Your organisation needs data skills to remain competitive. Trends, such as the rise of AI, the explosion of big data, and the drive towards real-time analytics, are pushing organisations’ technical expertise to their limits. Failure to keep up is likely to put their business on the road to ruin. But if they build strong data capabilities in-house, they are more likely to place in the top quartile of financial performance within their industry.
This creates a lucrative opportunity for you.
According to research by Deloitte, over two-thirds (68%) of CDOs say their #1 priority is to improve how they use data and analytics, because it helps to drive business innovation and enables data-driven decision-making. Perhaps unsurprisingly, data analysts are in the top 10 fastest growing roles.
If you find yourself immersed in spreadsheets on a daily basis, upskilling to become a data analyst could be a smart career move. With the analytics power of Python, SQL, and introductory machine learning at your fingertips, you make yourself an indispensable resource to your business, because you can:
- Assist internal teams with data analysis
- Manipulate datasets to pinpoint insights, anomalies, and trends
- Identify opportunities for growth and operational efficiency
- Influence strategic decisions
- Engage stakeholders and communicate your findings
- Report and monitor KPIs that contribute to the business objectives
So what’s the best route to upskill as a data analyst?
University Degree vs. Data Analyst Apprenticeship
The traditional route to becoming a data analyst is to enrol in a university degree programme. However, these are quite costly – on average, £27,750 for a typical 3-year BSc degree course.
After costs, the second biggest barrier to learning is a lack of time. A full-time degree course is a massive commitment over several years. If you’re already established in your career and looking to upskill, it’s likely you have work and personal commitments that would be difficult to study around.
Additionally, a university degree tends to focus more on theoretical concepts, has limited applied learning, and limited/no work experience. This means that when it comes to putting your new skills into practice, there’s a big leap from the classroom to the business.
The alternative to a university degree is an apprenticeship.
Unlike a university degree, an apprenticeship teaches you technical acumen blended with soft skills and industry-specific knowledge. For example, you’ll learn to master tools such as SQL for database management, Python for data manipulation, and R for statistical analysis. As well as learn effective communication skills, critical thinking, and cross-team collaboration. And gain industry-specific knowledge to ensure your data interpretations are contextually relevant, which makes the insights derived both accurate and actionable.
Cambridge Spark's Data Analyst Apprenticeship (Level 4)
At Cambridge Spark, we set the gold standard. We’re always first to market and set the benchmark to upskill the global workforce with the critical digital transformation skills needed to succeed with data and AI.
So what does our Data Analyst Apprenticeship (Level 4) look like?
The curriculum:
Core Modules |
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| Introduction to Python and Pandas | Explore Python and Pandas to leverage Python's popularity and Pandas' powerful tools for essential data analysis tasks. |
| Data Science for Business | Apply data science to solve business problems with data analysis techniques, considering ethics and emerging legislation. |
| Further Python and Pandas | Develop proficiency in Pandas to efficiently manipulate and process data, enabling advanced data visualisation and modelling techniques for real-world applications. |
| Data Visualisation | Develop proficiency in Pandas to efficiently manipulate and process data, enabling advanced data visualisation and modelling techniques for real-world applications. |
| Time Series Analysis: Foundations | Examine the unique challenges of time series data and apply specialised tools and techniques using Pandas, Python, and NumPy to analyse and model data that changes over time. |
| Hackathon | Work collaboratively in teams on a project to apply newly acquired skills in real-world contexts. |
| Maths for Data Analysis | Explore essential mathematical concepts including probabilities, statistical significance, and linear algebra to build a strong foundation for understanding AI and Data Science principles. |
| Introduction to Machine Learning | Understand fundamental machine learning concepts and tools to effectively apply various models and techniques in practical scenarios. |
| Databases and SQL | Learn to use SQL to efficiently store, query, and retrieve both structured and unstructured data, enabling effective interaction with databases. |
| Web Scraping, Text-Mining, JSON and APIs | Explore web scraping, textmining, JSON, and API techniques to retrieve and process structured and unstructured data from various web-based sources. |
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Elective e-Learning Modules
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| Supervised Learning | This intermediate module is designed to gain familiarity with an array of discriminative and generative supervised learning models as well as sophisticated techniques to evaluate model suitability and improve model performance. |
| Unsupervised Learning | This intermediate module covers a wide range of unsupervised learning models and techniques to reveal latent structure within your data and covers topics including KMeans, hierarchical clustering, DBSCAN, PCA and t-SNE. |
| Advanced Maths for Data Science | This intermediate level module is designed to build more advanced understanding of key mathematical concepts including probability, statistics, linear algebra, calculus and optimisation that underpin much of the AI and Data Science domain. |
Delivery: The programme is delivered via live lectures, workshops, and self-paced e-learning.
Support: At all times, you are supported by our expert lecturers, technical mentors, and professionally trained coaches. We expect input from your line manager to ensure you’re fully supported at work for the duration of your programme. And you’ll be invited to join our community of 4,000+ learners and alumni.
Putting theory into practice: At a minimum, we expect 6 hours ‘off-the-job’ learning (applies to apprentices working 30 hours or more per week – learning that is undertaken outside of your day-to-day work duties and during your normal working hours. You also gain access to EDUKATE.AI, our online learning platform, so you can practice and hone your skills using real datasets in a sandbox environment.
Time commitment: 14-month programme + End-Point Assessment (EPA)
Cost: Cambridge Spark apprenticeships are covered by The Apprenticeship Levy, so can be paid for using money your employer has already ring fenced for training.
Employers Love Upskilling Over Recruiting
Over the last two years, nearly half (46%) of businesses have struggled to recruit for roles that require data skills, with 1 in 10 citing data analyst roles as the worst offenders. The truth is, there is a shortage of available talent. And attracting digitally savvy talent is seen as the biggest challenge for two-thirds of data leaders.
Employers must recruit, but the recruitment process is notoriously laborious. First is the time, effort, and cost involved in the hiring process, plus the stress and strain of screening CVs, arranging interviews, and doing follow-ups. Once your employer has found a suitable candidate, they face the risk of a cultural mismatch within the team. And then half of the new hires fail within the first 18 months of employment, which means they need to start the whole process again.
In stark contrast is upskilling, because the employer is investing in an employee they know and who the team trusts. In committing to professional development, the employer also signals how much they value that employee, so the employee won’t want to leave and work elsewhere. The bonus: you know their company, so as you embark on the programme, you’ll naturally view the insights through their company lens. And because you participate in off-the-job training, they see an immediate return on their investment from day one.
Find out more about the Data Analyst Apprenticeship (Level 4)
If you want to break free from the limitations of spreadsheets and learn how to leverage the analytics power of Python, SQL, and introductory machine learning, we’ll teach you everything you need to know to become a data analyst who makes a BIG impact in your business.