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22 Best Free Online Data Analytics Courses

Our Favorite Free Online Data Analytics Course

Exploratory Data Analysis

Exploratory data analysis

This course is in English and has subtitles in 10 different languages.  this course is part of multiple programs and can be applied into multiple specializations. You will cover the exploratory techniques for summarizing data and they are usually applied before modelling begins. It can assist with the development of more complexed statistical models, and are also important for discarding or honing probable hypothesis regarding the world that is associated with the data. The plotting systems in R and some basic principles used in building data graphics together with some of the common multivariate statistical techniques used to visualize high-dimensional data. What you will learn is to understand analytic graphics and the base plotting system in R, and use advanced graphing systems like the Latter system. Lastly, you will learn to make graphical displays of very high dimensional data and apply cluster analysis techniques to locate patterns in data. Skills Acquired: Cluster Analysis, Ggplot2, R Programming, and Exploratory Data Analysis. Duration: Approx 55 hours over 4 weeks

Rating: 4.7/94%

What are the Top Free Online Data Analytics Courses

After much research on all online Data Analytics Courses on offer, we have found the top 22 Data Analytics courses to make it easier for you to choose

22 Advanced Data Structures

Advanced data 

The course is in English and is an intermediate course that continues in the use of C++ skills and the use of more advanced command-line programs that use file processing, linked lists, stacks, queues, trees, binary search trees and tree balancing, algorithms to solve problems. You will cover many implements in the development of each data structure including hash maps, AVL, and red and black trees. You will experience actual practise writing in C++. The focus of this course is on the efficiency of different data structures to solve many types of problems. Data structure is a collection of data values their relationships and the function or operation that can be applied thereto.. Duration: Approx 7 - 9 hours per week for 9 weeks

21 Data Wrangling

Data wrangling

The course is in English. You will discover how to wrangle data from various sources and best apply it to data driven applications. You will learn how to gather and filter data from various data formats and how to assess the quality of your data and investigate the best practices for data cleaning.  You will be introduced to Mongo DB. Covering data storage and the Mongo DB query language together with exploring analysis using Mongo DB aggregation framework. This course if very good for data analysts wanting to add big data to their capabilities. Duration: Approx 2 months which includes 6 lessons at your own pace. 

20 Data Literacy Foundations

Data literacy foundations

This course is in English and will teach you critical thinking skills as an essential tool to data literacy in today’s data driven environment. You will learn how you utilize data on a daily basis. Considering the value of the data and studying the transformation of data from analogue to digital. The ethics of data usage to be considered using case studies and the role of critical thinking in data analysis, which is essential for strategic planning and revealing corporate competitive advantage. Finally, you will explore data, analytic tools, and methodologies. Duration: 6 - 8 hours per week over 4 weeks but self paced

19 Data Analysis Decision Making

Data Analysis decision making

The course is in English. In this course you will learn how to apply critical thinking when working with data. You will learn to organize and clean data creating a data analysis plan utilizing Microsoft Excel while using Tableau to create tables and graphically representations to meaningfully organize and present the data. You will also learn to identify critical data analysis and integrate these with other information. And finally you will assess your representations for accuracy. Duration: Approx 6-8 hours per week for 3 months and self paced but to get the full experience you will need to pay for the course 

18 Business and Data Analysis Skills

Business and data analysis skills

The course is in English. In today’s day and age it is critical to know how to analyse, distill and generate stories using data. The course will show you how to utilize Microsoft Excel as a powerful analytical and presentation tool. In the course you will analyse real world market and financial analyses and present your findings for impact. Finally, you will learn how to make data-based decisions resulting from your findings thus helping your organization to grow. You will learn how to choose the most appropriate excel functions for the tasks, calculating and assessing market size and share , how to critically read the data and communicating of different types of revenue, cost, and key profitability criteria. Duration: Approx 3 - 4 hours per week over 3 weeks and is self paced. Rating: 21 350 enrolled

