Best Data Analytics Courses - Q1 2023
Our experts have evaluated the top courses across the leading online learning platforms based on strict criteria to identify the best choices for you.
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Content Coverage (5/5)
This course was specifically designed for intermediate to advanced Excel learning for Data and Business Analysts. The course is delivered using version 2016 and Office 365. However, learners using 2007, 2010, 2013, 2016 and 2019 have the same compatibility as shown in the instructional videos.
A total of 57 sections containing 253 lectures, practice exercises and quizzes will require a total of 33 hours of committed time. The topics presented are intermediate to advance in nature and contain the minimal course topics expected at the intermediate to advanced level. The course is available for lifetime access, which will be extremely helpful to those using advanced Excel functions in the workplace for data and business analytics.
Lecture Quality (5/5)
The lecture videos are extremely professional and easy to follow. A Udemy presenter introduces the section and prepares the learner for what to expect in the section. For instance, the difference between the instructor files, and downloadable files for the learner, and when to use the files. There is also a simulation showing you how to go about downloading the files within each module. This presentation may come up more than once to remind the learner of how to navigate the course.
A transcript is available for each lecture on the right pane of the video as well as captioning for the hearing impaired. Captions are available in English only.
Quiz Quality (5/5)
Quizzes comprised of multiple-choice questions are offered at the end of each section and directly after an exercise. The learner can see if their answer is correct or incorrect prior to moving on to the next question. The consistent use of quizzing allows learners to reinforce what they’ve learned before moving on to more complex concepts.
Assignment/Exercise Quality (5/5)
The downloadable resources are well-designed and correspond directly to the information being presented in the lecture, and some contain formulas to get the learner started. The goal is for the learner to practice along with the video and see if they come to the same result as the instructor – an excellent teaching practice.
Certificate Brand Quality (3.5/5)
The Microsoft Excel Data Analysis Toolkit Bundle is instructed by Deborah Ashby of Simon Sez IT, a partner of Udemy. Simon Sez IT has hosted courses for over 640,000 learners, with 95,000 views worldwide. Simon Sez IT learners are comprised of individuals, small businesses, and Fortune 500 companies.
The course offers a certification of completion after all lectures have been reviewed, which is critical given the learning is job specific for Data and Business analysts.
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Topics To Look For in a Data Analytics Course
- Understanding data
- Structured vs unstructured data
- Data sources
- Data wrangling
- Data cleaning
- Big data
- Databases
- Data warehouses
- Understanding datasets
- Building pivot tables
- Descriptive vs predictive analytics
- Data visualization
- Understanding data fields, values, and variables
- Transforming variables
- Data storytelling – skills in conveying data into actionable insights and decisions
- Technologies such as Excel, R, and Python
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The Major Online Learning Platforms
Each online learning platform is unique. Some specialize in certain types of content, some partner with major universities or corporations to develop content, and some have special learning features. While not exhaustive, the list below includes the major online platforms whose courses we feature.
Pros / Cons
Pros: Certificates often are offered from major universities or corporations.
Cons: The quality of course content and features available varies dramatically between courses.
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Pros / Cons
Pros: Courses are very similar to those offered in colleges.
Cons: The quality of content and features offered varies dramatically between learning programs.
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Pros / Cons
Pros: A focus on microlearning with consistently high-quality content. Consistent use of quizzes throughout all courses.
Cons: Pricing is skewed toward annual subscriptions, which is far more content (and cost) than most learners need within a year.
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Pros / Cons
Pros: Labs offer a means of gaining practical experience in technical skills.
Cons: There are no incremental quizzes or ways to test learners on specific topics.
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Pros / Cons
Pros: Udemy tends to be one of the more affordable options for individuals looking to learn specific technical skills.
Cons: Highly inconsistent quality between courses.
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Pros / Cons
Pros: Consistent, high-quality micro-content that focuses on practical exercises.
Cons: The catalog is somewhat limited compared to other vendors on this list.
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Pros / Cons
Pros: Many free online classes are offered. Live online classroom experiences are offered for some of their courses.
Cons: Variable quality of courses, with some courses having relatively low quality.
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Pros: Skillshare has a large library of courses related to creative skills.
Cons: Most courses are just a series of lectures with no interactivity, quizzing, or opportunities to practice concepts.
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For more information, check out our detailed post on the best online course platforms.
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Frequently Asked Questions about Data Analytics
How can data analytics help business?
Structuring, organizing, analyzing, and reporting data can greatly benefit businesses. Some examples include:
- Combining disparate data sources to come up with unique insights. For instance, you may have some business data in Excel and other business data in customer relationship databases and e-commerce systems.
- Data-driven decision making – Companies that analyze the right data at the right times can make decisions on facts rather than hunches or strong opinions. Data can also be an equalizer in situations where the opinions of those with strong personalities win out over more reserved personalities.
- Customer insights – These days, there is a wide range of quantitative and qualitative data that can be used to gain customer insights. These include website user data, demographic data, buying behavior data, surveys, and many other forms of data. Understanding the structure of all of these types of data and how they can be used to gain accurate insights is an important job for most businesses.
- Streamlining processes – It is often necessary to gather disparate data in a purposeful way in order to identify bottlenecks and high-cost parts of processes. Doing so can save companies millions in time and money.
Does data analytics require coding ?
Yes – in most cases, data analysts are expected to know some basic coding. This is because analyzing data often requires querying and transforming the data from multiple data sources. Understanding languages like SQL, advanced Excel functions, and Python or R can be extremely helpful for these tasks.
Does data analytics require statistics?
Are data analytics and data science same?
The major difference between data analytics and data science is the scope of analysis. Generally speaking, Data Scientists must be more skilled at statistical models and programming, as they perform sophisticated analyses of data structures. Often Data Scientists have specific domain knowledge (such as Finance or Education). Data Analysts generally perform higher-level analysis – they gather, organize and report on data within a specific scope. Data analysts are not usually as involved in making data predictions as data scientists.
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