+91 9580 740 740 WhatsApp

Top 17 Data Analytics Courses On LinkedIn For Data Enthusiasts

Are you looking for data analytics courses on LinkedIn? You’ve come to the right place since this post includes a list of data analytics courses on LinkedIn. You may get your ideal career as a data analyst by taking these courses and developing these abilities. Data analysts use technologies for data analysis to study information and work with their teams to generate ideas and business plans. You’ll require proficiency with tools for data analytics and visualization, as well as mathematics, statistics, communication, and managing data. Investigate this in-demand profession. Data analysis, analytics, visualization, Microsoft Excel, SQL, Business Intelligence, and Tableau are the most in-demand talents.

Top Data Analytics Courses On LinkedIn

Data Analytics: An Understanding

The phrase “data analytics” is broad and covers a large variety of information analysis techniques. information associate analytics techniques could also be accustomed to any type of info to induce an understanding which will be utilized to create things higher. Techniques for data analytics will highlight measurements and patterns that may rather be buried within the ocean of information. The potency of a firm or system might then be improved by exploiting this data to optimize procedures.

Data analytics are crucial since they enable companies to increase performance. By finding more cost-effective ways to try and do business and retentive heaps of information, corporations might facilitate cutting expenses by incorporating it into their business strategy. in addition, an organization might utilize information analytics to enhance business decisions and track shopper preferences and trends to develop recent, improved merchandise and services.

Data analytics expertise is highly sought after by businesses that want to use the data they have gathered to produce insightful business decisions. Demand for these abilities is growing as a result of the epidemic and the ensuing “new normal” of distant employment. Many people are using online learning resources to step up their game and obtain the data analytics abilities that will most likely set them apart. And whether you want to study such abilities for business or fun, our selection of multiple data analytics courses on LinkedIn will assist you in getting up to speed quickly so you can operate some of the most popular technologies!

Considering the context of this, we have created this ranking of the top data analytics courses on LinkedIn that you might want to take. For individuals seeking the most comprehensive experience possible with access to data analytics courses on LinkedIn Learning’s full course library or learning pathways, or for those hoping to take several courses or develop skills in other fields, the platform is ideal. In all, data analytics courses on LinkedIn Learning provide hundreds of units of instruction across more than 13 different categories.

List of Data Analytics Courses on LinkedIn

Following is a list of the data analytics courses on LinkedIn, each of which may be completed to earn a degree.

1. Learning Data Analytics

Robin Hunt outlines data analytics and the duties of data analysts in this program. She then demonstrates how to recognize your data set, including any data you lack, as well as how to understand and summarise data. She also demonstrates how to carry out more complex operations including cleaning data, connecting data sets for reporting, and drawing process diagrams.

Learning Objective:

  • Recognize the skills of a data analyst.
  • Apply SQL statements using the correct syntax.
  • Interpret existing data.
  • Explain how data is cleaned.
  • Demonstrate how to use joins.
  • Identify different types of data.
  • Describe how to model data.

Instructor: Robin Hunt

Level: Beginner

Duration: 3.5 Hours

2. Data Fluency: Exploring and Describing Data

Join Barton Poulson throughout this session as he focuses on the principles of data fluency, or the capacity to utilize data to draw conclusions and select your next course of action. Barton demonstrates how using graphs and statistics to describe data may help you attain your objectives and make wiser decisions. He focuses on generic techniques that may be used to tackle individual problems rather than focusing on specific instruments. Learn how to gather data, visually explore it, and characterize it using statistical techniques.

Instructor: Barton Poulson

Level: Beginner

Duration: 4.5 hours

Syllabus

  • Introduction
  • Think with Data
  • Prepare Data
  • Adapt Data
  • Explore Data
  • Describe Data
  • Probability and Inference
  • Continuing Your Data Fluency Learning Quest

3. Power BI Essential Training

Gini von Courter walks you through using this potent toolkit in this course. In her introduction, Gini discusses the web-based Power BI tool, outlining how to enter data, produce visuals, and organize those representations into reports. She goes through how to use Power BI Q&A to ask questions concerning your data and how to pin visuals to dashboards for sharing. She also covers Power BI Mobile and demonstrates how to employ the data modeling features of Power BI Desktop.

