Data is a fantastic resource, but it’s only applicable if collected and analyzed to make the right decisions and identify potentially valuable trends. That’s why we need data analytics. Consequently, there’s a greater demand for data analysts. The job market needs more data analysts, and the best way to create them is to offer good data analytics courses.
This article answers the question, “What is a data analytics course?” We will explore data analytics, outline the prerequisites and requirements for data analytics courses, what a typical data analytics course syllabus looks like, and the benefits of taking these courses.
We begin our data analytics journey with a definition.
What is Data Analytics?
Data analytics involves collecting, analyzing, and interpreting data to help organizations make better decisions and achieve their goals. Data analytics involves diverse processes that create a data science pipeline or workflow, including exploring, gathering, managing, preparing, storing, and evaluating data. Finally, the data or the data analysis results are typically shared with the organization’s decision-makers or business users. Data analytics is helpful in many different sectors and industries, including business, education, health care, and government.
Ultimately, data analytics techniques allow businesses to take raw data and discover patterns from which they can extract valuable insights. So, data analysis helps companies make informed decisions, create more effective marketing strategies, streamline operations, and improve customer engagement, loyalty, and experience, among other things.
But let’s take a brief detour and address a common mistake.
The Difference Between Data Analytics and Data Analysis
People often use the terms “data analytics” and “data analysis” interchangeably, which isn’t accurate. Although there’s some overlap, these are two similar yet distinct terms. Data analysis is actually a subset of data analytics.
Data analysis involves finding meaning from data to be used to make informed decisions. Data analytics has a broader scope and deals with using data and analytical techniques and tools to uncover new insights and make forecasts and predictions, typically for the benefit of an organization. So, data analysis focuses on exploring raw data, while data analytics uses various processes (including data analysis) to turn data into actionable information.
With this distinction in mind, what’s a data analytics course?
What is a Data Analytics Course?
A data analytics course’s short and sweet definition is “An online program designed to teach students foundational data analytics skills necessary to collect, prepare, analyze, and present data. This Data Analytics Bootcamp also covers basic programming skills in selected languages such as Python, R, and SQL and appropriate tools like Microsoft Excel and Tableau.
Although there is no single definitive data analytics course composition, a good course should cover the following subjects:
- Algorithm Analysis
- Data Structures and Algorithms
- Exploratory Data Analytics
- Introduction to Programming
- Introduction to Artificial Intelligence
- Machine Learning
We will discuss relevant subjects and topics in greater detail later when discussing a syllabus.
What Are the Prerequisites and Requirements Before Taking a Data Analytics Course?
Although a good data analytics course will cover some of the requirements you need to work in data analytics, it doesn’t hurt to establish a solid foundation of appropriate skills. Although you don’t necessarily need a college degree, a bachelor’s degree in mathematics, computer science, computer programming, data science, or statistics wouldn’t hurt.
Also, it’s wise to start learning programming languages, especially Python, and cultivate basic proficiency in Excel and Tableau.
Ultimately, a candidate wanting a career in data analytics should have some proficiency in the following hard and soft skills:
- Computer programming
- Data visualization
- Data warehousing
- Spreadsheet applications
- Time management
A Typical Data Analyst Course Syllabus
For starters, there’s no such thing as the ultimate course syllabus. Syllabuses (or syllabi if you’re really into Latin) vary according to institution, program, and overall curriculum. With that in mind, here’s a sample syllabus that covers the most essential data analytics subjects.
Introduction to Data Analytics
- Benefits of Data Analytics
- Box Plot
- Central Limit Theorem
- Confidence interval
- Data Analytics Overview
- Data Types, Measure of Central Tendency
- Data Visualization for Decision Making
- Descriptive Analytics
- Descriptive Stats
- Diagnostic Analytics
- Graphical Techniques
- Importance of Data Analytics
- Measures of Dispersion
- Predictive Analytics
- Prescriptive Analytics
- Sampling Funnel
- Sampling Variation
- Skewness & Kurtosis
- Types of Data Analytics
Introduction to Statistical Analysis
- ANOVA and Chi-square
- Correlation and Regression
- Counting, Probability, and Probability Distributions
- Data Cleaning
- Estimation and Hypothesis Testing
- Imputation Techniques
- Sampling Distributions
- Scatter Diagram
- Economics of Business
- Ethics and Social Responsibility
- Managing Human Resources
- Motivating Employees
- Operations Management
- Teamwork in Business
- Structuring Organizations
- The Foundations of Business
- Analysis of Unstructured Data
- Extracting and Presenting Statistics
- Observational result
- Statistical Techniques
- Survey Sampling
- Creation of a Bar Chart
- Customized Geographic Map
- Scatter Plot
Data Structure and Algorithms
- Array, Iteration, and Invariants
- Binary Search Trees
- Efficiency and Complexities
- Hash Tables
- List, Recursion, Stacks, and Queues
- Natural Language Basics
- Processing and Understanding Text
- Semantic and Sentiment Analysis
- Text Classification
- Text Similarity and Clustering
- Text Summarization
Software and Tools Typically Used in Data Analyst Courses
Here’s a breakdown of some of the most popular tools used in teaching data analytics courses. Naturally, these are also the tools you find in data analytics positions.
