Top 10 Data Analytics Courses on edX With Practical Examples
Set up by Harvard and MIT, edX, an American enormous open online course provider, offers some of the best and most free data analytics courses. Let us further see the data analytics courses on Edx.
Here Are Some of the Data Analytics Courses on Edx:-
1. Course Name – Data Analysis for Decision Making by Rochester Institute of Technology (Rit or Ritz)
Learn to confidently make decisions based on data.
Program Overview
Students, decision-makers, and professional employees can become data literate in both their professional and personal life by pursuing this advanced Professional Certificate Data Analytics Course on EdX. Data literacy will make you a “data citizen” in today’s data-driven world, enabling you to confidently interact and make decisions dependent on facts. You’ll become a leader in promoting a data-literate culture. You will learn to use analytical tools, data visualization, data collecting, and big data to understand business problems and guide decision-making. Students and professionals who want to learn more than just how to use data analysis software might benefit from this course. They can learn how to read, discuss, and write about data in context and gain a grasp of data sources and frameworks.
There Are Three Courses in This Program:
- Data Literacy Fundamentals
- Data Analysis and Processing through Excel
- Data Representing and Visualizing in Tableau
What You Will Learn:
- While you deal with data, learn how to use critical thinking skills
- How to utilise Microsoft Excel to initially arrange and then cleanse data and formulate a data analysis plan
- Utilise Tableau to create graphical representations and valuable tables to arrange and visually present data
- Identify the most essential results from data analysis and combine them with other necessary information.
- Critically evaluate graphical representations for misrepresentation and accuracy of data
Course Highlights:
- Expert Training
- Self-paced
- Three months’ duration
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2. Course Name – Data Visualization and Analytics in Health Care by Ritz
To become literate in data-driven, thoughtful imperatives that impact all elements of health care, learn the best practices in data visualization, analytics, and informatics
Program Overview
Big data is changing the healthcare sector to improve care quality and cut costs, which are important goals for most companies. Employers are scrambling to find suitable individuals with the skills necessary to extract, analyse, and interpret data from patient health records, financial records, insurance claims, and other sources to use it to create an impelling and useful story utilizing health care data analytics.
The main elements of the American healthcare system are studied analytics and data as the course’s first topic. Global learners will be able to compare and contrast the themes with the systems in their own countries even if they are seeing them from a U.S. lens.
You’ll look into the importance of health information technology and health informatics in consumer health, evidence-based medicine, clinical process improvement, and population health in light of this crucial industry background.
You’ll lay out the elements of an effective data analytics course in health care using it as a launch, building the “virtuous cycle” of standardization and data quality necessary for clinical improvement and innovation.
The study of how data visualizations use data to convey a compelling, useful story serves as the course’s final goal. You’ll become more knowledgeable about numerous analytical tools as well as visualizing tools and their characteristics.
During the Advanced Data Analytics Courses on Edx, You’ll Learn
- Recognize the labor forces that are altering the healthcare sector now.
- Outline the ongoing healthcare trends and their influence on patient involvement, cost, and quality.
- Explain how the use of health informatics affects clinical workflow and patient interaction.
- Recognize the elements of health information technology.
- What role does interoperability play in healthcare analytics?
- Enumerate the best practices for data gathering, processing, and analysis.
- Examine how artificial intelligence may affect the analysis and extrication of complex data sets
- Explain data analysis outcomes using an example of visualizing
- Determine the best methods for visualizing
- Make a simple data visualization through healthcare data
Course Highlights:
- Eight weeks’ duration
- Self-paced
3. Course Name – Presentation of Data Analytics for Managers by Michigan (University of Michigan)
Utilizing additional statistical methods, machine learning, and SQL with an emphasis on business applications, discover data science and analyze business data utilizing Microsoft Azure
Program Overview
This advanced Data Analytics course on EdX introduces students to data analytics methods and their applications in business through a blend of lectures, hands-on training, and business case studies. Through the use of various analytical methodologies and data sources, including “big data,” the case studies studied will show how organizations are utilizing these sources to enhance performance. Free web-oriented graphical development environs will provide you with e opportunity to experiment with utilizing some of these tools while you learn by doing. Additionally, you’ll gain awareness of the various business possibilities for data science and be able to choose from a variety of extra opportunities to learn to gain a deeper understanding. This course guides managers on how to use data analytics in business who do not have the skills and background to engage with data analytics themselves. For managers who recognize the chance of using data analytics in the business but lack the skills and knowledge to do it themselves, this course is a great resource.
