Best Guide: Be a Job-ready Data Analyst Within 6 months (2024)
Becoming a job-ready data analyst in just 6 months is possible, even with no prior experience. By dedicating time to learning essential skills like SQL, Excel, Python, and data visualisation, you can gain the knowledge needed to succeed in this in-demand field. Data analyst jobs are projected to grow by 25% from 2020 to 2030, making it a promising career choice.
Based on salaries reported on multiple job portals, the salary range for data analysts in Singapore as of April 2024 is approximately S$4,000 to S$14,167 per month, with the average falling around S$5,000 to S$6,000 per month.
With the right learning path and hands-on projects, you can develop a portfolio that showcases your skills to potential employers and kickstart your data analytics career in just half a year.
Key Takeaways (TL;DR)
- If you have a non-tech or non-IT background, spend some time to learn and understand the basics of data analytics and computer science. It helps you connect the dot better down the road.
- If you have time and budget constraint, try your very best to master essential tools like Microsoft Excel and SQL. You do not need to go for deep level of understanding at this stage.
- Work on your knowledge and technical skills in Python and Power BI when you have more time and budget.
- Other than paid online courses, your learning and development journey can be complemented by free resources on YouTube channels and ebooks.
- More importantly, you should target to build 3 to 5 projects or use cases from scratch and showcase them on GitHub. It is one of the best ways to convince yourself and your potential employer that you have a strong foundation in data analytics, and you are ready to take up a Data Analyst role.
- Be proud to showcase all of your certifications and technical skills on LinkedIn and social media platforms. In today’s world, your online profile probably works better than your traditional paper resume or CV.
Step 1: Understanding Data Analytics and Computer Science
In today’s data-driven world, understanding data analytics and computer science is essential for aspiring data analysts. Data analytics involves extracting insights from raw data using statistical techniques and programming, while computer science provides the foundation for designing and maintaining data systems and databases. By combining these two fields, data analysts gain the ability to make sense of complex data, uncover trends and guide decision-making in organisations. Proficiency in data analytics and computer science enables analysts to leverage tools like Python, SQL and machine learning to tackle real-world challenges and drive business success.
Knowledge in data analytics and computer science empowers you to thrive in this exciting and evolving field, opening doors to countless opportunities and making a meaningful impact in organisations across industries.
Google Data Analytics Professional Certificate
This online course is a beginner-friendly online course that teaches the basics of data analytics in about six months. Even if you have no previous experience, you’ll learn how to organise, visualise and analyse data using tools like spreadsheets, SQL and R programming.
IBM Data Analyst Professional Certificate
In this online course, you’ll learn to use the most powerful and up-to-date tools of the trade. You’ll master the art of data visualisation using Excel charts and BI tools like IBM Cognos Analytics & Tableau, allowing you to transform raw data into engaging visual stories. But that’s not all! You’ll also dive into the world of Python, harnessing its libraries like Pandas and Numpy to analyse data like a pro. Plus, you’ll learn to work with APIs and Web Services, adding even more superpowers to your data analytics toolkit. The best part? You’ll gain hands-on experience through labs and projects, building an impressive portfolio to showcase your new skills.
IBM Data Science Professional Certificate
In this online course, you’ll learn to think and work like a data science pro. You’ll master the essential skills and tools that data scientists use every day, including Python and SQL. With these tools, you’ll be able to effortlessly import, clean, analyse and visualise data into insightful stories. On top of that, you’ll also learn to build machine learning models and pipelines, allowing you to predict the future like a data-driven fortune teller. Ultimately, you’ll apply your new skills to real-world projects, creating a stunning portfolio that will impress your potential employers.
Harvard University CS50: Introduction to Computer Science via edX
Whether you’re a coding novice or a programming prodigy, this entry-level course is offered by Harvard University and will be delivered via edX. The legendary instructor, David J. Malan, will transform the way you think about problem-solving. You’ll learn to break down complex challenges into bite-sized pieces using algorithms and data structures, while exploring the realms of security, software engineering, and web development. With a variety of languages including C, Python, SQL, JavaScript, CSS, and HTML, you’ll be equipped to tackle any coding adventure that comes your way. But the real magic happens when you apply your newly acquired skills to problem sets inspired by real-world domains like biology, cryptography, finance, forensics, and gaming.
Google Advanced Data Analytics Professional Certificate
In this online course, you’ll explore the various roles data experts play within a company, learning how they turn raw numbers into game-changing insights. You’ll master the art of data visualisation, creating stunning visual stories that bring data to life. You’ll also learn to leverage the power of statistics, regression and machine learning models to analyse and interpret data like a pro. Finally, you’ll learn how to effectively communicate your data-driven insights to stakeholders, becoming a valuable asset to any organisation.
