Before I even begin, let me just put it out there that I know it’s very difficult to get a data analyst job. I hear it often in the comments that it was easy for me. Because I already had a master’s degree, but trust me, that was not the case. The world unfortunately is not a fair place. And I understand and recognise that there are lots of people in much worse situations than me. But I also know that there are many people worrying much better situations compared to me.
My parents were a primary school teacher and they worked tirelessly without a day off for decades. That’s going to work 7 days a week, 365 days a year for 20 plus years. They gave me the opportunity to study in one of the best private university in Bangladesh. But trust me, it was not easy. I learned english on my own. I worked at the customer service of a company during uni and studied day and night and applied to jobs 24/7 during my masters. I got rejected many many times before lending my first job and when I started my career I worked even harder. I was up at 5 AM. So I can study for certifications and do some exercise before work. I worked many late nights whilst I was investment banking. I’m a big believer that you should worry about the things that you can control. And what I was able to an am still able to control its how I approached my life, my tasks, my workload.
This blog is about the easiest way I know to become a data analyst, so let me jump into the first step, which would be to learn basics. Well, very well, now this might sound likely shame. But it’s foundational data analyst knowledge that will support you throughout your entire career.
Data analytics might sound a bit intimidating. At first, but let me assure you that you don’t need an advanced degree. Math to grasp the concepts at the core of data analysis are a key concept such as understanding data types, statistical measures and data visualisation techniques. So let me break these down a little bit further for you. Data comes in various forms and being able to distinguish between them is essential. Numerical data represents quantities and can be measured, for example, numerical data could include sales figures, inventory levels or customer ages. On the other hand categorical data represents categories or groups and cannot be measured. Categorical data could include product categories, customer segments or store locations. Another type is original data which contains order, but not precise differences between values and example could be a rating scale from port to excellent for customer satisfaction. Once you have a grasp of your data types, statistical measures come into play to help you make sense of the information measures. Like mean, median and mode provide insights into the essential tendencies of your data. For example, calculating the average sales per month can offer valuable insights into seasonal trends or overall performance. Median, on the other hand might be more appropriate if your data is skewed by extreme values. Mode identifies the most frequently occuring value you in a data set, which could be useful in identifying popular products or customer preferences. Data visualisation is a powerful tool for exploring and communicating insights from your data effectively. By representing data graphically, you can uncover patterns, trends and outliers that might not be apparent from raw numbers alone. Bar charts, for instance, are great for comparing categorical data such as sales performance across different product categories. Scatter plots are useful for visualising relationships between two numerical variables such as sales revenue and advertising expenditure. Line graphs can show trends over time, making them ideal for tracking sales performance or website traffic fluctuations.
Now I know that there’s a plethora of online courses out there to master all these key concepts, but if you want to learn in a fast efficient and structured way, the data analytics course from Course Careers could be the one for you. A course is for people with all kinds of backgrounds, whether you are looking for a college alternative or you re looking to make a career change. You can actually take a free introductory course to find out what working in data analytics is like and whether it be a good fit for you before committing to spending your time and your hard-earned money on the entire course. So if you wanna go ahead and try the introductory data analytics course for free, just use the link.
Back to the fundamental concepts. If you master these, I guarantee that you lay a solid foundation for your journey into data analysis. Remember practice is key to mastery. Don’t hesitate to apply what you have learned to real world data sets or engaging hands-on exercises to reinforce your understanding.
Tip number 2 would be to master spreadsheets like excel or Google sheets. This is an essential step on the journey to becoming a proficient data analyst. These spreadsheet tools serve as the cornerstone of data manipulation, analysis and visualisation for professionals across various industries. While they may seem like basic software applications delving into their functionalities can significantly enhance your analytical skills.
Now which one you should learn should really depend on what companies you wanna apply to? It’s rare that firms will use both to just pick one and go with it. My personal recommendation would be excel, because it is just so much more powerful than Google sheets. But it is also a lot more expensive. It’s highly likely that start-ups will use Google sheets. Why because it’s free. A Microsoft contract with the full suite of applications is expensive which is why smaller startups tend to use Google sheets and large established companies like the bank I work for will use Microsoft Excel as they can afford the hefty microsoft contract. Whichever you learn, the main goal will always be the same to manipulate data effortlessly. Whether you are dealing with large data sets or simple spreadsheets, mastering the art of data manipulation is crucial. Functions and formulas enable you to organised filter or a combined data in ways that suit your analytical needs. I actually gather the most popular excel formulas and functions in a single excel file. To make it easy for you to quickly reference, understand and use them on a daily basis. The excel file has popular math, date and time and text and many other data manipulation formulas and functions with real life examples and explanations to help you with actually applying the formulas in a business context.
