YTread Logo
YTread Logo

EASIEST WAY TO BECOME A DATA ANALYST

Apr 02, 2024
Before I even start, let me tell you that I know that it is very difficult to get a

data

analyst

job. I often hear in the comments that it was easy for me because I already had a master's degree, but believe me, that was not the Unfortunately, the world is not a fair place and I understand and recognize that there are many people in much worse situations than me, but I also I know there are many people who are in much better situations compared to mine. My parents were immigrants to Hungary. a poor Eastern European country and worked tirelessly without a day off for decades working 7 days a week, 365 days a year for over 20 years.
easiest way to become a data analyst
I was given the opportunity to study in the UK, but believe me, it wasn't easy. I learned. English on my own I took out a student loan that I am still paying every month. I worked as a waiter during college and studied day and night and applied for jobs 24/7 during my master's degree. They rejected me. I did it many times before I got my first job. and when I started my career I worked even harder. I got up at 5:00 a.m. m. so I can study for certifications and get some exercise before work. I worked many late nights while I was in Investment Banking.
easiest way to become a data analyst

More Interesting Facts About,

easiest way to become a data analyst...

I arrived in the UK with absolutely nothing more than luggage, a backpack and a desire to make something of myself to take advantage of the opportunity my parents have worked so hard to give me. I firmly believe that you should worry about the things you can control and what I could control. and I can still control is how I approach my life my tasks my workload I mind my own business and try to do the best I can to live the best life I can With all that being said, this video is about the

