Are you interested in pursuing a career as a data analyst and want to learn more about what it entails?
Data analysis is an interesting domain with great employment opportunities. If you’re considering learning data analytics or data analytics department structure, it’s natural to be concerned about the technical skills required.
Data analysts use skills such as programming in Python, SQL database querying, and statistical analysis to execute their work.
While mastering these skill sets can be tough, with the right strategy and course of action, it is entirely possible to master them and get a job as a data analytics professional.
Read ahead to find out how to learn data analytics from scratch and understand the basics of data science.
What is Data Analytics?
The practice of studying raw data in order to derive relevant and actionable insights is known as data analytics.
Most organizations gather huge volumes of data all the time, but this data is worthless and irrelevant in its raw form.
This is where data analytics comes into the equation.
These key insights are then used to derive intelligent business decisions for an organization. Therefore, a data analyst will collect raw information, classify it, and then analyze it, converting it from unintelligible statistics to logical, understandable information.
After collecting and analyzing the data, the data analyst will present their results in the form of ideas or proposals for the company’s next moves.
Consider data analytics to be a type of business intelligence (BI) that is used to resolve complex challenges and obstacles within a company.
Companies and organizations can better comprehend their industry, audience, and business as a whole when empowered with advanced analytics. As a result, these companies become far more capable of making better decisions and planning.
What Are the Different Types of Data Analytics?
The field of data analytics is vast. Data analytics is classified into four types: descriptive, diagnostic, predictive, and prescriptive analytics. Each category has a distinct aim and role in data analysis and is one of the most common data analytics methods in business.
Let’s explore them one by one:
Descriptive analytics is a streamlined and superior type of analysis that analyzes what has happened in history. The two primary techniques utilized in descriptive analytics are data mining and data aggregation.
The data analyst first collects the data and summarizes it (this is the aggregation step) and then “mines” the data to uncover patterns. The data is then presented in a manner that a broad audience, not just data experts, can understand.
While descriptive analytics focuses on the “what” aspect, diagnostic analytics investigates the “why” factor. When performing diagnostic analytics, data analysts will first look for irregularities in the data, which are any instances that cannot be understood by the data in question.
For instance, if the data shows a sharp reduction in monthly sales for May, the data analyst must determine the cause. To accomplish this, they will begin the discovery phase, in which they will identify any more data sources that may reveal more about why such irregularities occurred.
Finally, the data analyst will seek causal links, such as activities that may correlate with the decline in sales. Data analysts can employ regression analysis, probability theory, filtering, and time-series data analytics at this point.
Predictive analytics, as the term suggests, attempts to forecast what will happen in the near future. This is the stage at which data analysts begin to generate useful, data-driven insights that the organization can utilize to influence its next course of action.
Predictive analytics evaluates the possibility of a future occurrence based on previous data and probability theory. And while it does not guarantee hundred percent accuracy, it does eliminate much of the guesswork from critical business decisions.
Prescriptive analytics aims to provide guidance on what actions to be taken and decisions to be made. In other words, prescriptive analytics demonstrates how you can capitalize on predicted outcomes. When performing prescriptive analysis, data analysts will explore a variety of hypothetical scenarios and evaluate the various actions that the organization could take.
Prescriptive analytics is a more advanced sort of analysis that may entail the use of machine learning, algorithms, and computational modeling techniques.
However, the successful application of prescriptive analytics can have a significant impact on the company’s decision-making process and, eventually, on the outcome.
We’re quite familiar with the concept behind data analytics and its many types by this point, but it’s all still a little unclear.
How is data analytics used in practical applications?
Let’s find out!
What is Data Analytics Used For?
The truth is that data is everywhere. It is, in fact, all around us and in every little thing. This implies that data analytics has nearly limitless applications across all types of businesses and organizations.
Data analytics, in general, is used to make more efficient and intelligent decisions, lower total organizational expenses, create more profitable goods and services, and optimize business operations.
