A 4-Month Roadmap to Becoming a Data Analyst in 2024 (Part 1)
This roadmap is for beginners with ZERO knowledge in data and analytics
Are you aspiring to become a data analyst in 2024? This article provides a technical roadmap for beginners to enter data analytics field in 2024, with free resources included.
Hello, nice to e-meet you. My name is Suhaila and I am a self-learned data analyst. I started learning data and analytics in 2021, and successfully landed a job in the analytics field as a Strategy & Policy Analyst the following year. My job involves producing data-driven consultancy and reports.
If you are looking to understand how analytics work in the professional world, I have written in my previous article about fundamental lessons in leveraging the analytics skills: 3 Fundamental Lessons as an Analyst from a Non-IT Background
This roadmap is designed based on timeline phases, outlining what you need to learn from Month 1 to Month 4, progressing difficulty level from the easiest to most challenging concepts. If you have zero knowledge in data analytics, my advice is to follow the roadmap systematically.
Month 1 and Month 2 (Part 1) will be the focus of this article, allowing us to delve into the details without creating an overly lengthy piece.
Before we dive into the core of the roadmap, let’s begin with an introduction to the general responsibilities of a Data Analyst.
What does a Data Analyst do?
If you have ever come across statistical figures in the company’s reports such as a 32% revenue growth from 2023 to 2024, the individuals responsible for producing those figures are Data Analysts.
Data Analysts are the detectives of the digital world. Their roles are essential to companies and organisations, involving tasks such as monitoring performance, project management, and inform decision-making. In essence, the day-to-day responsibilities of Data Analysts include:
Collecting and gathering data from various sources (e.g., databases, public domain, personalised data collection projects).
Cleaning and transforming raw data into an analysable dataset.
Exploring data to uncover meaningful patterns and insights.
Modelling and analysing data using appropriate statistical techniques.
Visualising data into desired outputs (e.g., reports, dashboards).
Data Analysts transform data into actionable insights for stakeholders, utilising multiple technical skills and tools, as well as cross-functional knowledge, as we will further explain in this roadmap.
So, let’s embark on this adventure together!
Month 1: Building Knowledge Foundation
The first month is dedicated to laying the groundwork with fundamental concepts and knowledge. We will explore basic Math and Statistics, data concepts and MS Excel.
Week 1: Learn the Basics of Mathematics and Statistics
A solid foundation in Math and Statistics is crucial for uncovering trends, patterns, and causal connections. The key concepts from Math and Statistics help data analysts reveal the intricacies of relationships between variables and underlying processes.
Arithmetic Skills: Begin by refreshing your arithmetic skills, which are essential for data manipulation and analysis. Practice addition, subtraction, multiplication, and division to build a strong numerical foundation.
Statistical Concepts: Dive into the world of statistics, starting with fundamental concepts like mean (average), median (middle value), mode (most frequent value), variance (spread of data), and standard deviation (measure of data dispersion).
Free Resources to Learn:
Week 2: Introduction to Data Concepts
Mastering fundamental data concepts is essential for aspiring Data Analysts as it underpins efficient data management practices, ensuring precision and accuracy in analyses. Proficiency in data concepts empowers analysts to navigate diverse datasets adeptly.
Data Types: Learn about different types of data, including categorical (e.g., names, categories) and numerical (e.g., numbers, quantities). Understand how data types impact analysis.
Data Sources: Explore the origins of data, including surveys, databases, spreadsheets, and web scraping. Recognise the importance of data quality and reliability.
Data Collection Methods: Gain insights into data collection methods such as surveys, observations, and interviews. Understand how data is collected and recorded.
Data Storage Formats: Explore common data storage formats like CSV (Comma-Separated Values), Excel spreadsheets, and databases. Learn about the advantages and disadvantages of each format.
Free Resources to Learn:
Week 3-4: Master Microsoft Excel
Proficiency in MS Excel is indispensable for Data Analysts due to its versatility in data manipulation and analysis, enabling efficient organisation, cleansing, and visualisation of data. Excel proficiency equips analysts with powerful foundational tools for data processing and facilitating comprehensive insights extraction.
Introduction to Excel: Start by becoming comfortable with the Excel interface. Learn how to open and save files, create workbooks, and navigate worksheets.