Instructor: Gini von Courter

Level: Beginner + Intermediate

Duration: 3.5 hours

Syllabus

  • Introduction
  • Get Started with Power BI
  • Get Data
  • Create a Report with Visualizations
  • Modify and Print a Report
  • Create a Dashboard
  • Ask Questions About Your Data
  • Share Data with Colleagues and Others
  • Using Power BI Mobile Apps
  • Using Power BI Desktop
  • Conclusion

Recommend Read,

4. Azure Spark Databricks Essential Training

Throughout this course, Lynn Langit delves into the patterns, tools, and best practices that programmers and DevOps experts can utilize to effectively construct data solutions on Apache Spark using Azure Databricks. Lynn explains how to create large data workloads using Azure Databricks notebooks, tasks, and services in addition to setting up clusters. She also examines machine learning architecture patterns, Azure Databricks data pipelines, and how to leverage ML Pipelines.

Instructor: Lynn Langit

Level: Beginner + Intermediate

Duration: 3.5 hours

Syllabus

  • Introduction
  • Big Data on Azure Databricks
  • Core Azure Databricks Workloads
  • Scaling Azure Databricks Workloads
  • Data Pipelines with Azure Databricks
  • Machine Learning Architectures

5. Qlik Sense Essential Training

Learn how to use this robust platform for data analysis and visualization in this course. Curt Frye, the instructor, walks through installing or connecting to Qlik Sense, importing and summarising data, creating apps from data sources, and creating and managing tables. A canvas that you can fill with data, graphs, and other visualizations is provided by Qlik Sense sheets, which he also walks through managing. Learn how to construct and edit a range of data visualizations, such as bar charts and histograms, as well as how to make PivotTables, reports, sort, and filter data. You’ll also discover how to mix the sheets in your app with elements, text, and photos to tell a story that amply demonstrates your point.

Syllabus

  • Introduction
  • Introducing Qlik Sense and QlikView
  • Manage Data Sources and Tables
  • Manage Qlik Sense Sheets
  • Create Charts for General Data
  • Create Charts, Text, and Images for Dashboards
  • Create PivotTables and Reports
  • Sort and Filter Data
  • Conclusion

6. Excel Statistics Essential Training: 1

The principles of inferential and descriptive statistics are covered in this course by Joseph Schmuller, who also demonstrates how to use them in Microsoft Excel, a user-friendly, affordable program that comes with a variety of strong statistical tools. Joseph gives instructions on how to arrange and present data, comprehend sample distributions, test hypotheses, and draw any conclusions using built-in functions, charts, and the Analysis Toolpak add-on. He covers a variety of topics, including regression analysis, estimation, variance, variability, and more. You ought to be able to comprehend and apply fundamental statistical ideas to a range of data after the course.

Instructor: Joseph Schmuller

Level: Intermediate

Duration: 3.5 hours

Syllabus

  • Introduction
  • Excel Statistics Fundamentals
  • Types of Data
  • Probability
  • Central Tendency
  • Variability
  • Distributions
  • Normal Distributions
  • Sampling Distributions
  • Estimation
  • Hypothesis Testing
  • Testing Hypotheses about a Mean
  • Testing Hypotheses about a Variance
  • Independent Samples Hypothesis Testing
  • Matched Samples Hypothesis Testing
  • Testing Hypotheses about Two Variances
  • The Analysis of Variance
  • After the Analysis of the Variance
  • Repeated Measures Analysis
  • Hypothesis Testing with Two Factors
  • Regression
  • Correlation
  • Conclusion

Also check,

7. Learning Excel: Data Analysis

With the aid of Excel’s built-in data analysis and visualization capabilities, this course teaches you how to unleash the potential of the data within your business. The central limit theorem is introduced by author Curt Frye after laying the groundwork with fundamental ideas and fundamental computations including mean, median, and deviation. Then he demonstrates how to use scatterplots, graphs, and charts in Excel to display data, relationships, and potential outcomes. Aside from testing hypotheses, modeling various data distributions, and computing covariance and correlation across data sets are other topics he addresses.

Instructor: Curt Frye

Level: Intermediate

Duration: 3.16 hours

Syllabus

  • Introduction
  • Foundational Concepts of Data Analysis
  • Visualizing Data
  • Testing a Hypothesis
  • Utilizing Data Distributions
  • Measuring Covariance and Correlation
  • Calculating Probabilities, Combinations, and Permutations
  • Performing Bayesian Analysis
  • Conclusion

8. Apache Spark Essential Training

Get familiar with Spark in this course and learn how to use this well-known processing engine to get accurate and thorough analyses of your data. Ben Sullins, the instructor, gives a platform overview before delving into Apache Spark’s various parts. He investigates executing machine learning algorithms using MLib, illustrates how to build streaming analytics applications leveraging Spark Streaming, and much more. He also teaches how to analyze data in Spark using PySpark and Spark SQL.