Step 2: Understanding Microsoft Excel for Data Analysis
Microsoft Excel is a critical tool for mastering data analytics. It allows users to organise, manipulate, and analyse large datasets efficiently. Excel’s powerful features, such as formulas, pivot tables, and charts, enable data analysts to quickly identify patterns, trends, and insights. Proficiency in Excel is essential for data cleaning, transformation, and visualisation, which are fundamental steps in the data analysis process. Moreover, Excel skills are highly valued by employers in the field of data analytics. Mastering Excel empowers you to effectively communicate data-driven findings and make informed decisions, setting you apart in the competitive world of data analytics.
Excel Skills for Data Analytics and Visualisation Specialisation
You’ll learn to use Microsoft Excel like a pro, importing, visualising, and analysing massive datasets with ease. Not only will you boost your current work performance, but you’ll also unlock new career opportunities. Plus, you’ll dive into the world of Power BI, adding a valuable skill to your employability portfolio. Whether you’re in business or data analysis, mastering advanced Excel and beginner Power BI is your ticket to success.
The Power Excel Bundle by Leila Gharani (Microsoft MVP)
This bundle includes Master Excel Power Query course & Master Power Pivot and DAX course. It’s Excel’s Ultimate Power Tools in one convenient, affordable bundle. What you will learn in this bundle applies to Power BI as well.
With The Power Excel Bundle, you’ll learn to effortlessly merge data from multiple sources, saving you time and frustration. No more wasting your day on tedious tasks like deleting pesky empty spaces in front of your data. And when it comes to taming those unruly tables, you’ll be a pivot table wizard, whipping them into shape with ease.
Step 3: Understanding SQL
SQL is the common language used to work with databases. It lets you access and manage data stored in databases. Almost all big companies use SQL.
SQL is like a superpower that lets you control data easily. You can use SQL to make, change and search databases quickly.
SQL might sound complicated, but it’s actually simple to learn and very useful. No matter what you do with data, SQL is an important skill to have. So, let’s jump in and understand why you should understand SQL!
SQL for Data Science by UC Davis
In this online course, you will learn how to:
- Get specific data from a table, write a SQL query that only returns the rows and columns you want;
- Use SQL to filter out unwanted data, put the results in a specific order, and calculate things like sums and averages;
- Combine the results of multiple SQL queries into a single table using the UNION command; and,
- Change the format of text, dates and numbers using functions. This lets you combine data from different places into one table with matching formats, making it easier to analyse.
Practical SQL: A Beginner’s Guide to Storytelling with Data by Anthony DeBarros
This ebook is a beginner-friendly guide to SQL, the language used for managing relational databases. Using PostgreSQL and pgAdmin, you’ll learn how to use SQL to uncover insights from your data.
The book starts with database and SQL fundamentals, then teaches you how to analyse data from government agencies through hands-on exercises and real-world examples. You’ll learn to create databases, build queries, aggregate data, and more.
Practical SQL focuses on clear examples and easy-to-follow lessons, making it accessible even to those with no programming experience. By the end, you’ll have the skills to build and manage your own databases efficiently.
Step 4: Understanding Python
Python is a popular programming language that is easy to learn and use. It’s a versatile language that can be used for many things, like creating websites, analysing data, and building games. Python uses simple, readable code that is easy to understand, even if you’re new to programming. It has a large community of users who contribute to its development and create helpful libraries and tools. Python is known for its simplicity and efficiency, making it a great choice for beginners and experienced programmers alike. If you’re interested in learning to code, Python is a great place to start.
Ultimate Beginner’s Python Course
This Python online course focuses on hands-on learning through small projects. By working on these projects, you’ll apply fundamental Python concepts to real-world problems, solidifying your understanding and building practical skills.
The course is structured like a video textbook, with 23 well-defined chapters. This organisation makes it easy to follow along and track your progress, whether you’re a beginner or have some programming experience.
Instructor Dave Gray brings his expertise to the course, explaining complex topics in a clear and straightforward manner. His teaching style helps you learn Python effectively and develop valuable problem-solving skills.
Python for beginners by Alex The Analyst
Alex Freberg, the mastermind behind the YouTube channel “Alex The Analyst,” is your go-to guru for all things data analysis. Whether you’re looking to switch careers or upgrade your skills, Alex has got you covered. Subscribe to his channel and never miss a beat on his latest and greatest content.
If you’re a Python newbie, you’re in luck! Alex’s “Python for beginners” video series will hold your hand and walk you through everything you need to know to get started in Python.
Beginning Python: From Novice to Professional by ML Hetland
This ebook, Beginning Python: From Novice to Professional, Second Edition is a practical guide that teaches you the fundamentals of Python. It covers a wide range of topics, from Python’s syntax and features to more advanced areas like database integration, network programming and web services.
The book follows sound development principles and includes ten hands-on projects to help you apply what you learn. Updated for Python 3.0, it also covers advanced topics like extending Python and packaging/distributing applications.
Whether you’re new to programming or have experience with other languages, this book provides a solid foundation in Python.