What You’ll Learn
- The several various data science methodologies and how they might be used in the business through case studies
- Management of data analytics in graphical development environs that allows for easy access to complex tools with no coding
- How to carry out and explain some fundamental data science activities
Course Highlights:
- Eight weeks’ duration
- Self-paced
4. Course Name – Data Analytics for Business by Georgia Institute of Technology (Georgia Tech or Gtx)
In business companies, this course equips students with the leadership skills and knowledge of business analytics
Program Overview
Students can learn business analytics and thus become leaders in these disciplines in business companies by taking these in-depth Data Analytics Courses on EdX. The scientific method of turning data into insights for improved business decisions is taught in this course. It addresses the approaches, problems, and difficulties involved in analyzing business data. By letting students use business analytics algorithms and approaches to solve business challenges, it will help students understand the analytics procedures. The application of relevant business analytics procedures is stressed by the use of examples, which put business analytics approaches in context and show students how to avoid frequent errors.
What You’ll Learn
- Apply analytical thinking to business problems using data. You will be able to consider how and whether business and data analytics might enhance business performance in a thoughtful and systematic manner
- Analyze data through business analytics software, produce business analytics ideas, and offer business insights
Course Highlights:
- Sixteen weeks’ duration
- Self-paced
5. Course Name – Big Data Analytics Through Spark by University of California, San Diego
Know in what way should you analyze gigantic datasets through Spark, Jupyter notebooks, and, MapReduce as a platform
Program Overview
You will learn about the bottlenecks in an extremely parallel calculation in this demanding Data Analytics Course on EdX, which is a component of the Data Science Micro-Masters course, and how to use spark to reduce these bottlenecks. With the help of the Machine Learning Library (MLlib), you will discover how to oversee unsupervised machine learning on huge datasets. You will get practical experience with PySpark inside the Jupyter notebooks environs in this course, as well as in the other courses in this Micro-Masters program.
What You’ll Learn
- Programming of Spark through Pyspark
- Recognizing the estimation tradeoffs within a Spark application
- Carrying out data load and cleansing through Parquet and Spark
- Modeling data with the help of machine learning and statistical techniques
Course Highlights:
- 10 weeks duration
- Trainer-paced
- Trainer-led on a course routine
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6. Course Name – Machine Learning for Data Science and Analytics by University of Columbia
Learn the concepts of machine learning and why algorithms are essential
Program Overview
An introduction to machine learning and algorithmic learning is provided in this outstanding data science and Data Analytics course on EdX. You will gain a fundamental knowledge of machine learning concepts and understand how to use predictive analytics to come up with practical solutions. You will also look at the importance of algorithms within big data analysis.
What You’ll Learn
- What machine learning is and how data analysis and statistics are related to machine learning
- How computer algorithms are used by machine learning to look for patterns in data
- With examples from real-world healthcare concerning preterm birth and genetics, learn how to employ data patterns to generate predictions
- How to use topic modeling to find hidden themes in massive sets of documents
- How to prepare data, interact with missing data, and build customized data analysis solutions for various sectors
- Dynamic programming, grouping, selfish algorithms, and searching are fundamental and often-used algorithmic approaches
Course Highlights:
- Five weeks’ duration
- Self-paced
7. Course Name – Analytics for the Motive of Decision Making by Babson college or (BabsonX)
Learn the essential concepts behind modern data science and gain the ability to analyse various data forms and quality for wise business decisions
Program Overview
Do you want to learn how to use data to make better decisions and avoid making bad ones?