17 Data Analysis and Statistical Modelling and Computation in Applications

Data analysis and statistical modelling

The course is in English. Multi disciplinary skills are required in data analysis like mathematics, statistics, virtualization, problem solving, machine learning and communication skills. You will combine these essential skills together with domain knowledge to ask and answer questions utilization data. The course reviews the following tools like Hypothesis testing, gradient descent, and regression methods. Thereafter you will learn models and methods to analyse specific data in 4 domain areas: Epigenetic Codes and Data Visualization; Criminal Networks, Network analysis; Prices, Economics and Time Series; and, Environmental Data and Spatial Statistics. You will be guided to analyse data from each of these focus areas and to present your findings. Prerequisites: undergrad Python Programming, undergrad Calculus and Linear Algebra, undergrad probability theory and stats and undergrad Machine learning.Duration: Approx 10-15 hours per week for 16 weeks and is in instructed led. Rating: 23 000+ enrolled

16 Analytics for Decision Making

Analytics for decision making

This course is in English. The course is about avoiding bad decision with data, good decisions will give you distinctive edge in business. This course is based on statistics and data analysis and will help you with the concepts of sound statistical thinking that can be applied in many fields. Pillars like understanding variations, pinpointing risks related to alternative decisions, and finding the sources of variations. The aforementioned ideas led to the development of quantitative models which usually get lost. This course focuses on these ideas. You will see and answer questions like: The relevance of traditional statistical methods in modern analytics?, When approaching quantitative problems, how to avoid fallacies and misconceptions. In predicative applications, how we apply statistical methods and how to get an understanding of customer engagement through analytics. It is recommended for student with a bachelors degree or someone deciding to study in a business field. Duration: 4 - 6 hours per week for 4 weeks, self paced. Rating: 36 694 enrolled

15 Introduction to Analytics Modelling

Introduction to Analytics Modelling

The course is an introductory level in English. You will learn the essential analytics models and methods to appropriately apply them using tools like R do retrieve specific insights. To best understand your data, to generate predictions, and to make sound business decisions, you will need to establish analytical models. Without these models it would be very difficult to gain proper insight from your data. In modelling the choice of data sets, algorithms, techniques, and formats to solve problems, is essential. Here you will gain an understanding of fundamental models and how to analyse and get practice on how to implement them using tools like R. You will be taught how to choose the correct approach for the wide array of tools at your disposal. Using statistical models, machine learning, and models you will create models for classification, clustering, change detection, data smoothing, validation prediction, optimization and experimentation. Prerequisite: Probability and stats, Basic programming, Linear algebra, and basic calculus. Duration: Approx 8 - 10 hours per week for 16 weeks.Rating: 74 465 enrolled

14 Data Analytics Basics for Everyone

Data analytics basic

This course is in English and you will learn the basics of data analytics and acquire knowledge about data eco systems, the process, and life cycle of data analytics. You will learn about data structures, file format, sources of data and data repositories. You will learn about Big Data and uses and features of some Big Data processing tools. You will find out the daily tasks of a data analyst as to how they identify, gather, wrangle, mine and analyse data as well as communicating their findings to stake holders. You will be shown some of the tools used for these tasks. The course covers the use of relational and non-relational data bases, data warehouses, data marts, and data lakes. The course features a section on how ETL (Extract-Transform-Load) is used to convert raw data into data ready for analysis. Finally, you will be introduced to the specific languages used by data analyst to extract, prepare, and analysis data. Duration: Approx 2 - 3 hours per week for 5 weeks at your own speed

Rating: 98 466 enrolled

13 Analyzing and Visualizing Data

Analyzing and Visualizing

The course is in English. If you are an entry level data professional, student, researcher or academic, a marketing analyst, a business and data analyst, or a financial analyst then this is the course for you. Power BI is a business analytics and visualization tool made available by Microsoft and helps data professionals give great presentations regarding their data. The course is beginners guide to working with data and suits professionals alike. Lastly, you will become effective in working with data, creating data visualization, and preparing dashboards and reports. What you will learn is how to identify and work with business oriented data, to import and prepare data to load into a model, identify the types of data visualizations and their purposes and to create and share Power BI reports and dashboards. Duration: Approx 10 - 12 hours per week for 4 weeks at your own pace. Rating: 100 556+ enrolled

