The Importance of a Placement Year for Data Science Success

Introduction: #

Internships and placement years are so highly praised whilst being at university. If you’re able to land one, it’s said that it could be the magic opportunity that could set up your long-term career prospects.

After countless interviews, numerical tests, and coffee breaks, I successfully landed a placement role, while I was studying economics as an Intern MI Analyst, and I was hoping to start as soon as possible in Brighton, East Sussex.

Throughout this placement, I worked across teams, honed my analytical skills, and developed my knowledge of SQL, PowerBI, and Microsoft Excel to deliver business insights to individuals across the organisation where I worked this allowed me to understand the importance of SQL and having experience using it.

In this article, I will be sharing my insights relating to my placement experience and the importance of securing a placement opportunity to maximise your future employment prospects within data science. I hope that the insights I will deliver will provide a better understanding of what to expect from a data-oriented placement year and emphasize the importance of landing one for data science.

What is an MI Analyst? #

MI Analyst stands for Management Information Analyst. Individuals within such roles usually utilise a variety of technologies to support data-driven decision-making across the organisation that they work for.

Analysts usually have to communicate across the organisation to be able to ensure that they will produce the appropriate data output that is required by a range of individuals from across an organisation, and ensure that the data is easily able to be understood by almost anyone irrespective of whether they are from a numerical background or not.

MI analysts play an essential role across a wide range of organisations and can be commonly found in industries that rely on data-driven insights and reporting.

MI analysts can be found working within various industries and organisations such as:

• Consulting
• Banking and Finance
• Telecommunications
• Insurance
• Healthcare
• Retail and E-commerce
• Government

They can generate significant amounts of value by allowing such organisations and industries to observe wherever they may have shortcomings so that they can make rapid adjustments to boost their overall productivity, profits, and total revenues.

What are the primary tasks of an MI Analyst? #

• Data collection and reporting regularly using databases and spreadsheets.
• Data cleansing by removing irrelevant features from the data to later provide meaningful data insights.
• Producing or maintaining existing reports on a weekly, monthly, or quarterly basis.
• Providing performance analysis by identifying trends and analysing behaviours across time.
• Creating interactive dashboards in PowerBI, Tableau, or Power Query.
• Advising managers based on data trends and supporting their decision-making process.

Therefore, it is evident that MI analysts will require the use of a variety of different technologies and usually require some combination of SQL, PowerBI, Excel, and a range of other Data Analytics and Business Intelligence tools to support data-driven decision-making and very often such analysts will be approached from a range of managers that work for different departments.

For example, a performance manager may schedule a meeting with you to discuss a new report that they’re interested in seeing which allows them to easily keep track of and observe the amount of hours lawyers have been working this week.

Therefore, I would attend this meeting with the intention of specifying what the desirable outcome would look like for the involved party.

This would mean that I would require the following:

• What data visualisations would they like me to produce or modify?
• What is the proposed deadline for producing such a product?
• What are their needs and what do they require?
• Would they like the report to be self-adjusting or require manual adjustment?

I would usually then decide the most appropriate course of action based on what data I had available to myself on SQL. This would allow me to see what data I could easily extract and modify to meet their needs e.g. If it was about producing a report to see hours worked by lawyers, I would look at the corresponding databases and extract all the relevant information using the SQL coding techniques that I have been taught by my senior colleagues.

Once this step had been performed, I would modify a report using PowerPoint, or PowerBI to provide this information back to the client.

On the other hand, the client’s request would sometimes be as simple as just having this information provided back in a raw format, such as a table, or reported verbally for note-keeping.

This illustrates the variety of tasks an MI Analyst may handle, as they switch from SQL to PowerBI to Excel to Microsoft PowerPoint to juggle a number of data-driven operations, but this provides a massive learning opportunity that shows you how deep the world of data can truly go.

Why do I need a placement year as an aspiring data scientist? #

Applying to data science jobs can be difficult, especially with the incredibly competitive job market that we currently have to deal with, with many people struggling to land their first role.

Having valuable work experience before you graduate from university is something that can be a significant stepping stone that can launch your data science career on an upward trajectory and set you apart from the competition.

Colleagues I previously worked with deployed this strategy, as they started working as Intern MI analysts, and they were successfully able to land a part-time role as MI Analyst to then work up towards being a data scientist.

This is incredibly useful because you get hands-on experience in applying and dealing with vast datasets, creating data visualisations, and coding in SQL, a highly in-demand coding language, used for storing, manipulating, and retrieving data, which is commonly used for data science.

Experience is key when it comes to applying for data science jobs, and currently, competition has never been fiercer.

If you’re currently at university and reading this, it’s best to try and opt for as many chances as you get to work in a data-related field, as you’ll have many new opportunities to work with different large databases and learn new skills that you otherwise may not have been able to learn.

If you get the opportunity, you won’t regret it.

Networking opportunities #

Making friends within the workplace is an amazing opportunity to build lifelong connections who would likely support you throughout your data science career.

Working as an Intern MI Analyst, alongside senior MI analysts allowed me to learn directly from my colleagues and make important connections.

These connections can influence the trajectory your career spirals towards, as your colleagues can provide feedback, one-on-one support, and career advice about data science by accessing their own network and making referrals on your behalf to support your development.

Furthermore, a major advantage of having a data-related placement is that it allows you to leverage connections for personal growth and development. However, these opportunities will only arise when the correct opportunity presents itself and you take the chance to apply for the role.

Summary and final thoughts #

Overall, securing a placement as an Intern MI Analyst is an excellent opportunity to work towards a career in data science because of the coding skills you’ll develop through extensive use of SQL and other data analytics and business intelligence tools throughout the year. However, if one considers all the networking opportunities that also arise from getting early experience this truly is a win-win scenario to help kickstart your long-term data science career.

I’d highly recommend applying for such roles whenever they become available to you throughout your studies to boost your long-term data science career prospects.

Also! #

If you enjoyed this article, please feel free to take a read of my other articles where I regularly post about new data science topics and content to help inform you of the latest data science trends and foundational topics.

Have a great week ahead! 👋