Using data to make informed decisions can give you a definite competitive advantage in business. You will get an understanding of the central concepts of solid statistical reasoning that may be applied in a surprisingly wide range of contexts, sometimes even before there is any data, by taking this data analysis, and statistics course. The importance of key ideas including identifying variation sources, comprehending variation, and judging the relative risk of various decisions will be emphasized.
The development of quantitative models has been spurred by these broad concepts, but in most conventional statistics programs, these ideas are obscured by a mountain of minute procedures and calculations. In this comprehensive data analytics course on edX, the key concepts are maintained, and their application through engaging, and, real-world examples is made clear.
You will investigate issues including whether conventional statistical approaches are still useful in contemporary analytics applications in the advanced Data Analytics Courses on EdX. How can you tackle quantitative problems without falling victim to typical assumptions and fallacies? How can statistical techniques be used in applications that forecast the future using predictive analytics? How might analytics help us comprehend customer involvement better?
Anyone seeking a framework for sound decision-making will find this course to be relevant. Students with a bachelor’s degree who are thinking about pursuing graduate studies in business will benefit from the training.
What You’ll Learn
- Changeability in the practical world and its effects on decision-making
- Data quality and data forms with the help of appropriate visualizing
- When making managerial decisions, especially in start-ups, use data analysis
- Using big data to make informed decisions to big data from no data
Course Highlights:
- Four weeks’ duration
- Self-paced
8. Course Name – Enabling Technologies for Data Science and Analytics: The Internet of Things by Columbia University (Columbia)
Explore the connection between the Internet of Things (IoT) and Big Data
Program Overview
You will discover the key elements of the Internet of Things (IoT) and in what way data is gathered from sensors in this fantastic data science and Data Analytics course on EdX. Additionally, you’ll study how to analyze event data, sentiment examination, facial recognition software, and how to leverage data from devices to inform decisions.
What You’ll Learn
- Networks, basic software, and, arrangements for the Internet of Things (IoT)
- How to automate decisions and control for performance using IoT technologies
- Explain devices including hubs or/and or gateways sensors, cloud computing platforms, and low-power processors
- Gain knowledge regarding the relationship between natural language and data science and audio-visual content analyzing
- Examine research projects pulled out from novels, scientific journals, and, online media
- Assess basic procedures for visual characteristics extrication, classification of content, and high-dimensional indexing
- Processes that can be used to solve problems in mobile product search, web-scale image search engines, copy detection, security surveillance, and, face identification
- Evaluate data gathering, processing, and analysis
Course Highlights:
- Five weeks’ duration
- Self-paced
9. Course Name – Data Analytics Foundations for Everyone by IBM
Know the foundations of Data Analytics and discover the numerous learning pathways, the data ecosystem, the method and lifespan of data analytics, career options, and the field, you may follow to become a data analyst
Program Overview
You will comprehend what big data entails, as well as the characteristics and applications of particular tools of big data processing. Learn about the various elements of a contemporary data ecosystem and the roles that Data Engineers, Data Analysts, and Data Scientists perform in this ecosystem in these highly specialized Data Analytics Courses on EdX. You will learn about data sources, file formats, data ordering, and data repositories. You will comprehend what big data involves, as well as the characteristics and applications of particular tools for processing big data.
You will be introduced to the main duties a Data Analyst carries out daily by taking this universal Data Analytics course on EdX. This covers how they recognize, gather, wrangle, mine, and examine data, and then effectively convey their findings to various stakeholders. Some of the tools that Data Analysts use for each of these activities will be introduced to you.
The characteristics and applications of data lakes, non-relational and relational databases, data marts, and data warehouses, will be covered. You will comprehend the Extricating, Transforming, and Loading (ETL) process, which turns raw data into data that is available for analysis. What are a few of the particular languages that data analytics uses to extricate, prepare, and Analyse data?
