Speaker 1: What's up guys, Wally here. Welcome back to the channel. In today's video, I'm going to be talking about some of the different competencies and resources that can be helpful for people that have no data analysis experience and are interested in getting started in learning data analysis. Recently, I was discussing with a friend who is quite interested in data analysis and I was asking him what tool he planned on learning first and his answer was Python. And I asked why Python and he says because it's the hottest tool right now and everyone is talking about it. So, I know there's a lot of buzz around learning Python to be a data analyst and it's easy to get carried away with that buzz but try not to get carried away. Rather, you want to sieve through the noise and find a tool that suits your style and then you can gradually build competence around that tool. So, I'm going to provide some options to some resources which are going to be free, extremely beginner-friendly, and I'm going to focus on the core competencies to get you started. Generally, there are four main areas to build competence in data analysis and very broadly, I'll list them as data querying, data cleaning, analyzing data using statistical or non-statistical methods, and data visualization. So, as a data analyst, you'll be required to be knowledgeable using one or a combination of Microsoft Excel, SQL, Tabu, or Power BI, Python, or R. The reason why Microsoft Excel is first on the list is because if you work in a corporate organization or plan to work in a corporate organization, most of the data analysis tasks you will do will be on Microsoft Excel. Typically, your colleagues will send you an Excel file to work with and it might be unnecessary and sometimes inconvenient to switch into Python to do an analysis that can be done using Excel. As a beginner data analyst, it is important to learn Microsoft Excel and you should really be aiming to become knowledgeable in areas involving the use of sort and filter, VLOOKUPs, pivot tables, formulas, and basic data cleaning methods. To master Excel for an absolute beginner, here are the courses I would recommend. Introduction to Data Analysis using Excel. You'll find this course on EDX. It's a free course for four weeks where you'll learn how to use pivot tables, aggregate functions, a basic knowledge of formulas, cell referencing, and many more. After taking this course, then you can jump into the next course, which is also on EDX, which is Analyzing and Visualizing Data with Excel. Here, you'll learn how to import data from different sources using Power Query, manipulate your data using DAX formulas, and prepare for data analysis into Power BI. One major limitation with Excel is it's limited to one million rows of data, so when working with a large set of data, it starts to become slow and might crash on you a few times. But if your job does not require a huge data set, then Microsoft Excel is good enough. Nonetheless, I cannot deny the huge demand for data analysts with SQL and Tableau skills, so I get the need for picking those skills up as well. SQL is a language that lets you, as a data analyst, interact with a database. A database is a collection of data stored in a computer system, usually large volumes of data, where accessing it requires some form of structured coding. As a data analyst, you might be required to run SQL queries, and to do so, you must first understand the basis of relational database and structure, specifically things like primary key, foreign key, relationships between tables. Then, you go ahead and learn SQL functions like SELECT statement, FROM statement, WHERE statement. GROUP BY, ORDER BY, JOINS are areas you want to master. For learning SQL, again, you can check out this edX course on SQL. It's called SQL for Data Science. Alternatively, you can check out Khan Academy's SQL course called Intro to SQL Querying and Managing Data. The third competence to learn is either one of Tableau or Power BI. I just finished learning Tableau and what I discovered was it's not so much different from Power BI. They do basically the same thing, which is to visualize large datasets. The cool thing about both two is you can import your data directly from SQL or a CSV file or any other database to create your visualizations. At this point, because you're already familiar with visualizations using Excel, it becomes super easy to understand how to use either one of Tableau or Power BI. To learn Power BI, once again, head over to edX and enroll for the Analyzing and Visualizing Data with Power BI course. In this course, you'll learn how to identify different types of data visualizations and how to create fully functional reports and dashboards. Now, finally, Python. As a data analyst, Python is used to manipulate data similar to what you would do on SQL. Python is also used in data science for highly mathematical and statistical analysis. When using Python, what you're really learning is how to use statistics to analyze data. Remember my friend who's learning how to use Python. So far, he's gotten a hang of some of the theoretical concepts. However, there is the practical side which he's currently struggling with. And by practical side, I mean how do you know what sort of problems or challenges that should be solved using statistics. Not all analysis requires statistics and that's something you must be aware of as a data analyst. So some of the basic statistical concepts you should be aware of or you should know are sampling, frequency distribution, mean-median mode, measures of variability, standard deviation, probability, significant testing, z-scores, confidence intervals, and A-B testing. All these can be learned and practiced using Microsoft Excel Data Analysis Toolpack even before picking up a Python class. So if you ask me, learning Python is quite optional, which is the whole essence of this video. I do not want people to feel they have to learn Python or R to become data analysts. Trust me, I know data analysts that have worked solely on Excel and they are fine. Even right now as a data analyst, most of the work that I do is on Microsoft Excel. And to really show you this, here is a data analyst role on Andela. Andela is a renowned technology slash IT talent pool company in Africa. And let's jump straight to the requirements for this role. Here you have must have deep understanding of statistics, have strong Excel skills, have experience with SQL and database management, have experience with any statistical analysis package such as Excel, SPSS, and SAS. So the bottom line is you can be a data analyst with the simple tools at your disposal. Start with Excel, move over to SQL if you need to, try your hands on different visualizations, TabView or Power BI. If you're really curious and you want to take it a step higher, by all means, use Python. At the end of the day, as long as you can use the tool to get the job done, then fantastic. You must find ways to apply your knowledge in specific projects to dip in your experience and showcase your skills. Without projects, you will not be able to retain what you've learned for long. Kaggle.com is one place to download some data sets and play around with them. Apply the knowledge you've learned and experiment with different visualizations. It will help increase your confidence levels significantly. Alright, if you have any questions, do leave them down in the comment section below. I'd be happy to answer them. And if this video has been helpful to you in any way, do smash the like button so that others can benefit as well. Thanks for watching and I'll see you in the next one.