Data analysis has proved to be an essential component of the decision-making process for businesses. Irrespective of the field you are working in, be it marketing, tech, product, finance, design, or any other, your analytical capability will help propel your career in a meteoric way. To help you learn the essential data analytics skills, we have handpicked top 10 data analytics courses from the best learning platforms.

 

Top Data Analytics Courses

Introduction to Data Analytics for Business on Coursera (Duration – 11 hours)

Level – Beginner

Course Description 

The course covers the essentials of data analytics practices in the business world. It explores key areas of data analytical process, data creation, storage and access processes, and its usage at organizational levels. You will also learn about advanced investigative and computational methods.

Course Content 

  • Data and Analysis in the Real World
  • Analytical Tools
  • Data Extraction Using SQL
  • Real World Analytical Organizations

 

Business Metrics for Data-Driven Companies by Duke University on Coursera (Duration – 9 hours)

Level – Beginner

Course Description 

The course will help you learn best practices of using data analytics to recognize the most critical business metrics and distinguish them using data.

Course Content

  • Introducing Business Metrics
  • Working in the Business Data Analytics Marketplace
  • Going Deeper into Business Metrics
  • Applying Business Metrics to a Business Case Study

 

Business intelligence and data analytics: Generate insights by Macquarie University on Coursera (Duration – 28 hours)

Level – Beginner

Course Description 

This is among the most popular data analytics courses that will introduce you to analytical tools and skills to understand, analyze, and evaluate the ‘megatrends’ challenges and opportunities. You will learn about the key data analytics concepts such as systems thinking, multi-level perspectives and multidisciplinary methods for envisioning futures, and apply them to specific real-world challenges. 

Course Content

  • Basics of insight generation
  • Basic statistics: Foundations of quantitative insights
  • The normal distribution and histograms
  • Data visualization
  • Advanced charts and dashboards
  • Demand forecasting

 

Data-driven Decision Making by PwC on Coursera (Duration – 9 hours)

Level – Beginner

Course Description 

In this course, you will get an introduction to data analytics and its role in business decisions, basics of big data and its applications, and tools and techniques used in data analysis. You will also get to work in a simulated business setting to have hands-on experience in data analytics.

Course Content

  • Introduction to Data Analytics
  • Technology and types of data
  • Data analysis techniques and tools
  • Data-driven decision-making project

 

Big Data Analytics by Queensland University of Technology on FutureLearn (Duration – 4 weeks )

Level – Beginner

Course Description 

It is a flexible course that offers you practical insight into big data and helps you understand more about the popular tools for collecting, analyzing and visualizing it. 

You will also learn about the use of statistical inference, machine learning, mathematical modelling and data visualization in data analysis.

Course Content

  • Big Data: from Data to Decisions
  • Big Data: Statistical Inference and Machine Learning
  • Big Data: Mathematical Modelling
  • Big Data: Data Visualization

 

Big Data and Education by the University of Pennsylvania on edX (Duration – 8 weeks)

Level – Beginner

Course Description 

With this course, you will learn different data analytics methodologies in educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You will get to learn about applying methods using Python’s built-in machine learning library, SciKit-learn, using standard tools such as RapidMiner, and answering practical educational questions.

Course Content

Week 1 – Prediction Modeling

Week 2 – Model Goodness and Validation

Week 3 – Behavior Detection and Feature Engineering

Week 4 – Knowledge Inference

Week 5 – Relationship Mining

Week 6 – Visualization

Week 7 – Structure Discovery 

Week 8 – Discovery with Models

 

Applying Data Analytics in Marketing by the University of Illinois at Urbana-Champaign on Coursera (Duration – 20 hours)

Level – Intermediate

Course Description 

It is one of the intermediate level data analytics courses. It covers the basics of business analytics in the digital space. Designed for businesses and managers, the course explores the application of data analytics in the real world. You will learn to identify the ideal analytic tools, understand valid and reliable ways to collect, analyze, visualize data; and utilize data in decision-making.

Course Content

Course Introduction – overview of marketing analytics; identifying customer satisfaction

Module 2 – Process of A/B testing, design of experiments, data analysis, hypothesis testing, and Analysis of Variance (ANOVA) 

Module 3 – Binary Outcome model using Logit function; Multidimensional Scaling (MDS)

Module 4 – Conjoint Analysis; Examples of the analysis in R

 

IIM Lucknow & Wiley – Executive Program in Business and Data Analytics  (Duration – 6 months)

Level – Intermediate

Course Description 

The course is designed for mid-level professionals with a minimum of 4 years of experience. It covers Data Analytics, Technology, Domain and Business Expertise for leaders of the digital economy. It developed in collaboration between Indian Institute of Management Lucknow and Wiley. By the end of the course, you will be able to –

  • Solve business problems using data analytics
  • Learn to integrate data analytics, technology, domain knowledge, and business expertise to drive business growth 
  • Gain proficiency to analyze data and use machine-learning algorithms

Course Content

Module 1 – Orientation and Introduction to Data Analytics

Module 2 – Statistics for Data Analytics

Module 3 – Data Handling

Module 4 – Exploratory Analysis

Module 5 – Big Data Analytics and Data Visualization

Module 6 – Machine Learning Techniques

Module 7 – Predictive Modeling Techniques and Exposure to Deep Learning

Module 8 – Analytics across Enterprise Operations and Industries

 

Data Analytics in Health – From Basics to Business on edX (Duration – 4 weeks)

Level – Intermediate

Course Description 

The course sheds light on the usage of big data in health care through novel data analytics based solutions, leading to better diagnosis, care and cure. It covers different real-world approaches using data analytics and explores entrepreneurial opportunities to help develop a business plan.

Course Content

Week 1: Module 1: Diabetes

Health data expenditure, machine learning, data transformation, deriving patterns, opportunities.

Week 2: Module 2: PCR Analysis

Introduction to PCR, data mining, competitive analysis, industry analysis.

Week 3: Module 3: Genomic Data Analysis

Data sharing, data reliability, association rules, market research, marketing, solution optimization.

Week 4: Module 4: Diagnostic Model Research

Workflow, data missing values, density maps, business modelling, requirements and planning, investment needs.

 

Integrated Program in Business Analytics (IPBA) on Jigsaw Academy (Duration – 10 months)

Level – Advanced

Course Description 

IIBA is a comprehensive course that covers Data Science, Statistical Modeling, Business Analytics, Visualization, Big Data, and Machine Learning. You will get to explore different tools and techniques involved in data extraction, data manipulation with SQL, data manipulation and processing with Python, and data visualization with Tableau, among others. 

Course Requisite 

A valid GMAT / CAT / GRE / Jigsaw Administered Test score.

Course Content

  • Data Handling and Extraction with SQL and Python
  • Data Visualization with Tableau and Python
  • Statistical Data Analysis with R, Excel and Python
  • Feature Engineering with Python for Structured and Unstructured Data Types
  • Predictive Statistical Modeling Algorithms
  • Machine Learning and Deep Learning Models on Text and Images
  • Implementing Algorithms at Scale with Big Data Systems
  • Generating Business Values and Effective Storytelling with Tableau

 

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