12 Probability and Statistics in Data Science

Probability and Statistics

This course is in English and is an advanced course.  Inherent in the analysis of broad data is reasoning about uncertainty. Probability and statistics are the foundation for this reasoning. In this course you will learn about probability and statistics and mathematical theory as well as getting experience of applying this theory to actual data. You w ill cover information are regarding random variables, dependence, correlation, regression, PCA, entropy and MDL. Duration: Approx 10 12 hours per week for 10 weeks at your own pace. Rating: 125 243+ enrolled

11  Introduction to Probability

Introduction to probability

The course is in English, it is an intermediate course. This course will provide you with the necessary tools to understand data, science, philosophy, engineering, economics and finance. You will learn how to solve challenging technical problems and how to apply those solutions in everyday life. Examples you will work with ranging from medical testing to sports predictions will provide a good foundation for the study of statistical inference, stochastic processors, randomized algorithms and other subjects where probability is required. You will learn how to make good predictions, the story approach to understanding random variables, common probability distributions used in data science, and how to use conditional probability to approach complicated problems. Duration: Approx 5 - 10 hours per week for 10 weeks at your own pace. Rating: 142 304+ enrolled

10 Inferential Statistics

Inferential statistics

This course is in English and has subtitles in 8 different languages. Inferential Statistics make inferences based upon relationships found within a data sample to relations in the population. They help us to decide for instance whether differences between groups observed in data can be carried through to hypothesize whether the differences exist in general within the entire population. The course begins by teaching about significance testing which includes the sampling and test statistic distribution, p-value, significance, power, and type I and type II errors. You will then cover a number statistical tests and techniques to aid us making inferences for different data types and different research designs. You will also learn to perform tests to consider how each individual statistical test works, for what data, and if it is appropriate and how results should be interpreted there from. These tests can be performed using freely available software.  Skills Acquired: Statistics, Statistical Inference, Regression Analysis, and Analysis of Variance (ANOVA). Duration: Approx 23 hours over 7 weeks. Rating: 4.3/94%

09 Essential Design Principals for Tableau

Essential design principals

The course is in English and has subtitles in 8 different languages. This course will help you to analyze and apply design principles to your tableau visualizations. It is understood that you have some tableau experience with regards to the tools and that you have some knowledge of he concepts of data visualization. The differences and similarities of exploratory and explanatory analysis will be defined and you will be shown how to ask the right questions of what is required in a visualization. You will see how the design and data work together including choosing the correct representation for your data and you will look at what separates an effective visual to an ineffective visual. You will learn to apply visualization best practices and how to create and design visualizations that will impact your target audience. Skills Acquired: Data Analysis, Tableau Software, Data Virtualization, and Data Visualization (DataViz). Duration: Approx 13 hours over 4 weeks. Rating: 4.4/88%

08 Getting and Cleaning Data

Getting and cleaning data

This course is in English and has subtitles in 10 different languages. You will learn the basic ways to obtain data. You will be shown how to get data from the web, APIs, Data bases, and from colleagues in various formats. You will learn how to clean data and to tidy data which will greatly speed downstream data analysis tasks. Finally, you will look at the components of a complete set of data including raw data, processing instruction, code books, and processed data together with the essentials required for the collecting, cleaning, and sharing of data. You will also use R for text and date manipulation. Skills Acquired: Data Manipulation, Regular Expression (REGEX), R Programming, and Data Cleansing. Duration: Approx 20 hours over 4 weeks. Rating: 4.5/90%

07 R Programming

R Programming

This course is in English. It has subtitles in 12 different languages. You will learn programming in R and how to use R for data analysis. You will be taught how to install and configure software to a statistical programming environment and describe programming language concepts as they are implemented in high level statistical language. Statistical computing issues which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting in R code is also covered. You will also learn how to make use of R loop functions and debugging tools and how to collect detailed information using R profile. Skills Acquired: Data Analysis, Debugging, R Programming, and Rstudio. Duration: Approx 57 hours over 4 weeks.Rating: 4.5/94%

