EASIEST WAY TO BECOME A DATA ANALYST

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 recognize that there are lots of people in much worse situations than me but I also know that there are many people who are in much better situations compared to me my parents were immigrants to Hungary a poor Eastern European country 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 the UK but trust me it was not easy I learned English on my own took a student loan which I am still paying off every month I worked as a bartender during uni and studied day and night and applied to jobs 24/7 during my masters I got rejected did many many times before landing my first job and when I started my career I worked even harder I was up at 5:00 a.m. so I can study for certifications and do some exercise before work I worked many late nights whilst I was in Investment Banking I came to the UK with absolutely nothing but a luggage a backpack and a desire to make something of myself to seize the opportunity that my parents have worked so hard for to give me I'm a big believer that you should worry about the things that you can control and what I was able to and am still able to control is how I approach my life my tasks my workload I mind my own business and try to do the absolute best I can to live the absolute best life I can with all this said this video is about the easiest way I know to become a data analyst so let me jump into the first tip which would be to learn the basics well very well now this might sound like cliche 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 in math to grasp the concepts at the core of data analysis are key Concepts such as understanding data types statistical measures and data visualization 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 ordinal data which contains order but not precise differences between values an example could be a rating scale from poor 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 occurring value in a data set which could be useful in identifying popular products or customer preferences data visualization 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 products product categories Scatter Plots are useful for visualizing 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 the course is for people with all kinds of backgrounds whether you're 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 now I would highlight that course careers and I partnered on this video just check out their trust pilot ratings and reviews they're good good really good so if you want to go ahead and try the introductory data analytics course for free just use the link in the description below back to the fundamental concepts if you master these I guarantee that you'll lay a solid foundation for your journey into Data analysis remember practice is key to Mastery don't hesitate to apply what you've learned to real world data sets or engage in Hands-On exercises to reinforce your understanding tip number two would be to master spreadsheets like 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 visualization 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 are want to apply to it's rare that firms will use both so 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 startups 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 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're dealing with large data sets or simple spreadsheets mastering the art of data manipulation is crucial functions and formulas enable you to organize filter or combine 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 I'll put the link in the description below in case you want to check it out Beyond basic data manipulation Excel and Google sheets offer a vast array of functions and formulas that facilitate complex calculations functions like V lookup xlookup and index match enable you to retrieve specific data points from large data sets helping you in tasks such as inventory management and sales analysis or functions like sum average if and count if 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're comfortable with applying the functions and the formulas I'd 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 data sets with these you can quickly generate reports that summarize 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 three 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 visualization at first glance learning programming languages may seem intimidating especially if you're new to coding but with AI tools on On The Rise you could easily supplement your learning by just prompting and asking the right questions from chat GPT nowadays so the barrier to entry to coding is definitely so much lower than it used to be SQL stands for structured query language and it's 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 an SQL query to extract customer information such as demographics or purchase history from a customer database or you could analyze sales transactions track inventory levels and identify Trends within your data SQL is a skill that pretty much all data analyst positions look for so please spend the time to learn it well I have an entire free SQL database tutorial course and I'll put the link in the description below so make sure you check it out after you finished watching this video now 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 and math plot lib python offers a comprehensive toolkit for handling and manipulating and visualizing data python can be used to automate repetitive tasks such as generating weekly sales reports or cleaning messy data Python's machine learning capabilities enable you to build predictive models that can forecast future sales Trends or customer Behavior based on historical data by leveraging python you can streamline your analytical workflows uncover actionable insights and drive business outcomes similarly R provides a rich ecosystem of packages and libraries specifically designed for statistical analysis and data visualization with packages like deeper ggplot and tier R offers a userfriendly interface for data manipulation and visualization making it a Preferred Choice amongst statisticians and data scientists diving into Data analysis tools like SQL python or R represents a crucial step in advancing your skills as a data analyst I'd strongly recommend learning SQL and a programming language either python or R if you're wondering which one I would pick it's certainly python but that's not to say that R is useless both are good and knowing R is definitely better than not knowing are and last but not least tip number four 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 five I'd say you can highlight your Proficiency in various analytical techniques and methodologies ultimately setting yourself apart from the other candidates in a competitive job market I have an entire endtoend portfolio Project Playlist that you can follow to create your very own data analyst portfolio projects and I also have the ultimate portfolio that you can easily check out to see what good looks like as it contains four projects of mine with exclusive and to end expert writeups presentations and detailed summaries think of the ultimate portfolio as a One-Stop shop for all of your projects where you can publish your entire data portfolio to the web without having to code anything I'll put the link for the portfolio Project Playlist and the ultimate portfolio in the description below make sure to use them to create your very own data portfolio one approach you could take would be to focus on projects that showcase your ability to analyze data and derive meaningful insights for example you could create a project where you analyze uh 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 um an e-commerce platform by analyzing factors such as customer demographics purchase history and engagement metrics you can build a model that predicts the likelihood of a customer leaving the platform this type of project demonstrates your Proficiency in Predictive Analytics and your ability to provide actionable 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 context next outline the steps you took to collect clean and pre-process the data ensuring transparency and reproducibility in your analysis detail the analytical techniques and methodologies you employed providing insights into your thought process and decision making rationale whether you utilize statistical analysis machine learning algorithms or data visualization techniques articulate how each method contributed to achieving the project objectives finally summarize your findings and conclusions highlighting key insights and actionable recommendations for stakeholders visual aids such as charts graphs and interactive dashboards can enhance the presentation of your results and make them more accessible to non-technical audiences and I'm afraid we've come to the end of my tips for now if you enjoy content like this make sure to check out some of my other videos right here thanks so much for taking just a little bit of time out of your day to watch this I'll see you in the next [Music] one

Channel: Mo Chen

Views: 28,002

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Id: IUQlYbKwiWg

Length: 15min 9sec (909 seconds)

Published: Wed Mar 13 2024