Data Analyst Training and Internship

Talent Glider
Liverpool
1 year ago
Applications closed

Related Jobs

View all jobs

Entry-Level Data Analyst: Online Training & Placement

Entry-Level Data Analyst Training & Career Placement

Junior Data Engineer/Analyst — Training & Growth

Data Analyst Career Pathway: Training & Placement

Entry-Level Data Analyst: Online Training & Placement

Launch Your Data Analyst Career - Training & Placement

Talent Glider is a premier staffing company dedicated to connecting top talent with global opportunities. As part of our commitment to nurturing future leaders, we proudly offer internship programs that provide students with valuable exposure to industry standards early in their careers.


Position: Data Analytics Training & Internship Program

Talent Glider is excited to welcome enthusiastic individuals eager to explore the dynamic field of Data Analytics. This internship offers the unique opportunity to work on live projects, gaining hands-on experience with cutting-edge tools and methodologies. If you’re passionate about analytics and ready to make an impact on real-world projects, we’d love to have you on board!


Projects you will work on:

E-commerce Optimization:

  • Setting up conversion tracking for online stores.
  • Analyzing sales funnels and user behavior to improve cart abandonment rates.
  • Implementing dynamic remarketing tags for personalized ad campaigns.


Retail Analytics:

  • Developing dashboards to track in-store vs. online sales performance.
  • Measuring the effectiveness of omnichannel marketing campaigns.
  • Setting up custom events to monitor promotional campaign success.


Travel & Hospitality Insights:

  • Tracking bookings and cancellations through advanced tracking setup.
  • Creating detailed reports on user engagement for travel websites.
  • Analyzing customer journey paths to improve trip-planning experiences.


Content-Driven Websites:

  • Monitoring user engagement metrics such as page views, session duration, and scroll depth.
  • Setting up video tracking to analyze interaction with media content.
  • Building dashboards to visualize content performance and audience demographics.


Who Should Join:

  • Students or professionals eager to kickstart their journey in Data Analytics.
  • Individuals passionate about working with tools and technologies like Google Analytics, Google Tag Manager, Google Data Studio, SQL, Power BI, and Web Analytics.
  • Those who value flexibility and seek a blend of robust training and practical exposure.


How to Apply:

Please submit your application directly through this job post or email us at


Note:

This is an unpaid position. Once you submit your application, our team will reach out to provide further details about the application process and next steps for joining.


Take the first step toward building a thriving career in Data Analytics with Talent Glider!

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.