Instructor: Ben Sullins

Level: Intermediate

Duration: 1.5 hours

Syllabus

Introduction

  • Introducing Apache Spark
  • Analyzing Data in Spark
  • Using Spark SQL to Analyze Data
  • Running Machine Learning Algorithms Using MLlib
  • Real-Time Data Analysis with Spark Streaming
  • Connecting BI Tools to Spark
  • Conclusion

9. Learning Data Visualization

Join Bill Shander, a data visualization expert, as he walks you through the processes of transforming “facts and statistics” into “story” to appeal to and meet our need for knowledge. This course is designed for anybody who uses data and must explain it to others, including researchers, data analysts, consultants, marketers, and journalists.

Instructor: Bill Shander

Level: Intermediate

Duration: 4 hours

Syllabus

  • Introduction
  • Big Idea
  • Information Hierarchy
  • The Analog Process
  • Storytelling
  • Visual Display
  • Interactivity
  • Conclusion

10. Tableau Essential Training

Learn the skills you’ll need to use Tableau 2020 to analyze and present data so you can make smarter, more data-driven choices for your business in this course. Learn how to set up Tableau, establish a connection to a data source, and sort and filter your data. Curt Frye, the instructor, also provides examples of how to build and work with data visualizations, such as showcase tables, charting, scatter diagrams, maps, and dashboards, as well as how to publish your visualizations. We also recommend that you look through the whole Tableau training collection on LinkedIn, which, depending on your level of experience, offers more than 700 (!) results.

Instructor: Curt Frye

Level: Beginner

Duration: 5 hours

Syllabus

  • Introduction
  • Introducing Tableau
  • Managing Data Sources and Visualizations
  • Managing Tableau Worksheets and Workbooks
  • Creating Custom Calculations and Fields
  • Analyzing Data
  • Sorting and Filtering Tableau Data
  • Defining Groups and Sets
  • Creating Basic Visualizations
  • Formatting Tableau Visualizations
  • Annotating and Formatting Visualizations
  • Mapping Geographic Data
  • Creating Dashboards and Actions
  • Conclusion

11. Data Analytics for Business Professionals

Author and economist John Johnson demonstrate to CEOs and decision-makers how to utilise analytics to create data-driven choices and achieve a competitive edge in this basic overview. See examples of actual applications of analytics first. Discover how to design questions, a process that may be almost as illuminating as discovering the answers, before examining the distinctions between predictive and prescriptive analytics. John then demonstrates how to gather, purge, and consolidate data from various sources across your company and spot data errors.

Instructor: John Johnson

Level: Intermediate

Duration: 1.16 hours

Syllabus

  • Introduction
  • Data Analytics in the Business World
  • Predictive and Prescriptive Analytics
  • Asking the Right Question
  • Unlocking the Data Within
  • Understanding Averages
  • Sampling
  • Cherry-Picking
  • Forecasting
  • Correlation versus Causation
  • Conclusion

12. Learning Data Analytics Part 2

This course might get you off to a solid start if you’re thinking about focusing on your studies or pursuing a job in data analysis. Learn how to deliver high-quality data sets, precise visualizations, and practical knowledge to get the most out of your data in this course.

Learning Objective:

  • Investigate ideas associated with data analytics.
  • Describe how to design queries using wildcard characters.
  • Analyze various data visualization types.
  • Describe the application of slicers.
  • Describe the terms used to create visualizations.
  • Establish a distinction between reference and duplicate data sets.
  • Considerations for permits and licenses should be noted.
  • Skills included

Instructor: Robin Hunt

Level: Intermediate

Duration: 3.5 hours

Recommend read,

13. Predictive Analytics Essential Training: Data Mining

This course presents that viewpoint through the eyes of a seasoned professional who has finished several real-world projects. A self-employed data miner and author, Keith McCormick focuses on segmentation analysis and prediction models, covering association rules, clustering algorithms, and classification trees.