More specifically, data analytics can be used to:
- Evaluate the efficacy of advertising and marketing campaigns
- Detect and eliminate inefficiencies in a specific method
- Improve marketing strategies through better targeting and personalization
- Increase client interaction on social media
- Forecast future sales and purchase patterns
- Boost client acquisition
- Increase customer retention
- Discover new revenue streams and prospects for growth and innovation
- Come up with solutions for risk mitigation
- Boost the efficiency of the supply chain
- Identify, forecast, and prevent fraud in the finance and insurance sectors
As you should know that anywhere data is collected, data analytics can be employed to gain meaningful insights and help predict future actions.
Let’s explore some of the tools and techniques that a data analytics expert might use.
How to Learn Data Analytics from Scratch: Tools and Techniques for Beginners to Learn
Which are the different data analytics tools?
If you want to work as a data analytics specialist, you need to be familiar with at least some of the tools listed below. Learning how to use the tools of the trade, like most things, is a part of the process.
Microsoft Excel is a software application system that allows you to format, organize, and calculate data using formulae within a spreadsheet program.
Data analytics professionals can use Microsoft Excel to conduct basic queries and build graphs, pivot tables, and charts. Excel also has a macro programming language feature known as Visual Basic for Applications (VBA).
Tableau is a prominent and robust business intelligence and data analytics software application tool mostly used for data visualization. Tableau is used by data analytics experts to convert raw data into visual dashboards, maps, worksheets, and charts.
This makes data more accessible and understandable for everyone, allowing data analytics professionals to successfully share their key insights and recommendations.
Python is a known programming language used to manage and wrangle massive amounts of data. Data wrangling is a terminology used in the field to describe the act of processing data for analysis by processing it in multiple formats (for instance, merging and grouping data).
Python has several built-in capabilities that aid with data wrangling or analyzing, making it a suitable alternative to Microsoft Excel, particularly when working with larger datasets.
Structured query language or as more commonly known as SQL, is a programming language used to access and manipulate databases. Consider SQL to be a tool that allows you to interface with data stored in a database, which is required if you want to get relevant data and prepare it for analysis.
Because most big companies keep their data in some form of SQL, mastering it is crucial if you want to work as a data analytics professional.
Power BI is a business analytics tool that allows you to visualize data and share key insights across your company. Power BI, like Tableau, is largely used for data visualization. Tableau is designed for data analytics experts, whereas Power BI is a more generic business intelligence platform.
Which are the different data analytics techniques?
Before we get into learning some essential data analytics techniques, let’s first identify the two kinds of data you might deal with: quantitative and qualitative.
Quantitative data is defined as anything that can be measured, such as the number of individuals who responded “yes” to a certain question on a poll or the number of sales achieved in a given year.
Qualitative data, on the other hand, cannot be measured and includes things like text written in an email or what individuals say in an interview.
Data analytics experts often work with quantitative data; however, there are some professions that will require you to gather and analyze qualitative data as well. Therefore, it’s a wise option to be familiar with both.
With that in perspective, below are a few of the most common data analytics techniques:
This technique is used to predict the relationship between two or more variables. You can use regression analysis to determine whether certain factors can accurately forecast another factor.
The primary objective of regression analysis is to make forecasts. However, it is vital to remember that regressions can only be utilized to evaluate whether or not there is a correlation between a set of variables on their own. They cannot tell you much about cause and effect.
This technique assists data analytics specialists in identifying the hidden variables that influence an individual’s behavior and decisions. Finally, it consolidates the data from multiple variables into a few super-variables, making the data easier to deal with.
For instance, if you have three different variables representing customer satisfaction, you could utilize factor analysis to consolidate these variables into a single, comprehensive customer satisfaction score. As a consequence, factor analysis is often referred to as dimension reduction.
This method is used to identify patterns within a database. Cluster analysis divides data into sections that are internally homogeneous but outwardly heterogeneous. In other words, elements in one group have to be more similar to one another than to those in other clusters.
Cluster analysis shows how data is dispersed across a database when no predetermined groups or categories exist. Cluster analysis, for instance, can be used in marketing to discover discrete target groups within a wider client base.
