningLinear 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 |
Programming LanguagesPython JavaScript TypeScript
Software EngineeringVersion Control and CI/CDGit GitHub GitHub Actions Monorepos
Front-End DevelopmentBack-End DevelopmentNode.js Express.js tRPC RESTful APIs RPC APIs Cookies JWT (JSON Web Tokens) Authentication Authorization
DatabasesTestingCode QualityLinting Formatting Type Checking Type Safety
DeploymentDocker Containers Cloud Services AWS
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PythonModular Coding Asynchronous Programming Context Managers Metaprogramming
Version ControlSQL and RDBMSData Warehousingdbt Data Modeling Data Governance
Apache AirflowData Pipeline TechnologiesDockerKubernetesData MeshSecurity and PrivacyCloud Platforms |
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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