Data Analysis
September 18, 2022 2024-04-24 17:09Data Analysis
Begin your journey to land a Data Analyst job
Learn about creating effective data models, cleaning, transforming and presenting data to stakeholders.
Online | 2-month duration | Monthly Intakes
Who is a Data Analyst?
A data analyst reviews data to unveil crucial insights and innovative solutions. They also effectively communicate their discoveries to stakeholders, shaping strategic decisions.
- Unveils valuable insights from data to solve problems.
- Effectively communicates findings to company leaders.
- Essential in all industries.
- Varied levels with attractive average salaries.
Course Overview
The DBSchool Data Analysis course offers an extensive curriculum designed to develop fundamental to advanced data analysis skills.
Combining practical exercises, scenario-based tasks, and comprehensive instruction in Excel, SQL, Python, and PowerBI, this course prepares students to excel in data analysis. Emphasising hands-on learning, it ensures students are well-equipped to apply their knowledge effectively in real-world situations.
Student Journey
1. Introduction
- The journey begins with an orientation session, where new students are introduced to the Digital Business School and the structure of the Data Analyst Course.
- Students receive login credentials for the learning platform and access to course materials.
2. Foundational Learning
- The course kicks off with foundational modules covering the basics of data analysis, including terminology, concepts, and best practices.
- Students engage in interactive lessons, quizzes, and exercises to solidify their understanding.
3. Exploring Data Analysis Tools
- The journey continues with modules focused on popular data analysis tools such as Excel, PowerBI, and Python.
- Students learn how to manipulate and analyse data using each tool, with hands-on exercises and real-world examples.
4. Data Retrieval and Storage
- Next, students delve into the realm of data retrieval and storage technologies.
- Modules cover SQL for database querying, scripting languages for data manipulation, and techniques for reading data into analytics tools.
5. Data Visualisation
- With a solid foundation in data analysis and retrieval, students progress to modules on data visualisation.
- They explore techniques and best practices for creating impactful visualisations using various tools and libraries.
6. Real-Life Scenarios and Projects
- The heart of the course lies in real-life scenarios and projects that simulate the challenges faced by data analysts in the workplace.
- Students are presented with authentic datasets sourced from platforms like Kaggle and tasked with analysing them to extract insights.
- Throughout these projects, students document their workflows, methodologies, and findings, emulating real-world data analysis processes.
7. Report Writing & Presentation Skills
- As students near the end of the course, they focus on report writing and presentation skills.
- They learn how to effectively communicate their data analysis results to stakeholders, including managers and decision-makers.
- Through practice sessions and feedback, students refine their ability to craft clear, concise reports and deliver engaging presentations.
8. Capstone Project and Graduation
- The journey culminates in a capstone project where students apply all they've learned to tackle a comprehensive data analysis task.
- Upon successful completion of the project and final assessments, students graduate from the Data Analyst Course, equipped with the skills and knowledge to excel in the field of data analysis.
Target Audience
This course is suitable for beginners to intermediate learners interested in data analysis...
... including professionals looking to enhance their analytical skills, students preparing for a career in data science or analytics, and anyone keen on using data for better decision-making and insight generation.
Minimum Requirements
Essential:
Basic computer literacy and familiarity with spreadsheet software.
Nice to have:
Previous experience with any programming language or database management system, though not required.
Duration
2 Months (8 weeks)
The course is conducted over 2 months with weekly classes every Saturday. These sessions are designed to be interactive, providing both theoretical knowledge and practical skills through a blend of lectures, hands-on exercises, and scenario-based learning. The schedule is structured to accommodate the comprehensive curriculum while also allowing students enough time during the week for self-study, practice, and assignment completion.
Learning Outcome
After completing this course, students will be able to:
- Manage and visualise data using Excel, mastering Pivot Tables, advanced functions, and creating macros.
- Utilise SQL for database management and querying, learning data aggregation, relationships, joins, normalisation, and advanced SQL querying techniques.
- Apply Python for data analysis, exploring basic syntax to advanced data manipulation with libraries like Numpy and Pandas.
- Create and customise data visualisations and dashboards with PowerBI, covering data cleaning, transformation, modelling, and writing DAX formulas.
- Understand and apply statistical analysis for forecasting and decision-making.
- Extract data from diverse sources for analysis.
- Manipulate data to clean, transform, and prepare it for analysis.
- Visualise data effectively, using various tools and libraries to present insights clearly.
Course Highlight
On course modules are:
- Module 1: Excel Basics and Applications
- Module 2: Introduction to SQL and Applications
- Module 3: Working with PowerBI
- Module 4: Python Basics to Intermediate
- Module 5: Exploratory Data Analysis/Visualisation and Inferential Statistics
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Where Our
Graduates Work
Multiple graduates have been trained and coached and now work for companies in various fields as permanent or contract employees.
Our Payment
Partners
We have collaborated with Stripe. They provide various options, such as instalments, or the 'buy now, pay later' service and also online credits.
