In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if the available data is scarce. Big data and data science are concepts often heard together. It is believed that nowadays there are large amounts of data and that data science can draw valuable insights from all these terabytes of information.
SQL is replacing Excel in many fields, and data analysis is certainly one of them. If you are still using Excel as a data analyst, you are missing something very valuable. SQL can make your life easier, as it’s more efficient and faster than Excel. So, how and from where can you learn SQL? How Can SQL Help Data Analyst? You can use SQL to help you with the following work:
Data science is hot right now. If you want to learn more about it, where should you go? Online, of course! Check out our favorite data science sites. Whether you’re a beginner or a pro, these are the sites you should know. Not so long ago, if you wanted information on a topic like data science, you had to look for it—either at your local library or at a university.
Excel spreadsheets are quickly becoming obsolete thanks to the emergence of the latest data analytics tools and languages such as Python, Java, R, and Microsoft HDInsight. However, a large number of companies still use digital spreadsheets, creating a lot of problems for modern business data analysts. Analyzing data through excel is a poor choice because of reasons like errors in data validation, a poor shared workbook feature, no multi-user editing, inaccurate data, and safety concerns, making it necessary for you to switch to better and advanced alternatives.
Data analyst is a relatively new position available at several companies. It’s also a high-salary specialization without a complex learning curve. Thus, many professionals are looking to make a career switch to this burgeoning field. In this article, I’ll explain what skills you need to become a junior data analyst. We’ll also review some tips for making this career change and see what an entry-level data analyst salary looks like.
Indexes are one of the most misused and misunderstood entities in physical database design. A good understanding of indexes and how they solve database performance problems is necessary for any database novice. In this article, we’ll look at basic database indexes and their role in database development. To picture what an index is, consider a textbook. At the end of most textbooks is an index listing all the terms one can find in the text and the pages on which they appear.
SQL wildcard allows us to filter data matching certain patterns in SQL. We use wildcards with the LIKE operator in the WHERE clause of an SQL query to filter data. In this beginner’s article, we’ll look at everything you need to know about basic SQL wildcards. If you like playing cards, then you know that wildcards can substitute any other card in your deck. Similarly, wildcards in SQL strings can substitute one or more characters.
If you plan to learn SQL online but aren’t sure where to start, you’re in the right place. I evaluated the top 7 ranked SQL online courses to help you find the right solution. While summer’s a great time to relax and get away from work, it’s also an excellent opportunity to learn new skills—like SQL! Thanks to online schools, learning new tech skills is super easy, and you can choose from a variety of formats to suit your needs, like courses and tutorials.
R and Python are two of the most popular data science languages, but which one is better? And will Python replace R in the near future? Let’s find out! R vs. Python: the Basics First, some history. R first appeared in 1990; it was derived from the language S, a statistical programming language developed for statisticians. It was (and still is) commonly used in educational settings and is a favorite among biostatisticians.
Think summer is reserved for flying to warm places and hanging out at the beach? Sure! But it’s also a great time to learn new skills that you haven’t had time for. If you recently graduated from high school and want to get a head start on computer programming for college, learning SQL over the summer is a great opportunity. You have nothing to lose and everything to gain—SQL is actually really easy to learn, especially with so much free time over the summer.
Why is June 21 the official start of summer? Let’s see how SQL can help us answer this question. The Summer Solstice Officially, June 21 is recognized as the summer solstice, the longest day of the entire year in terms of daylight. Why? Because on this day, the sun rises early and sets quite late. People in the Northern Hemisphere celebrate the summer solstice with feasts, bonfires, picnics, and traditional dances and songs.
In my previous articles, I explained how you can check for associations between two continuous and two discrete variables. This time, we’ll check for linear dependencies between continuous and discrete variables. You can do this by measuring the variance between the means of the continuous variable and different groups of the discrete variable. The null hypothesis here is that all variances between the means are a result of the variance within each group.
Going from zero to one can be daunting in any endeavor. The same is true for learning new programming languages, even simple ones like SQL. In this article, we’ll take a look at some key insights that will help you understand the nuances of SQL queries. If you’ve never used SQL, you’re in the right place. When learning anything new, you’ll find that there are always some key insights or tips that can help you on your way.