After completing this outstanding Data Analytics Course on EdX, you will get to know about the numerous learning directions you can think of for entering this field and the various career opportunities that are available in the domain of Data Analytics.
The thoughtful Data Analytics Courses on EdX conclude with several exercises and a practical lab to assess your comprehension of some of the fundamental activities involved in acquiring, wrangling, mining, analyzing, and visualizing data.
What You’ll Learn
- Describe data analytics and the main steps that make up the data analytics process
- Establish distinctions between various data roles, including business analysts, data engineers, data scientists, and data analysts
- Illustrate the various data sources, file formats, and data layout types
- Describe the applications of Big Data platforms, various types of data repositories, and the ETL technique
- Describe the process and tools used to collect, organize, wrangle, mine, and analyse the data
- List the tools available for gaining knowledge in this area and the various data-related careers available
- Show that you are aware of how to gather, wrangle, mine, analyse and visualize data
Course Highlights:
- Five weeks’ duration
- Self-paced
10. Course Name – Analysis of Data through Microsoft Excel
Build the core knowledge necessary to conduct foundational data analysis using Microsoft Excel (spreadsheets). To assist students in analysing their data, the course covers Excel’s fundamental functionality and major features
Program Overview
Students can learn the core knowledge necessary to utilize Microsoft Excel (spreadsheets) to perform simple data analysis in this interesting Data Analytics course on EdX. The course includes several practical laboratories, videos, examples, and demonstrations to aid in learning, and it concludes with a final assignment project that will enable you to apply what you have learned.
When working with data, whether it is for data analysis objectives, marketing, business, or marketing, Excel is a crucial tool. This course is intended for those who wish to pursue careers as data scientists or analysts, as well as for those who simply need to utilize Microsoft Excel to carry out data analysis within their organization or environment.
The usage of functions for data cleansing and wrangling as well as techniques for data analysis such as pivot tables, grouping, and refining will be useful skills that learners will pick up. This course starts with an explanation of how to import data from numerous various formats and orientation to spreadsheet applications like Google Sheets and Microsoft Excel. With the use of grouping, refining, and pivot tables on a spreadsheet, you will learn how to do some fundamental data wrangling and data cleansing tasks throughout this introductory course before expanding your understanding of data analysis.
With every hands-on laboratory, you will get more experience in the wrangling and management of data and begin to comprehend the crucial function that Microsoft spreadsheets may play in a data analysis environment. This course places a strong emphasis on practical experience and applied to learn. You can use these newly learned skills to analyse the data in the business world for your final assignment project. Through the conclusion of the course, you will have exhibited the fundamentals of data cleansing and analysis without having to learn any coding, started working with a variety of data spreadsheets and sets and dealt with many data sets. No prior knowledge of spreadsheets or coding is necessary for this course, making it simple to begin utilizing Excel for data analysis.
What You’ll Learn
- The foundations of Microsoft Excel (spreadsheet) applications
- How to carry out fundamental Microsoft Excel (spreadsheet) tasks
- Significance of data quality
- How to import file data into Excel
- The foundations of analyzing data employing a spreadsheet
- How to refine and group data
- How to cleanse and prepare data
- How to examine data through Microsoft Excel (spreadsheet)
Course Highlights:
- Five weeks’ duration
- Self-paced
Frequently Asked Questions
Q1. Are all these courses offered by edX?
No, edX is just an online course provider. It offers courses that are designed and provided by institutions across the world.
Q2. Are all the data analytics courses the same?
No, all the data analytics courses are distinct. They have their characteristics and designed keeping the demands of unique individuals.
Q3. Which data analytics is the best?
See, all the courses are great on their own. You have to select the courses after considering all your demands.
Q4. What are the fees for the data analytics courses on edX?
Kindly, search for the course name and visit edX’s webpage to know more about the fees.
Conclusion
So finally, we have provided you with the list of the best data analytics courses on edX. Like it was previously mentioned, all the courses are unique on their own and they offer distinct advantages. But you have to select the course after carefully thinking about your needs and requirements, and eligibility.
