Learning Data Analysis: A Step-by-Step Guide

Learning Data Analysis

As we normally do, Let’s start by defining Data Analysis:

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making.

Why Should you learn Data Analysis? 

Data science and analysis positions are often the hardest for a company. Thanks to the exploding demand for data professionals, there are a ton of open roles and not enough candidates to fill them.

Still not convinced? Well, I guess it’s only fair we consider the data!

  1. Job Growth: The foreseen job development for statistical surveying investigators (another term for data examiners) between 2014-2024 is 19%, given data from the Agency of Work Measurements. That’s a lot of new positions being created.
  2. Demand: There is a demand for people who can use data to perform reporting and analysis, thus helping businesses and organizations make important and critical decisions.”
  3. Salary: Data analysts are paid well even if they don’t continue on to data science or engineering! How much do data analysts make? On average, a data analyst can mzake from $75,000 to $110,000 a year!
  4. Competitive Advantage: The ability to ask questions about your data is a powerful competitive advantage, resulting in new income streams, better decision-making, and improved productivity.
  5. Universal Need: According to Symon He and Travis Chow, instructors of Intro to Data Analysis using EXCEL for Beginners, “Every business generates data. But [its value] depends on your ability to process, manipulate, and ultimately translate that data into useful insights.”

In my opinion, there are only two things to consider when learning Data Analysis, focus on learning the processes necessary for the compilation and experimentation of data.

Let’s talk about it in more detail:

1. Focus on learning the process and techniques of working with data

Every programming language has idiosyncrasies, which can lead to coding frustration. It’s easy to get bogged down with the syntax of a programming language, so you should focus on learning data analysis skills. R lets you do this because the writing is well-documented and many users have created packages to make data analysis easier. This enables you to ask questions about your data to learn how to solve problems with the data. The syntax will change between languages, but the concepts and ideas for working with data will not.

Once you learn how to load data and do some basic tasks in R, you can focus on learning more about data manipulation, machine learning, and data visualization. You need to learn how to gain insight from data by understanding the data set’s structure and the variables’ distributions and relationships. Many textbooks and examples of using the R programming language in each of these domains exist. The R programming language also has many user-created packages, simplifying working with data. Here are some recommended packages that can help you learn more about the skills for working with data.

2. Experiment and play with data!

Find a data set and start applying what you learn! You can grab a data set online (many governments and nonprofits will have published information) or ask a co-worker or manager if they have data they are trying to understand.

If you ever get stuck, refer to the documentation for R or a user-created package. The documentation will have examples you can copy, paste, and run to figure out what the code does. If you’re still scratching your head about how to work with your data, you can take to Cookbook for R, R-bloggers, or StackOverflow to find curated examples, blog posts, and explanations.

Data analysis can seem overwhelming at first, but your journey into learning data analysis doesn’t need to be so stressful. You can start today by learning the basics of the R programming language. Then, you can choose a skill you want to learn (summarizing data sets, correlation, or random forests). And finally, you can put your skills into practice by working with data. As you work with more data, you will see yourself as a proficient R programmer and data analyst.

Next Up the Steps to learn Data Analytics are as follows:

Excel: is the most basic and fundamental tool for data analysis. Excel makes it easy to explore, clean, and analyze data with in-built functions like pivot tables. Although many argue that Excel is losing its edge in data analysis, it is still the most used tool. Excel is also great for calculations because it possesses several formulas.

Statistics & Probability: This is a wide net, so let me narrow it down. Focus on descriptive statistics. Descriptive statistics is an analysis based on present data and is what most data analysts practice. If you’re interested, you can look into inferential and predictive analyses.

Predictive modeling: Predictive modeling is a process that uses data mining (to forecast outcomes. Each model is made up of several predictors, which are variables that are likely to influence future results. Once data has been collected for relevant predictors, a statistical model is formulated. The model may employ a simple linear equation or a complex neural network, mapped out by sophisticated software. The statistical analysis model is validated or revised as additional data becomes available.

SQL: Excel is great for many things, but it does have it’s a limitation and can house only so much data. This is where SQL comes in. Today’s data is housed in database warehouses and requires query languages, like SQL, to retrieve the data. SQL is the most used database querying language.

R – R is an open-source programming language and software environment, commonly used for statistical computing within data-heavy roles such as data mining and statistics.

While R can seem overly complicated at the start, R is worth your consideration for those looking for a programming language with a lot of meat on the bones.

In fact, several well-known organizations are taking advantage of R’s impressive statistical features.

Data Visualization and reporting Techniques

Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends, and correlations that might go undetected in text-based data can be exposed and recognized more comfortably with data visualization software.

Bottom Line

In conclusion, data analysis is wide and open to just about anybody. It has attractive pay prospects too among other benefits. So jump in already!