Data analytics is the process of examining data and extracting actionable insights. Once the raw data is pulled together, it’s analyzed in order to draw a conclusion that’s meaningful for business execs. It’s used to reveal trends and metrics that would otherwise be lost in the mass of information.

The information retrieved from data analytics is useful because it can be used to help business make decisions and optimize procedures. The process can be broken down into four basic steps:

  1. The first step is to determine the data requirements. For example, the data points might be separated into different groups based on age, gender, demography, and income. Data values could be numerical, but might also be in any other form.
  2. The second step in data analytics is the procedure for collecting it. This can be done through computers, cameras, and online sources.
  3. The data collected must be organized, preferably on a spreadsheet.
  4. The last step is data cleansing. In this step, the data is cleaned up before it is analyzed to double-check for possible duplication errors.  

Strong data analysis skills could get you hired in a highly rewarding field, and lead to a life-long career full of growth opportunities. If you’ve got your Excel basics down pat, we’re here to share some of the best courses that are sure to lead you to a great data analyst job.

Types of Data Analytics

Data analytics can be broken down into four categories:

  1. Descriptive Analytics: Descriptive analytics gives an account of what has happened in the past, over a given period of time. For example, in the sales world, providing sales data in a graph makes it easier for an organization to assess the sales performance for a given period of time.
  2. Diagnostic Analytics: Diagnostic analytics focuses on understanding the reasons behind events and behaviors in business. It answers the question “why has this happened?” For instance, did the COVID pandemic impact sales?
  3. Predictive Analytics: As it suggests, predictive analytics predicts future behavior based on the current data by applying mathematical models. For instance, what will happen to sales if so and so happens in the future?
  4. Prescriptive Analytics: Prescriptive analytics help develop a course of action to deal with a possible situation. This analysis can be used to make immediate and long-term decisions. For instance, what should be done if so and so happens?

Where Is Data Analytics Used?

Various industries are starting to realize the importance of data analytics to grow their business. Some of the industries that heavily rely on data analytics are as follows:

Travel: People nowadays tend to appreciate personalized travel experiences. Data analytics can be used in personalized marketing and customer segmentation. Additionally, it is through analysis of data that travel companies are able to analyze customer sentiments and develop tourism forecasting models.

Gaming: Gamers create terabytes of data every day when playing video games. Game publishers and developers who do not use this massive amount of data to their advantage tend to fall behind. To avoid this, they use data analytics tools to collect real-time information on how players play a game. This information can then be used to improve the gameplay experience.

Healthcare: This is another sector that has become increasingly reliant on data. In this field, data analytics can help derive information on the systematic waste of resources, the performance of individual practitioners, and even track the health of a population.

Energy Management: Energy optimization, distribution, and building automation are all closely managed by data analytics.


What Does A Data Analyst Do?

A data analyst collects raw data and performs statistical analysis on it. They discover insights and solve problems. Today, the demand for data analysts has grown tenfold as companies are looking to exploit big data for business growth. The development of a relational database, which allows data analysts to use SQL to retrieve information from databases, has also contributed to the growth of an already growing field.

How to Become A Data Analyst

Here are a few courses that you can take if you’re an aspiring data analyst:

Data Analytics Bootcamp - Thinkful: This course covers everything you need to know to start your career as a data analyst. You’ll learn all the necessary skills from qualified instructors, and you’ll continue to be supported with a 1-on-1 mentor and support team. This course will set you up for success and guarantees a job after graduation.

This bootcamp is unique since you’ll get much more than a skills-based data analysis education. In addition to a thorough curriculum, you’ll also benefit from:

In this course, you’ll learn how to reshape data to make sense out of it. You’ll be able to catch the missing details in the data, which is considered to be the most critical part of data analysis. By the end of the course, you’ll be able to:

Data Analyst In Python - Dataquest: This course covers the fundamentals of Python and will provide you with an overview of Python programming. You’ll also learn to code using mobile application data, and work with different kinds of data, update variables, and manipulate Python dictionaries. This course will enable you to create your own data science project.

By the end of this course, you’ll be able to:

Python For Data Science and AI - IBM: In this intermediate course, some key concepts of Python programming language will be covered. You’ll learn data cleansing, which is considered to be a critical skill for any data analyst or data scientist job. Essentially, you’ll learn to cleanse, standardize, and analyze messy data using Python’s DateTime module.

By the end of this course, you’ll be able to:

Pandas And NumPy Fundamentals - Dataquest: Pandas and NumPy are two popular Python open-source libraries. As the course begins, you’ll learn concepts such as vectorized operations. You’ll also learn how to select data based on its value using a method called Boolean indexing. Pandas is one of the most widely used open-source libraries for data science. After knowing the basics of Pandas and NumPy, you’ll be able to frame and filter data.

By the end of this course, you’ll be able to:

Storytelling Through Data Visualization - storytellingwithdata.com: Graphs are one of the most effective ways to visualize data. But simple graphs don’t always suffice when it comes to representing complex datasets. This material will teach you to communicate insights from data by telling stories using data visualization. Plots like Seaborn are used to create attractive and catchy visuals and to draw appealing statistical graphics. Specialized tools like Basemap, a toolkit used for 2D graphical representations, also helps analyze data in a business context.

Data Cleaning Project Walkthrough - Dataquest: This course will walk you through each phase of data cleansing. You’ll learn the ins and outs of computer correlations in Pandas. Once completed, you’ll be able to visualize and analyze data. Ultimately, you will be asked to build your own portfolio by performing data analysis using maps and visualizations.

By the end of this course, you’ll know how to:

What Skills Do You Need To Become A Data Analyst?

If you’re ready to turn your Excel and Tableau skills into a career, here are the basic skills you’ll need to practice:

We hope this article has helped you gain a clear understanding of what it takes to become a data analyst, and how to get started in this high-paying tech field. Expand your research through reliable data analyst resources to make sure that this is a career you’ll love.

If you’d like any more guidance to help you launch your career as a data analyst, Thinkful’s team is here to support you and point you in the right direction.

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