- OpenRefine. This program, also called GoogleRefine, is a data-cleaning tool that helps you clean up data for analysis. It is typically used to clean up messy data when the analyst parses and alters website data.
- Python. Python has become massively popular in data analytics circles thanks to its vast collection of libraries such as NumPy, Matplotlib, Pandas, SciPy, and Scikit-Learn. Statisticians, engineers, and data scientists perform data analytics using Python libraries. It also helps that the language is easy to learn, scalable, flexible, enjoys widespread community support, and has libraries dedicated to data visualization and graphics to build plots.
- QlikView. QlikView provides fast in-memory data processing, sending the results to the end customer. QlikView features data compression, data association, and data visualization.
- RapidMiner. RapidMiner is a robust integrated space that IT professionals can use with any kind of data source, including Excel, Access, Teradata, Oracle, Sybase, and Microsoft SQL, among others. Most of its applications are predictive analytics, which includes data mining, text analytics, and machine learning.
- SAS Analytics. SAS analytics is a tool increasingly used in business intelligence (BI), data science, and machine learning applications. SAS Analytics equips organizations with all the necessary tools to monitor their critical BI metrics and produces powerful analytics and comprehensive reports that let decision-makers make well-informed decisions.
- Tableau. Tableau gives users free access to any data source (e.g., Excel, Corporate Data Warehouse, etc.), then generates maps, interfaces, and visualizations with real-time updates for the Internet.
Now, let’s look at the benefits of taking a data analyst course.
The Benefits of Data Analyst Courses
So, is it worth taking a data analyst course? For starters, this is the digital age, and there’s no such thing as being too proficient or versatile regarding IT skills. Granted, we all only have a limited amount of time and money to take many courses, so why narrow it down to data analytics?
Here are the benefits of data analytics.
- Data analytics positions pay well. According to Indeed.com, a data analyst in the United States can earn an annual average of $77,073.
- Data analytics help people make better decisions. No organization wants to spend time and resources on strategies that ultimately won’t work. Data analytics helps companies make better, more informed decisions, mitigating risks.
- Data analytics increases your problem-solving skills. Data analytics is all about using data to solve problems, and it challenges your mind to think analytically.
- Data analytics careers offer plenty of opportunities for growth and diversity. You start as a data analyst but can end up as a business intelligence analyst or systems analyst.
- Data analyst jobs are highly secure. Organizations of all sizes are turning to big data to help them make better decisions and become more viable companies. This means that professionals who work with data are in high demand and will stay that way for the foreseeable future.
Do You Want to Learn More About Data Analytics?
Suppose you’d like a career in data analytics or upskill your current skill set to take on the data analytics role in your organization. In that case, you should check out this essential data analytics training bootcamp. This six-month program will teach you about Excel, SQL, Python, and much more, everything you need to take on the challenge of data analytics.
Data makes today’s world go round, and the people trained to work with that data can write their own ticket to a great future. Sign up today and get trained on these valuable, timely skills!
What can you learn in data analytics courses?
You learn the skills and tools needed to perform data analytics, including data structures, data mining, and exploring, gathering, managing, preparing, storing, and evaluating data.
Why take a data analytics course?
A comprehensive, hands-on data analytics course teaches novices and experienced people the skills to become a data analyst or fulfill that role in their organization.
Does data analytics require coding?
The typical data analytics role requires coding, although it can vary depending on the organization.
Who is eligible to take data analytics programs?
Everyone is eligible to take data analytics bootcamps, but it helps to have some proficiency in programming, computer science, IT, or math.