As SQL users, we usually focus on writing queries that return correct results. However, there are more things to consider when you’re writing a query; one of them is query performance. In this article, we’ll look at some examples where query response time is critical. Scene One: 911 Call Center Let’s suppose we’re at a 911 call center, when the phone rings. One of the operators answers the call; a witness reports that a man has been shot.
Will robots replace humans in the near future? As machine learning and artificial intelligence continue to grow in popularity, this question becomes all the more relevant. Which jobs will become extinct, and what will society looks like in the future? If you’re a data analyst whose worried about their job security, don’t worry—there’s still hope for you! In this article, we’ll take a look at the skills a data analyst can acquire to become a data scientist and rise above these pesky robots 🙂
Not all queries are alike, especially in terms of performance. In this article, we’ll look at how you can convert SQL subqueries to joins for improved efficiency. When should I use SQL subqueries? Great question! Unfortunately, there’s no concrete answer. SQL beginners tend to overuse subqueries. Typically, once they find that SQL construction works in one situation, they try to apply that same approach to other situations. It’s only natural.
Brush up on your data science and SQL skills with Vertabelo Academy’s interactive courses. Why Vertabelo Academy? You get instant access to lessonsthat teach various concepts of SQL, data science, and programming in R (soon also in Python!). Our courses are appropriate for people who have no prior knowledge of computer science or programming. The only requirement is a web browser. No need to install databases, download example tables, or spend time inventing exercises for yourself.
If you’re familiar with the famous Russian nesting doll, then SQL correlated subqueries should be a peace of cake to understand—subqueries are just queries nested within queries. An SQL subquery is often called an “inner” query; the main query is usually called the “outer” query. This article covers everything you need to know about correlated subqueries. What Exactly is a SQL Correlated Subquery? A correlated SQL subquery is just a subquery that is executed many times—once for each record (row) returned by the outer (main) query.
Curious about becoming a database analyst? Maybe you’ve taken some database courses at university and they really struck a chord. Or maybe you learned online. Now you’re thinking about making a career out of working with databases. Where would you start? What should you expect at each phase of your professional development? In this post, we’ll explore the challenging and exciting world of databases analysis. We’ll go from the very beginning of a career to the apex of professional success.
Learn how to extract data from strings in PostgreSQL using the split_part function. Quite often, we’d like to extract parts of a string when working with text values. A common example is when we have a full name and need to retrieve only the last name. In this article, we’ll examine split_part, a PostgreSQL string-related function that can be used to extract a substring. Why Use String Functions in Your Database?
In my previous article, we looked at how you can calculate linear dependencies between two continuous variables with covariance and correlation. Both methods use the means of the two variables in their calculations. However, mean values and other population moments make no sense for categorical (nominal) variables. For instance, if you denote “Clerical” as 1 and “Professional” as 2 for an occupation variable, what does the average of 1.5 signify?
Excel is a powerful beast that lets you analyze complex data. Yet, operating on big chunks of data can sometimes be a daunting task. Let’s take a look at how SQL can help. Today, we’ll tackle a common problem with importing data to an SQL database, using a real-life example. Suppose your company conducted a survey on the most popular programming trends and preferences, striving to meet the expectations of its users.
If you’re in the US, chances are you’ve been eagerly awaiting the approach of Black Friday just as much as Thanksgiving. Though the shopping frenzy takes hold of nearly everyone, some people have to stick to their budgets and shop prudently. In this article, we’ll take a look at how generating an SQL report can help you track how much your family spent shopping on Black Friday. Storing Black Friday Purchases in a Database Before we can create an SQL report, we first need some data we can use.
Working with the financial aspects of large and small enterprises can be a daunting task for a business professional. In this article, we’ll look at several ways of constructing the perfect SQL report. You’ve probably already heard about SQL from your colleagues or in other areas of your career. If you’re here, you’ve likely concluded that learning SQL will make your professional life easier – and you’re right!