Turing College

Course Description:

Tools learned: Excel/Google Sheets, SQL (BigQuery), Tableau/Power BI, Python, Pandas, Matplotlib, Seaborn, Git

What you’ll learn:
-Build a rock-solid foundation in structured, analytical thinking
-Systematically analyze data to uncover valuable insights
-Convert insights into actionable recommendations that drive business success

Course overview:
Core program (20 hands-on projects):
1. Introduction to Data Analytics: Basics of data analytics, spreadsheets, SQL.
2. Communicating Analysis Results: Data visualization, presentation skills.
3. Main Analysis Types: Cohort analysis, segmentation, A/B testing.
4. Python for Data Analytics: Python programming, data processing, visualization.

Optional specializations:
-Product Analyst
-Marketing Analyst
-Payments/Monetization Analyst
-Financial Analyst
-Risk Analyst

Career support, including endorsement process, hiring support and additional benefits such as workshops and career events.

Subjects:
Database Management, A/B Testing, Python, Data Analytics, SQL, Data Visualization

Course Description:

Tools learned: Python, Git, SQL, Docker, Kubernetes, Apache Airflow, Apache Kafka, dbt, Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure, Hadoop, Spark

What you’ll learn:
-Master Python, SQL, and advanced data engineering concepts like containerization, data pipelines, and cloud platforms
-Design and manage data infrastructure, including data warehousing, ETL processes, and big data solutions
-Build, deploy, and optimize data engineering solutions on cloud platforms like AWS, GCP, or Azure

Course overview
Core program (14 hands-on projects):
1. Introduction to Data Engineering: Python data model, relational databases, SQL basics, and intermediate SQL.
2. Fundamentals of Data Engineering: Advanced Python, Linux shell commands, database management, and containerization with Docker and Kubernetes.
3. Intermediate Data Engineering: Data pipelines with Apache Airflow, data modeling with dbt, advanced data modeling, and data mesh principles.

Optional specializations:
-Cloud Data Engineering with GCP
-Cloud Data Engineering with AWS
-Cloud Data Engineering with Azure
-Big Data with Spark & Hadoop

Career support, including endorsement process, hiring support and additional benefits such as workshops and career events.

Course Description:

Tools learned: Python, Pandas, NumPy, Pyspark, SQL (BigQuery), Matplotlib, Seaborn, Plotly, Tableau/Looker Studio/Power BI, Statsmodels, Scipy, Scikit-Learn, XGBoost, PyTorch, Docker

What you’ll learn:
-Build a comprehensive understanding of Python and essential data processing tools like NumPy and Pandas
-Master statistical inference, including probability, A/B tests, and causal inference
-Develop proficiency in machine learning, covering models such as KNNs, decision trees, and neural networks
-Specialize in computer vision or LLM engineering, applying skills to real-world projects
-Translate data insights into impactful business solutions through data storytelling

Course overview:
Core program (15 hands-on projects):
1. Data Wrangling with Python: Python fundamentals, data manipulation with NumPy and Pandas, data visualization.
2. Statistical Inference: SQL for data analysis, statistical modeling with Python, linear and logistic regression.
3. Machine Learning: Supervised and unsupervised learning, gradient-boosted trees, feature engineering, model deployment.

Optional specializations:
-LLM Engineering Fundamentals (AI Specialization)
-Computer Vision (AI Specialization)

Career support, including endorsement process, hiring support and additional benefits such as workshops and career events.

Subjects:
Python, Machine Learning, Data Analytics, SQL, Data Visualization, Data Science

Course Description:

Tools learned: Google Sheets, Google Analytics (GA4), Google Ads, Meta Ads, Google Tag Manager, PowerBI/Tableau/Looker Studio, SQL, BigQuery, CRM software, Marketing automation tools

What you’ll learn:
-Develop a deep understanding of digital marketing principles and strategies
-Analyze and interpret digital marketing data to optimize campaigns
-Transform data-driven insights into effective marketing tactics that boost business growth

Course overview:
Core program (14 hands-on projects):
1. Introduction to Digital Marketing: Fundamentals of digital marketing, advertising types, customer lifecycle management.
2. Google Marketing Solutions: Mastery of Google Ads, Google Analytics, YouTube, and App campaigns.
3. AI and Martech Fundamentals: Introduction to martech tools, AI in marketing, and company scaling.
4. Intro to SEO: Basics of search engine optimization, keyword strategy, and technical SEO.
5. Social Media Marketing Solutions: Social media strategies, paid and organic marketing, ad metrics.
6. Partnership Marketing: Influencers, affiliates, and strategic partnership management.
7. Marketing Analytics: Data manipulation, KPI tracking, and visualization with spreadsheets.
8. Conversion Rate Optimization: A/B testing, user behavior analysis, and CRO tools.

Optional specializations:
-Advanced Marketing Analytics
-Advanced Conversion Rate Optimization (CRO)

Career support, including endorsement process, hiring support and additional benefits such as workshops and career events.

Course Description:

Tools learned: Python, JavaScript, TypeScript, HTML, CSS, Git, SQL, Node.js

What you’ll learn:
-Approach problems systematically using core concepts like algorithms, data structures, and clean code
-Work with external APIs and create your own, such as a ChatGPT bot
-Understand and build the visual part of a web page using HTML, CSS, JavaScript, and front-end frameworks
-Create and manage databases and servers, connecting them to the front-end
-Build user-friendly APIs
-Build fully functional websites and decide on a specialization in front-end or back-end development

Course overview
Core program (14-16 hands-on projects):
1. Fundamentals of Programming and Computer Science: Python basics, algorithms, data structures, and clean code.
2. Core Web Development: HTML, CSS, JavaScript, Node.js, SQL, and version control with Git.
3. Web Development Framework: Vue.js, REST APIs, TypeScript, end-to-end testing, and Scrum methodology.

Optional specializations:
-Front-End Developer (React)
-Back-End Developer (Symfony)
-Back-End Developer (Node.js)

Career support, including endorsement process, hiring support and additional benefits such as workshops and career events.