Beyond basic data manipulation, Excel and Google sheets offer a vast array of functions and formulas that facilitate complex calculations. Functions like V lookup, X lookup and index match enable you to retrive specific data points from large data sets. Helping you in tasks such as inventory management and sales analysis or functions like SUMIF, AVERAGEIF and COUNTIF allow you to perform calculations based on specific criteria such as calculating the total sales for a particular product or the average revenue per customer.
Once, you are comfortable with applying the functions and the formulas, I would recommend mastering pivot tables and pivot charts as they are certainly one of the most powerful features of excel and Google sheets. These dynamic tables and charts allow you to summarise, analyses and visualise large data sets. With these you can quickly generate reports that summarise sales by product, category, region or time period providing valuable insights into performance metrics and trends.
All right. I think that’s enough of spreadsheets. So let’s move on to tip number 3 and dive into some other data analysis tools, which can significantly accelerate your journey toward becoming a proficient data analyst. While mastering spreadsheets provides a solid foundation exploring more robust tools like SQL, Python or R opens up a world of possibilities for advanced data manipulation, analysis and visualisation.
At first glance, learning programming languages may seem intimidating. Especially if you’re new to coding but with AI tools on the rise, you could easily supplement your learning. I just prompting and asking the right questions from Chat GPT nowadays. So the barrier to entry the coding is definitely so much lower than it used to be SQL stands for structured. For extracting querying and manipulating data stored in databases with SQL. You can the right queries to retrieve specific information from large data sets officially. For example, you could write an escalatory to extract customer information such as demographics or purchase history from a customer database. Or you could analyse sales transactions. Track inventory levels and identify trends within your data. SQL is a skill that pretty much all data.
Analysts positions look for so please spend the time to learn it well. An entire free school database tutorial course. Python and R are probably the two most popular programming languages for data analysis, thanks to their versatility and extensive libraries. Tailored for statistical analysis and machine learning with libraries like pandas, numpy, matplotlib -offers a comprehensive tool kit for handling and manipulating and visualising they I think can be used to automate repetitive tasks such as generating weekly sales reports or cleaning massive data. Machine learning capabilities enable you to build predictive models that can forecast. Yeah, future sales trends or customer behaviour based on historical data. By leveraging python, you can streamline your analytical workclothes. Uncovered actionable insights and drive business outcomes. Similarly, R provide a rich ecosystem of packages of libraries, specifically designed for statistical analysis and theatre. Visualisation with packages like deep liar. G plot and tidier are offers.
At last but not least, make sure to build a comprehensive portfolio to establish yourself as a competent data analyst and attract potential employers. Your portfolio serves as tangible evidence of your skills, demonstrating your ability to extract insights from data and solve real world problems effectively by showcasing a diverse range of projects anywhere between 3 to 5. Outside, you can highlight your proficiency, the various and political techniques and methodologies ultimately setting yourself apart from the other candidates in a competitive job market. I have an entire entire end-to-end. Portfolio project that you can follow to create your very own Data Analysts portfolio project and I also have the ultimate portfolio that you can easily check out to see what good looks like. For example, you could create a project where you analyse sales trends within a specific industry or market segment. By examining historical sales data identifying patterns and performing trend analysis. You can uncover valuable insights that inform strategic decision-making for businesses and another great project idea is to predict customer behaviour. For example, you could develop a predictive model to forecast customer churn for a subscription-based service or an e-commerce platform by advertising factors such as customer demographics, purchase history and engagement by. This type of project demonstrates your proficiency in predictive analytics and your ability to provide actional insights for businesses seeking to retain customers and improve customer satisfaction regardless of the specific project you choose to include in your portfolio. It’s essential to document your process thoroughly. Start by clearly defining the problem statement Or objective of the project along with any relevant background information or complex next outline. The steps you took to collect clean and preprocessed the data insuring transparency and reproduce ability in your analysis detailed the analytical techniques and methodologies you employed providing.
Finally, summarise your findings and conclusions. Highlighting key insights and actional recommendations for stakeholders, visualisation, such as charts graphs and interactive dashboards can enhance the presentation of your results and make them more accessible audiences and I’m afraid we’ve come to the end of my tips for now. If you enjoy the content like this make sure to check out some of my other blogs. Peace ✌️