Instructor: Keith McCormick

Level: Intermediate

Duration: 2 hours

14. SQL: Data Reporting and Analysis

Learn how to write a small amount of SQL code to obtain the data you need in this course. Data from the database will be more than simply something you can extract; you’ll be able to merge, organize, and relabel it to create the precise report you need. Join Emma Saunders as she demonstrates how to create straightforward SQL queries utilizing a publicly available online database for data reporting and analysis. Learn how to sort, organize, and filter data while formatting or calculating results using built-in SQL procedures. Learn how to run more complicated searches, such as those that connect data from several database tables. She also introduces perspectives, procedures, actions, and variables last but not least.

Instructor: Emma Saunders

Level: Intermediate

Duration: 2.5 hours

Syllabus

  • Introduction
  • Prepare to Code in SQL
  • Use SQL to Report Data
  • Group Your SQL Results
  • Merge Data from Multiple Tables
  • More Advanced SQL

15. R Essential Training: Wrangling and Visualizing Data

In this course taught by instructor and data scientist Barton Poulson, you will learn the fundamentals of R and begin analyzing your data for insights. The lectures cover installing R and code packages that increase R’s functionality as well as how to get acquainted with R. Additionally, you get hands-on experience with R and RStudio for basic statistical analysis, data visualization, and modeling. You’ll have a solid understanding of R’s strength and flexibility by the conclusion of the course, and you’ll know how to use it to explore and analyze a range of data.

Learning objectives

  • Installing R
  • Entering data
  • Packages for R
  • Importing XLS, XML, and JSON data
  • Visualizing data with ggplot2
  • Creating charts, histograms, scatterplots, and graphs
  • Converting data
  • Filtering cases and subgroups
  • Recoding data
  • Creating scale scores

Instructor: Barton Poulson

Level: Intermediate

Duration: 4.5 hours

16. Artificial Intelligence Foundations: Machine Learning

The definition and three different forms of machine learning—supervised, unsupervised, and reinforcement—are covered in this course. Then you may learn how to analyze your large data sets for patterns using well-known methods like regression analysis, clustering, and decision trees. Finally, you may discover some of the difficulties that might arise while using machine learning.

Instructor: Doug Rose

Level: Beginner

Duration: 1 hour

Syllabus

  • Introduction
  • What Is Machine Learning?
  • Different Ways a Machine Learns
  • Popular Machine Learning Algorithms
  • Applying Algorithms
  • Common Challenges
  • Conclusion

17. Data Cleaning in Python Essential Training

The course’s instructor, Miki Tebeka, discusses why clean data is crucial, what mistakes might result, and how to find, stop, and correct problems so that your data is always error-free. Miki describes the several kinds of data mistakes that might happen, along with missing or incorrect data values. He explains how design faults, machine-introduced errors, and human errors may contaminate your data and then demonstrates how to spot them. He delves into mistake avoidance, using methods like transactions, data pipelines, and automation. In his last section, he lists many techniques to correct problems, such as renaming fields, changing types, joining and separating data, and more.

Instructor: Miki Tebeka

Level: Intermediate

Duration: 1 hour

Professional Courses from IIM SKILLS

FAQs

Q1. How does LinkedIn employ data analytics?

Data analysts use technologies for data analysis to study information and work with their teams to generate ideas and business plans. You’ll require proficiency with tools for data analytics and data visualization, as well as arithmetic, statistics, communication, and dealing with data.

Q2. Are data analytics courses on LinkedIn Learning worth taking?

LinkedIn offers a variety of courses from beginner to experienced level courses to upskill in the field of data analytics. The adaptable style offers a manageable learning experience as many online courses do. If you intend to keep improving your skill set, LinkedIn Learning’s monthly membership is also a good investment.

Q3. What is covered in data analytics courses on LinkedIn?

The Data Analytics courses on the LinkedIn syllabus cover a variety of subjects, including data integration, testing hypothesis, data exploration, Python or R programming, descriptive statistics, inferential statistics, and regression analysis.

Q4. What does data analytics aim to achieve?

Data analytics’ primary objective is to use technology and statistical analysis to the data to find patterns and solve problems. Businesses are depending more and more on data analytics to examine and improve corporate operations, improve decision-making, and improve financial performance.

Conclusion

That is it about the list of data analytics courses on LinkedIn. We hope the above article about the data analytics courses on LinkedIn assists you to land your dream job. Data analysis is the most sought-after skill and profession nowadays among businesses wanting to produce useful business insight from the data they have gathered.

We have gathered the best possible data analytics courses on LinkedIn in the above-mentioned list with the best learning and upskilling experience possible.

 

 

Leave a Reply

Your email address will not be published. Required fields are marked *

Call Us