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Data Fellowship Curriculum Level 4

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Intent

At Multiverse we aim to enable apprentices to be professionally competent Data Analysts through teaching them tools and concepts they will need to use everyday. At the heart of our curriculum is the data analytics lifecycle, a north star principle which helps apprentices to 'think data' and guide them throughout all aspects of their role and projects.



Learning is not a straight path, but a spiral where we continually build on and extend prior learning to help apprentices gain new insights and fresh perspectives on how they can conduct their analysis. Throoughout the course we will return to techniques such as extracting, preparing, analysing, manipulating and visualising data through different tools and softwares such as Microsoft Excel, SQL, PowerBI, Python and R. At all stages we will review concepts and engage apprentices to consider which tools would be best used in their role to support their analysis, and justify this decision in their portfolio.


In the latter stages of the course we will visit data science concepts such as machine learning and encourage apprentices to build their own predictive models to leverage better insights from their data. Throughout the course we will be increasing the complexity of the tools apprentices will be using so they can apply their knowledge to increasingly complex situations.



Implementation

Due to the pandemic we are currently delivering all sessions online using Zoom or Microsoft Teams. Summative assessment is at the heart of our delivery and coaches are expected to regularly check apprentice understanding of the concepts taught. This can be done using features built into Zoom (e.g. screen annotate, polls, etc) or using websites such as Mentimeter or Kahoot as well as traditional questioning and visual feedback (thumbs up, etc).


To standardise our delivery and ensure a consistent experience for all learners, all coaches use the pre-made delivery resources found on this page. If you want to create a new resource or makde an edit to an exisiting one please use this form to make your suggestion. You can check curriculum updates on our Notion Page .


Coaches support apprentice understanding through 1:1's, progress reviews and objective setting. Use these opportunities to tailor the experience for each apprentice and help them build a portfolio and projects which demonstrate the standards and enable them to advance in their role.


Note, for all cohorts launched after June 2021 will be on the revised standard. Make sure you are using the newer resources for 'Data Analytics in Industry' and 'Database Fundamentals'. Also, these cohorts will not be using either exam or the module 'Advanced Skills.' </div>

Impact

Ultimately we measure the success of our programme through exam and EPA results which our curriculum build towards. This document provided resources to enable coaches to support apprentices through both assessments, including sessions that be run and documents that can be sent out.


From month 3 we want apprentices to start thinking about their portfolio, showcasing the projects they have been working on and how they have applied skills from the course. Encourage apprentices to start taking on projects which allow them to extract, prepare, analyse, manipulate and visualise data and think about how these can be written up.



Curriculum Overview

Session Title
1 <a href=#data_industry> Data Analysis in Industry </a>
2 <a href=#da_excel> DA: Excel </a>
3 <a href=#database_fund> Database Fundamentals </a>
4 <a href=#da_sql> DA: SQL </a>
5a <a href=#da_power> DA: PowerBI </a>
5b <a href=#da_tableau> DA: Tableau </a>
6 <a href=#km2> KM2 Data Analysis Concepts </a>
7 <a href=#hack_1> Hackathon 1 </a>
8 <a href=#epa_2> EPA Prep </a>
9 <a href=#ds_found> DS: Python Foundations </a>
10 <a href=#ds_eda> DS: Exploratory Data Analysis </a>
11 <a href=#ds_model> DS: Modelling Basics </a>
12 <a href=#ds_machine> DS: Machine Learning </a>
13 <a href=#hack_2> Hackathon 2 </a>
14 <a href=#tools_of_trade> Tools of the Trade </a>
15 <a href=#advanced_tech> Advanced Techniques </a>
16 <a href=#km1> KM1 Data Analysis Tools </a>
17 <a href=#gateway> Gateway </a>


Other Resources


Note: On all embedded slide decks, you can bring up coach notes by pressing 's' on your keyboard.



Data Analysis in Industry

In our first Multiverse delivery module we will be encouraging apprentices to 'think data' by introducing the data analytics life cycle and how it governs all aspects of our role. The first half of the sessione explores the 'plan' phase as apprentices begin to consider project briefs and requirements elicitation engage and manage stakeholders. In the second half of the module we will continue exploring how to plan our projects by considering technical requirements such as data types and structures as well as GDPR compliance.


Version 1   Revised Version
Cohorts launched before June 2021   Cohorts launched after June 2021
 

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DA: Excel

In the first technical portion of the course, we will be teaching apprentices how to extract, wrangle and analyse data using Microsoft Excel. At the end of the bootcamp there is a session on acceptable evidence to help apprentices begin writing their portfolio.



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Database Fundamentals

The second Multiverse delivery module focusses on database management. Apprentices will learn to appreciate how databases are created and maintained through normalisation, error checking and entity relationship diagrams. Much of the focus will be on relational databases, but there is scope to discuss NoSQL as well.


Version 1   Revised Version
Cohorts launched before June 2021   Cohorts launched after June 2021
 

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DA: SQL

Following on from learning about relational databases, we teach Structured Query Language (SQL) to help apprentices extract and modify data directly from the source



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DA: PowerBI

Note: Use these if employer has requested PowerBI. An essential weapon in an analysts arsenal is visualisation, in this module we will be showing apprentices how to build visualisations and dashboards in PowerBI.



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DA: Tableau

Note: Use these if employer has requested Tableau. An essential weapon in an analysts arsenal is visualisation, in this module we will be showing apprentices how to build visualisations and dashboards in Tableau.



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KM2 Data Analysis Concepts

Preperation resources for the first exam



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Hackathon 1

Hackathon resources



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EPA Prep

Following on from the previous EPA session we will consider how to write a reflective journal and project write up for apprentice portfolios.



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DS: Python Foundations

The first of the Data Science Immersives introduces python and essential skills in programming.



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DS: Exploratory Data Analysis

Following on from the previous session, apprentices will be introduced to pandas for EDA as well as how to plot visualisations. The final part of this module is understanding statistical analysis and hypothesis testing.



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DS: Modelling Basics

In this module we will be introducing basic linear models and logistic regression. Apprentices will be encouraged to fine tune models as well as consider bias through train_test_split and cross validation.



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DS: Machine Learning

In the final DS Immersive apprentices will be shown how to forecast using time series models as well as understand text through natural language processing.



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Hackathon 2

Hackathon resources



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Tools of the Trade

In this module we will be covering various tools apprentices will need to enhance their analytics performance in their roles. In the first half of the session we will give an overview of Big Data, considering how it works and what the benefits and drawbacks are of using it. This is followed by a discussion around data analytics platforms and comparing their usage to coding it yourself.


In the second part of the module we will be looking into the statistical programming language R, briefly looking at how many of the processes we learned in Python can be performed in R.



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Advanced Techniques

The final stop before the KM1 exam and Gateway, the Advanced Skills session aims to teach apprentices concepts around data warehouses, data integration and ETL. This is followed by a run down of the final few months of the course, helping apprentices understand what will be expected of them through Gateway and beyond.