Data Entry: Practice entering data into Excel spreadsheets, beginning with simple datasets to improve your data input skills.
Formulas and Functions: Explore essential Excel formulas and functions like SUM, AVERAGE, COUNT, and IF statements. These tools will enable you to perform calculations and data analysis within Excel.
Data Manipulation: Begin manipulating data using Excel's sorting, filtering, and data validation features. These skills are invaluable for organising and cleaning datasets.
Pivot Tables: Delve into pivot tables, a powerful feature in Excel for summarising, analysing and presenting data in a dynamic and customisable format, enabling deeper insights into your datasets.
Free Resources:
Month 2: Familiarise with Power BI
In the second month, our focus shifts to Business Intelligence (BI) tools, as they enable faster and easier development of simple data projects. There are multiple widely-used BI tools such as Power BI, Tableau and Looker Studio. In this roadmap, I am emphasising Power BI primarily because it offers a smoother transition from MS Excel since both are Microsoft products, providing a similar interface and functions, making it less overwhelming.
Week 1-2: Learn Power Query
Power Query is a data transformation and preparation tool within Power BI. Mastering Power Query is essential for Data Analysts as it streamlines data transformation and preparation processes, facilitating seamless integration of diverse datasets and enabling efficient data cleaning, transformation, and reshaping for insightful analysis and reporting.
Connect to Data Sources: Effortlessly link Power Query to various data sources such as Excel spreadsheets, CSV files, databases and web services.
Transform Data: Efficiently manipulate and restructure datasets within Power Query, optimising data organisation and enhancing analytical capabilities. Examples of functions used are split column, transpose, unpivot column, fill down, remove duplicates, data type conversions, replace values and etc.
Data Analysis Expressions (DAX): Leverage DAX for advanced calculations and data modelling, enable deeper insights and sophisticated analysis such as calculate y-o-y sales growth, average order value, or customer churn rate.
Modelling Data: Employ data modelling capabilities within Power Query by merging multiple datasets, establishing relationships between tables, and defining calculated columns or measures for meaningful insights and visualisation in Power BI dashboards.
Free Resources:
Week 3-4: Data visualisation with Power BI
In the final two weeks of Month 2, immerse yourself in data visualisation using Power BI dashboards. Mastering visualisation is essential as it enables clear and impactful communication of insights to stakeholders, fostering informed decision-making.
Dashboarding: Learn how to create interactive dashboards in Power BI. Dashboards allow you to combine multiple visualisations into a single, cohesive view, making it easier for decision-makers to grasp complex information at a glance.
Descriptive Analysis: Understand the importance of descriptive analysis, which involves summarising and presenting data in a meaningful way. Learn how to calculate key metrics like averages, totals, and percentages within Power BI.
Different Types of Charts for Visualisation: Explore various types of charts and graphs that you can use to visualise data in Power BI. Common chart types include bar charts, line charts, pie charts, scatter plots, and more. Understand when and how to choose the right visualization for your data.
Interactivity: Master the art of creating interactive reports and dashboards in Power BI. Learn how to add slicers, filters, and drill-through capabilities to allow users to explore data and gain deeper insights.
Free Resources:
Conclusion
This article has provided a comprehensive first 2-month roadmap, spanning from building foundational knowledge in Math and Statistics to mastering essential tools like MS Excel and Power BI. By following this roadmap, beginners can gain the skills needed to head start their data analytics journey.
Remember, the key to success in data analytics is continuous learning and practice. Stay curious, explore real-world datasets, and keep honing your skills. It is a progressive learning, so it is totally fine if you do not master each tool at one time. I hope this roadmap will give an idea of what makes up Data Analysts.
In the next part of this roadmap, which will be covered in the subsequent article, we will dive deeper into advanced topics like SQL, Python and building your portfolios. These skills will further enhance your capabilities and preparation to become a data analyst and open up more opportunities in the field.
Stay tuned for Part 2 of this article, where we will continue our journey into the exciting realm of data analytics.
Thanks for your time & effort it took to prepare this material. I appreciate it & planning to emulate this path. You inspire me (& many future readers) that changing career trajectory into Data industry is more direct than it seems. Just a suggestion, maybe you could include the paid courses you took as well for our guidance. Looking forward to your next sharing. Keep up the good work, Suhaila!