Develop new skills for a successful career in Data Science!

Dear Learner

Welcome to TechTalent Academy.

The growing shortage of digital skills means there are many opportunities for people to upskill and move into the tech sector. We think this is a perfect opportunity for all to embrace greater diversity and flexibility of thinking.

Please notify TechTalent Academy as soon as you receive this document if you have additional learning needs, or if you require special equipment, or a special environment to allow this training to take place, please contact our your trainer or hello@techtalent.academy.

KEY INFORMATION

You will be advised on your course start date by a member of the Admissions Team.

Please ensure that you have read and understood the following requirements before beginning the course:

  • Complete and return your onboarding paperwork, including a signed copy of your learner’s contract. These will be sent to you upon acceptance of your place on the Data Academy.
  • Ensure you are prepared to seek employment within the tech sector following completion of the Data Academy, and to keep TechTalent Academy informed as to any role / employment you accept.
  • An update on your employment status will be requested at 30, 60, and 90 days after your graduation. This information is vital to the release of funding to cover your place on the Academy.

Please contact either the Admissions Team, or technical trainer, if you have any issues or concerns before starting the course. Your assigned Trainer will make contact once the course begins.

POLICIES

Our Learner Privacy Notice can be found here

Our Safeguarding Policy can be found here

Our Compliments & Complaints Policy can be found here

Our Whistleblowing Policy can be found here

LAPTOP REQUIREMENTS

You will need access to a laptop or desktop from day one of the course. The course materials are based on Windows/Mac applications, so please advise your onboard manager, or a member of the Talent Team, if you do not have access to a Windows/Mac device.

At a minimum, a laptop with a webcam and current operating system, such as Windows 8/10 or MacOS 10.15 (Catalina), should be sufficient to carry out your course.

Some suggested system requirements are as follows: • Intel i5 CPU, with an i7 recommended. • 8GB RAM, with 16GB recommended. • 1920 x 1080 resolution monitor display.

Please note that tablets and mobile devices are not suitable for the course.

You will need to download the following free software applications to your laptop prior to the course (your trainer will help you during day one if you encounter any issues, suggesting alternatives that may be available).

LEARNING OBJECTIVES

– Fundamental understanding of programming languages and different techniques through Python.
– Experience using various industry-standard libraries (NumPy, Pandas, Sci-Kit Learn, TensorFlow, Altair, Matplotlib, GGPLOT).
– Apply algorithms to build machine intelligence.
– Use data visualisation theory and techniques.
– Knowledge and understanding of industry leading software and packages (PowerBi and Tableau)
– Recognise and analyse ethical issues surrounding artificial intelligence.
– Exposure to cloud computing and storage solutions (AWS, Azure, Google Cloud Platform)
– Demonstrate knowledge of statistical data analysis techniques through data analysis.
– Apply principles of Data Science to analyse problems.
– Developing databases suitable for containing big data by using SQL
– Use data mining techniques to solve real-world problems.
– Developing teamwork, leadership, and decision making/problem-solving skills

COURSE STRUCTURE

The data program focuses on data science’s fundamental areas, including data engineering, data analytics, algorithms, databases and machine learning techniques.

The aim is to equip all learners with a blend of technical and personal skills to launch their career, and to ensure they emerge with a well-rounded individual skillset. The technical part of the curriculum is significantly hands-on and includes practical tasks and projects to consolidate the learning in a real-life environment.

In addition to technical skills, we teach soft skills to prepare our learners to apply and succeed in the workplace by training them to be critical thinkers, to develop various projects, to develop collaborative team dynamics, and to build communication skills. All teaching is delivered in a virtual led environment for increased versatility.

  • Python Fundamentals

Learners will tackle the fundamentals of Python, building simple and complex scripts and developing applications. These involve understanding the three pillars of programming, including sequence, selection and iteration programming techniques. We give insight into clean coding techniques and the advanced features of using procedures, functions, reading, and writing to external files.

  • Data Science with Python: Introduction to Pandas

The learners apply vital data science skills using the industry-standard library, Pandas. The students will learn how to load data and manipulate it to investigate the data, in order to generate meaningful insights.

  • Data Science with Python: Introduction to Algorithms, Machine and Deep Learning

Teaching is focused on the algorithms that are used within data science. The learners will be applying these algorithms using Sci-Kit Learn for machine learning and TensorFlow. They will create a clustering and regression model, and a fully connected neural network to investigate classification problems

  • Introduction to Modelling and Data Visualisation

The learners model data and create meaningful data visualisations and data dashboards, using various Python libraries and proprietary software.

  • Big Data & Databases

Learners dig deep into what big data is and how it affects our everyday lives. The learners gain fundamental skills in creating relational databases using industry-standard SQL.

Each module above will have either a practical project, i.e., write code with the following functionality, or a small task to demonstrate skills learnt. Either of these approaches will provide the delegate with the opportunity to demonstrate their understanding of the topic.

HOME LEARNING

Home learning: Work hours are flexible, but please note that your Trainer will only be available during core hours, and there may be additional group calls scheduled. Again, this will be agreed with the Trainers. Each week you will be given a home learning deadline. This will usually be the day before your next class. It is important that you meet these deadlines to avoid falling behind.

Home learning submission format: Home learning will be submitted via GitHub. You will receive guidance on how to create a GitHub account and link this to your PC. You will be required to provide your GitHub account details to your Trainer. Each week you will upload your home learning to your GitHub account, and this will be reviewed by your Trainer. Confirmation of receipt and any feedback will be provided to you via Slack.

We would also very much appreciate your feedback on how we can improve each module and the course. You will receive feedback forms via email.

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