These are just some of the techniques that data analytics specialists utilize. Dispersion analysis, Monte Carlo simulations, discriminant analysis, and text or content analysis are some additional commonly used techniques.
Data analytics professionals are required to exhibit specific skills and capabilities in addition to being well-versed in the tools and techniques we’ve discussed so far. These skills are frequently acquired while enrolled in a data analytics program.
What Skills Does One Need to Become a Data Analyst?
Here are some of the most essential soft and hard skills for becoming a data analytics expert:
Expertise in mathematics and statistics
Because data analytics professionals work with numbers for so much of their time, it comes as no surprise that you need to have a mathematical understanding.
Mastery of programming languages such as Oracle, SQL, and Python
As we’ve seen, data analysts use a variety of computer programming languages to accomplish their tasks. This could appear challenging at first, but nevertheless, it is nothing that cannot be learned with practice.
Excellent problem-solving abilities
Data analytics experts have a wide range of tools and techniques at their fingertips, and understanding which to use when is an important part of their ob. Remember that data analytics is all about responding to questions and addressing business problems, which necessitates sharp problem-solving abilities.
Now that you are aware of the tool, techniques, and skills required to become a data analytics expert, let us now go through some tips that would smoothen your learning process.
4 Tips That Would Help You Learn Data Analysis Skills
Buy an online course to learn the fundamentals of data analytics
If you’re new to data analysis, it can be beneficial to begin with, a structured program that covers the fundamentals and introduces you to some of the data analytics techniques and tools.
The following are the things that are covered generally in a data analytics online course:
Data structures and data types
Data processing and preparation
Data analysis techniques
Storytelling and data visualization
Using data to provide answers
You can assess your current skill sets and identify ways to improve by gaining a wider perspective.
To avail of free online courses, you can try Udemy and Datacamp. They have a wide range of short course that covers many aspects of data analytics study.
Improve your data analyst skillset gradually over time
Pick one skill and delve deeper after you’ve established a foundation in data analysis with some sort of an organized perspective. Choose building confidence in a skill in which you already have some expertise or confronting your biggest shortcoming head-on.
Work on real data projects to hone your skills
You need not wait until your career gets started professionally as a data analyst to begin gaining experience and enhancing your skill sets. As you understand the concepts behind the practice, put them to actual use in reality by practicing on real data.
Look for online courses or training programs that include hands-on assignments and projects, or do it on your own by creating your own projects with open-source data sets that are free to use.
Join a data analytics community
It is never too early to begin networking. Consider joining a community of other data analytics learners and data professionals while you go through a course at university, best data analytics books, or your very own data project.
When you hit a snag in a program you’re working on or can’t seem to work out a statistical problem; you can seek help from your data analytics community.
Frequently Asked Questions
How do beginners get started with data analytics?
Beginners in data analytics can start to learn about the many forms of data and how it is processed to create meaningful insights. There are several online data analytics courses out there ( free and paid) that will provide you with a comprehensive introduction to data analytics and how it is used.
What should I start with when it comes to data analytics?
Learning about different forms of analytics and the many industry areas where data analytics is being used is an excellent place to start in the domain of data analytics.
What are the different sorts of data analytics?
Data analytics can be classified into descriptive analytics, diagnostic analytics, prescriptive analytics, and predictive analytics. Based on the questions a data analyst is attempting to solve, each category is employed for a specific purpose. A data analyst, for instance, would utilize diagnostic analytics to determine why something transpired.
Get Started with Learning Data Analytics!
As you should know, it can take anywhere from 10 weeks to four years to improve the skill sets required to become a professional in the field of data analytics.
The fact that there are numerous paths to becoming a successful Data Analyst explains this broad range of learning curves. Many data analytics professionals have typically started with a bachelor’s degree in computer science or IT (the four-year way).
However, it is becoming more frequent for data analytics aspirants to secure jobs straight out of data analytics training programs and courses, which can be finished in as little as 10-12 weeks.
We hope that this blog has outlined all the necessary details to get you started with learning data analysis and help you become a data scientist or data analyst.