Python Algorithms: Mastering Basic Algorithms in the Python Language by ML Hetland
This ebook, Python Algorithms, Second Edition teaches you how to analyse and design algorithms using Python. Written by Magnus Lie Hetland, it focuses on classic algorithms and fundamental problem-solving techniques.
The book covers important and challenging topics in programming and computer science. It explains the theory behind algorithms and shows how it’s used in real Python code. You’ll learn about built-in Python algorithms and data structures, as well as how to create and test your own.
Whether you’re new to algorithms or an experienced programmer, this book is a clear and readable guide to mastering algorithms in Python.
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib by Robert Johansson
Python offers powerful modules for numerical and mathematical computing, both in its standard library and through popular open-source packages like NumPy, SciPy, FiPy, and matplotlib. This updated edition showcases Python’s capabilities in solving numerical problems and modelling applications across various domains, including big data, cloud computing, financial engineering, and business management.
This ebook presents new and extended case studies demonstrating Python’s rapid development and exploratory computing capabilities. Readers will learn array-based and symbolic computing, visualisation, numerical file I/O, equation solving, optimisation, interpolation, integration, and domain-specific problems like differential equations, data analysis, statistical modelling, and machine learning.
Step 5: Using Power BI for Data Visualisation
Power BI is a tool that helps you create visual reports and dashboards from your data. You can connect to various data sources, like Excel files or databases, and then use Power BI’s drag-and-drop interface to create charts, graphs, and other visuals. This makes it easy to explore and understand your data. You can also interact with the visuals, filtering and slicing the data to gain insights. Power BI is great for sharing reports with others and can be used for both personal and business projects. If you want to create compelling data visualisations, Power BI is definitely worth checking out.
Microsoft Power BI Data Analyst Professional Certificate
In this online course, you’ll learn to connect to various data sources and transform them into actionable insights. Excel will become your best friend as you master the most essential formulas and functions to prepare your data for analysis in Power BI.
Next, you’ll dive into the world of data visualisation and reporting, creating stunning reports and dashboards that bring your data to life. To showcase your newfound skills, you’ll tackle a capstone project and prep yourself for the prestigious Microsoft PL-300 Certification exam.
Power BI tutorials for beginners by Alex The Analyst
Alex Freberg, the mastermind behind the YouTube channel “Alex The Analyst,” is your go-to guru for all things data analysis. Whether you’re looking to switch careers or upgrade your skills, Alex has got you covered. Subscribe to his channel and never miss a beat on his latest and greatest content.
If you’re a Power BI newbie, you’re in luck! Alex’s “Power BI tutorials for beginners” video series will hold your hand and guide you through the basics of Microsoft’s powerful data visualisation tool.
Step 6: Publishing Data Analytics projects on Github
GitHub is a popular platform for sharing your data analytics work. It’s a great way to showcase your skills and collaborate with others. You can upload your code, data and project files to a GitHub repository. This makes it easy for others to view, download and even contribute to your project. Publishing your projects on GitHub can also help you build a portfolio of your work. This can be useful when applying for jobs or sharing your expertise with the community. So, if you’re working on a data analytics project, consider using GitHub to share it with the world!
As a data analyst, your portfolio should ideally include 10 Data Analytics Projects that showcase a range of skills. Here are some projects you might consider:
- Customer Segmentation Analysis: Grouping customers based on common characteristics.
- Sales Forecasting Analysis: Predicting future sales from past data.
- Churn Prediction Analysis: Identifying customers likely to stop using a service.
- Fraud Detection Analysis: Using data to highlight potentially fraudulent activity.
- Social Media Sentiment Analysis: Analysing public opinions from social media data.
- Website User Behaviour Analysis: Studying how users interact with your website.
- Inventory Optimisation Analysis: Managing stock to reduce costs and increase sales.
- Employee Performance Analysis: Assessing staff efficiency and productivity.
- Product Recommendation Analysis: Suggesting products based on customer behaviour.
- Supply Chain Management Analysis: Overseeing and optimising supply chain operations.
When you’re ready to publish, follow these steps:
- Create a Repository: Sign into your Github account and select ‘+’, then ‘New Repository’.
- Name Your Repository: Choose a descriptive name that relates to your project.
- Describe Your Project: Write a succinct description detailing what your project is about.
- Upload Files: Add your project files, including any data sets, scripts, and a README.
- Craft a README: The README file is crucial. It should outline your project, including objectives, methodologies, tools used, and findings.
- Visibility: Decide whether to make your repository public or private based on your sharing preferences.
In short
Becoming a job-ready data analyst within 6 months is achievable by following a structured learning path. By understanding the fundamentals of data analytics and computer science, mastering essential tools like Microsoft Excel, SQL, Python, and Power BI, and showcasing your skills through projects on GitHub, you can build a strong foundation in data analytics. While the journey requires dedication and commitment, breaking it down into manageable steps makes the process less daunting. With the right mindset and consistent effort, you can acquire the necessary skills and knowledge to launch a successful career in this fast-growing field.