From Business Analyst to Data Scientist
Data science can add enormous value to any business. It’s not just relevant for data-driven departments and roles, but is useful across a range of functions including marketing, logistics, and banking.
For example, marketers can use data science to identify the best times to promote products based on peak demand times. In the logistics and operations industry, data science can be used to optimise routing, streamline factory functions and allow supply chain transparency. And in the banking and finance sector, data science can help companies to monitor and assess large amounts of customer data and create personalised products and services specific to individual consumers.
But what are the key skills your employees need to be trained in to make all this possible?
Python is the primary programming language used by data scientists across the world. Data science is largely about programming - it’s what enables us to transform raw data into actionable insights. And Python is the preferred tool for making this possible.
Traditionally, Excel has always been used to perform data analysis. But Python offers enhanced capability for pulling different datasets together, providing greater flexibility, productivity and more sophisticated analysis.
Python can also handle much larger volumes of data than Excel - which is only going to become more important for data analysts in years to come, as companies face the challenge of extracting more advanced insights from ever-larger amounts of data.
Anyone working with large datasets can benefit from using Python to automate repetitive tasks and visualise data. For example, Python enables people to easily cross compare data from various reports and detect inconsistencies - a process that would take a lot longer in Excel.
Other time-consuming processes can also be automated by writing simple code in Python, such as updating spreadsheets, renaming files and compiling reports. Plus, users can access Python libraries, which saves them from having to write code from scratch.
Python runs on all operating systems, so can fit seamlessly with whatever system your business uses. It’s also free for anyone to download and use - so there’s no costs involved in purchasing and implementing a new system. And because Python is backed by a large community of developers, there’s a huge support network there that your employees can access when they come unstuck or want to find more efficient ways of doing things.
Your business produces an enormous amount of data, but unfortunately it won’t all be sitting neatly in one big file somewhere. Instead, data will sit across numerous files, databases, web pages, PDFs - and in any other document your company produces.
Data wrangling is the process of transforming and mapping data from one raw data form into another format that is more appropriate for analysis. Pandas is a software library written for Python that is the most widely used tool for data wrangling. Pandas also support more advanced data analysis, such as the ability to aggregate data in advanced pivot tables, in order to obtain more sophisticated insights.
SQL (Structured Query Language) is a programming language that makes retrieving data from relational databases possible. Think of this as the key to unlocking all your data so that it can be manipulated and visualised. As well as extracting data, SQL can insert, update and delete records in your database, and create new databases.
Most relational database management systems use SQL, including Oracle, Sybase, Microsoft SQL Server and Access. So there’s no need to purchase any additional software - all your employees need to do is learn how to use it.
One of the biggest challenges of becoming a data-driven organisation is being able to present data and insights in a way that everyone in the business can understand - not just the tech whizzes.
Data visualisation is a game changer for organisations wanting to work directly with data, as it enables people to summarise, present and provide recommendations from the results of data analysis. It allows for insights to be conveyed visually in a clear and concise way so they can be shared and understood throughout the whole business.
Complex data can be transformed into engaging and easy to understand charts, such as histograms, bar charts and pie charts - showing anything from factors influencing customer behaviour to which products to place where, quarterly sales mapping, trends, and more.
Machine learning is a subset of artificial intelligence (AI) that involves making computers “intelligent”, so that they can make decisions based on data. It’s what makes things like self-driving cars and voice recognition possible. But in a business setting, it can help you to identify profitable opportunities – and avoid unknown risks. It’s particularly useful for things like fraud detection and predicting purchasing patterns.
Machine learning is a complex skill set as it covers data science, statistics and software engineering. Regression analysis is usually one of the first machine learning approaches people will learn. Regression analysis is a predictive modelling technique which investigates the relationship between two or more variables. It can be used for things like predicting sales, understanding supply and demand, and whether to choose one marketing promotion over another.
These skills form the foundation of data science and are relevant for all organisations wanting to harness the power of data for business insight. Apprenticeships, funded by the government, provide a well-defined route for upskilling people into Data Science and Data Analyst roles.
The Level 4 Data Analyst Apprenticeship is the most entry level programme, open to full-time employees with no formal data science training. It is fully funded by the government for companies paying into the Apprenticeship Levy. Those not paying into the levy pay just 5% of the costs to train their employees.
All learners on the Level 4 Data Analyst Apprenticeship are taught how to identify, collect, analyse and visualise data. However, there are subtle differences in the curriculum depending on which training provider you choose to deliver the programme. For example, not all will provide advanced training on how to use Python and Pandas. Be sure to read through the modules carefully when choosing a training provider to ensure your chosen programme covers the most relevant skills.
More advanced training options will deepen your employees’ knowledge and skills in the field of data science. For example, the MSc Digital and Technology Solutions (Data Analytics) degree apprenticeship is for people who already hold a bachelors degree and can write and execute Python code at an intermediate level. This programme equips them with the tools and techniques to process larger and more complex datasets.
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