06 Data Science Math Skills

Data Science Math Skills

The course is in English and has subtitles in 9 different languages. This course was designed for those learners who have a basic math understanding but who did not take algebra or pre-calculus but require the maths needed to be successful in data science. It introduces the kernel of math at is central to data science keeping it readily understandable, and introducing unfamiliar ideas and maths symbols slowly and carefully. Topics include set theory and Venn diagrams, interval notations, uses for summations and sigma notation, graphing and describing functions. Exponents, logarithms and probability including Bayes’ theorem. This course is a prerequisite for those interested in the course “Mastering Data Analysis in Excel”. Mastering this skill will fully prepare you for success with the more advanced maths in that course. Skills Acquired: Bayes’ Theorem, Bayesian Probability, Probability, and Probability Theory. Duration: Approx 13 hours over 4 weeks. Rating: 4.5/96%

05 Creating Dashboards and Storytelling with Tableau

Creating dashboards

The course is in English and has subtitles in 8 different languages. This course follows on from Visual Analytics with Tableau and uses the visualizations you created for this course to create dashboards which help you to recognize the story within your data. And you will learn to use Story points to create your story. You will be able to structure and organize your story to its full potential to both stakeholders and end users. You will learnt o assemble a dashboard, analyze concepts and techniques for compelling story telling with data. Skills Acquired: Storyboarding, Tableau Software, Data Virtualization, and Data Visualization.Duration: Approx 15 hours over 4 weeks. Rating: 4.6/88%

04  Databases and SQL for Data Science with Python

Databases and SQL

The course is in English and has subtitles in 2 other languages. A working knowledge of data bases and SQL (or structured query language) is necessary to become a good data analyst and data scientist. The course introduces relational data base concepts and will assist in the application of the SQL language in a data science environment. The emphasis is on practical learning and is such you will be working with real data bases and data science tools. You will create a data base instance in the cloud and begin practicing building and running SQL queries. Finally you will learn how to access data bases from Jupyter notebooks using SQL and Python. No previous knowledge of data bases, SQL, Python or programming is required. Skills Acquired: Cloud Databases, Python Programming, Ipython, Relational Database Management Systems (RDBMS) and SQL. Duration: Approx 37 hours over 6 weeks

Rating: 4.6/93%

03 Managing Data Analysis

Managing Data analysis

This course is in English and has subtitles in 9 different languages. You will learn the process of analyzing data together with the management of this process. The iterative nature of data analysis is described together with the role of asking a powerful question, exploratory data analysis, inference, formal statistical modelling, interpretation, and communication. You will also learn how to process the data to yield understandable and usable results. BY the end of the course you will have learnt to differentiate between different data pools, to describe the data analysis iteration, explore the data sets for appropriateness, and use statistical findings to create compelling data analysis presentations. Skills Acquired: Data Analysis, Communication, Interpretation, Exploratory Data Analysis. Duration: Approx 9 hours 1 week to complete. Rating: 4.6/95%

02 Linear Regression and Modelling

Linear regression and modelling

The course is in English and has subtitles in 9 different languages. This course takes you through simple and multiple linear regression models which allow you to access relationships between variables within a data set and continuous response variable. It determines relationships between seemingly disconnected variables and you will learn the fundamental theory behind linear regression. You will finally learn to fit, examine, and utilize regression models to determine relationships between many variables using the free statistical software R and RStudio. Skills Acquired: Statistics, Linear Regression, R Programming, and Regression Analysis. Duration: Approx 10 hours over 4 weeks

Rating: 4.7/94%

01 Exploratory Data Analysis

Exploratory data analysis

This course is in English and has subtitles in 10 different languages. This course is part of multiple programs and can be applied into multiple specializations. You will cover the exploratory techniques for summarizing data and they are usually applied before modelling begins. It can assist with the development of more complexed statistical models, and are also important for discarding or honing probable hypothesis regarding the world that is associated with the data. The plotting systems in R and some basic principles used in building data graphics together with some of the common multivariate statistical techniques used to visualize high-dimensional data. What you will learn is to understand analytic graphics and the base plotting system in R, and use advanced graphing systems like the Latter system. Lastly, you will then learn to make graphical displays of very high dimensional data and apply cluster analysis techniques to locate patterns in data. Skills Acquired: Cluster Analysis, Ggplot2, R Programming, and Exploratory Data Analysis. Duration: Approx 55 hours over 4 weeksRating: 4.7/94%

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