easiest

way that I can know how to

become

a

data

analyst

, so let me move on to the first tip, which would be to learn the basics very well.
easiest way to become a data analyst
This may sound like a cliché, but it is fundamental data analyst knowledge that will support you throughout your career. Data analysis may sound a little intimidating at first, but let me assure you that you don't need an advanced degree in mathematics to understand the core concepts of data analysis. Concepts like understanding data types, statistical measures, and data visualization techniques, so let me break them down. A little more 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 may include sales figures, inventory levels, or customer ages.
easiest way to become a data analyst
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 ordinal data that contains orders but not precise differences between values. An example could be a poor to excellent rating scale for customer satisfaction. Once you understand your data types, statistical measures come into play to help you understand the information. Measures such as the median, mode, and mean provide insight into essential trends in your data; for example, calculating average sales per month can provide valuable insight into seasonal trends or overall performance. the other side might be more appropriate if your data is skewed by extreme values.
The mode identifies the most frequently occurring value in a data set, which could be useful for identifying popular products or customer preferences. Data visualization is a powerful tool for exploring and communicating insights about your business. data effectively by representing the data graphically, you can discover patterns, trends and outliers that might not be evident from raw numbers alone, bar charts, for example, are great for comparing categorical data, such as sales performance. in different products, product categories. Scatter plots are useful for visualizing relationships between two. Numerical variables, such as sales revenue and advertising spending line charts, can show trends over time, making them ideal for tracking sales performance or website traffic fluctuations.
Now I know that there are a lot of online courses to master all of these key concepts, but if you want to learn in a fast, efficient and structured way, the course careers data analysis course could be the one for you, the The course is for people from all types of backgrounds, whether you're looking for a university alternative or looking to make a career change. In fact, you can take a free introductory course to find out what it's like to work in data analytics and if it's a good fit. for you before you commit to spending your time and hard-earned money on the entire course.
Now I would highlight the courses of the course. and I partnered in this video, just check the ratings and reviews from your trusted pilots, they are good, very good, so if you want to go ahead and try the introductory data analytics course for free, just use the link on description below to get back to the basics. concepts, if you master them, I guarantee you will lay a solid foundation for your journey into data analytics. Remember that practice is key to mastery, feel free to apply what you've learned to real-world data sets or participate in hands-on exercises.
To reinforce your understanding, tip number two would be to master spreadsheets like Excel or Google Sheets. This is an essential step on the path to becoming a competent data analyst. These spreadsheet tools serve as the cornerstone of data manipulation analysis and visualization for professionals across various industries. While they may seem like basic software applications, delving deeper into their functionalities can significantly improve your analytical skills. Now which one you should learn should really depend on the companies you want to apply to, it's rare that companies use both so just pick one and go with it. My personal recommendation would be Excel because it is much more powerful than Google Sheets but it is also much more expensive.
New businesses are very likely to use Google Sheets. Why, since it's free, a Microsoft contract with the full suite of applications is expensive. This 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 Microsoft's hefty contract. No matter what you learn, the main goal will always be the same: manipulate data effortlessly, whether you're dealing with large data sets or simple spreadsheets. Mastering the art of data manipulation is crucial. Functions and formulas let you organize, filter, or combine data to suit your analytical needs.
In fact, I collect the most popular Excel formulas and functions in one Excel. file to make it easier for you to quickly reference, understand, and use them daily. The Excel file has popular math date, time and text and many other formulas and data manipulation functions with real life examples and explanations to help you really apply the formulas in a business context. I will put the link in the description below in in case you want to check it out Beyond basic data manipulation, Excel and Google Sheets offer a wide range of functions and formulas that facilitate complex calculations, functions such as V-lookup, x-lookup, and index.
Matching allows you to retrieve specific data points from large data sets, helping you with tasks like inventory management and sales analysis or functions like average sum if and count if allowing you to perform calculations based on specific criteria, such as calculating total sales for a certain product or average revenue per customer Once you are comfortable with applying the functions and 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 summarize, analyze and visualize large sets of data, with these you can quickly generate reports that summarize sales by product category, region or time period, providing valuable information on performance metrics and trends.
Okay, I think that's enough spreadsheets, so let's move on to tip number three. and dive into other data analysis tools that can significantly accelerate your journey to becoming a competent 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 visualization at first glance, learning programming languages ​​can seem intimidating, especially if you're new to coding, but with the tools of artificial intelligence from On The Rise you can easily supplement your learning just by asking and asking the right questions from the GPT chat nowadays, so the barrier to entry to coding is definitely, much lower than what it used to be SQL means structured query language and is a powerful tool for extracting, querying and manipulating data stored in databases with SQL, you can write queries to retrieve specific information from large data sets efficiently, for example you could write a SQL query to extract customer information, such as demographic data or purchase history from a customer database, or you can analyze sales transactions, track inventory levels, and identify trends within your data.
SQL is a skill that almost all data analyst positions look for, so take the time to learn it well. I have a complete free tutorial course on SQL databases and I will put the link in the description below, so be sure to check it out after you finish watching this video. Now Python and R are probably the two most popular programming languages ​​for data. analysis thanks to its versatility and extensive libraries designed for statistical analysis and machine learning with libraries such as pandas numpy and math plot lib python offers a complete set of tools to manage, manipulate and visualize data python can be used to automate repetitive tasks, such as generating reports of weekly sales or clean up messy data Python's machine learning capabilities allow you to create predictive models that can forecast future sales trends or customer behavior based on historical data.
By leveraging Python, you can streamline your analytics workflows, uncover actionable insights, and similarly drive business results. R provides a rich ecosystem of packages and libraries designed specifically for statistical analysis and data visualization with packages such as ggplot deeper and tier R offering an easy-to-use interface for data manipulation and visualization, making it the Preferred choice among statisticians and data scientists who dive into data analysis tools like SQL Python or R. Crucial step to improve your skills as a data analyst. I highly recommend learning SQL and a programming language, either Python or R. If you're wondering which one I would choose, it's certainly Python, but that doesn't mean R is useless.
Both are good. and knowing R is definitely better than not knowing it and last but not least, tip number four, make sure you build a well-rounded portfolio to establish yourself as a competent data analyst and attract potential employers. Your portfolio serves as tangible evidence of your skills and demonstrates your ability to extract insights from data and solve real-world problems effectively by showcasing a wide range of 3-5 projects. I would argue that it can highlight his proficiency in various analytical techniques and methodologies, which will ultimately differentiate him from other candidates in a competitive job market. I have a complete end-to-end portfolio project playlist that you can follow to create your own data analyst portfolio projects and I also have the ultimate portfolio that you can easily check out to see what it looks like as it contains four projects of mine With exclusive features and to finish, expert reviews, presentations and detailed summaries, think of the ultimate portfolio as a one-stop shop for all your projects where you can publish your entire portfolio data to the web without having to code anything.
I'll put the link for the portfolio project. Playlist and Ultimate Portfolio in the description below, be sure to use them to create your own data portfolio. One approach you could take would be to focus on projects that show your ability to analyze data and gain meaningful insights; For example, you could create a project. whereAnalyzes 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. Another great project idea is to predict customer behavior based on past data.
For example, you could develop a predictive model to forecast customer churn for a subscription-based service or, on an e-commerce platform, by analyzing factors such as customer demographics, purchase history, and engagement metrics, you can create a model that predicts the probability of a customer leaving the platform, this type of project demonstrates your proficiency in predictive analytics and your ability to provide useful information for companies looking to retain customers and improve customer satisfaction, regardless of the specific project you choose to include in your portfolio, it is essential to document your process thoroughly and start clearly. define the problem statement or objective of the project along with any relevant background information or context, then describe the steps you took to collect, clean and pre-process the data, ensure transparency and reproducibility in your analysis, detail the analytical techniques and methodologies that employed, provide insight into your thought process and decision-making rationale, whether you used statistical analysis machine learning algorithms or data visualization techniques, articulate how each method contributed to achieving the project goals, finally summarize your findings and conclusions, highlighting key ideas and practical recommendations for stakeholders, visual aids such as charts, graphs and interactive dashboards can improve the presentation of your results and make them more accessible to non-technical audiences and I fear we have reached the end of my tips forNow, if you enjoy content like this, be sure to check out some of my other videos here.
Thank you so much for taking a little time out of your day to watch this. See you at the next one.

If you have any copyright issue, please Contact