This is a beginner level in English with subtitles in 9 different languages. Insights and trends in data has been an art form that is existed for eons. Ancient Egyptians utilized data from population census to improve tax collection and the prediction of the annual flooding of the river Nile. Of late people in the career of data science have created a unique and distinct field of work. In this course you will meet data science practitioners and glean an overview about the science as it is today. This course will teach you about the importance of data science in today’s data-driven world. You will also learn the various paths which emanate in a career in this field and you will be able to explain why data science is the most sort after job in this century. The syllabus covers: in Week 1 - The Definition of Data Science and What it is that Data Scientist Do; Week 2 - Data Science Topics; Week 3 - Data Science in Business. The medium of instruction is through video and readings with quizzes to consolidate your knowledge. Skills Acquired: Data Science, Deep Learning, Machine Learning, Big Data, and Data Mining. Duration: Approx 9 hours over 3 weeks. Rating: 4.7/97%

What are the Top Free Online Data Science Courses

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

22 SQL for Data Science Capstone Project

SQL for data science capstone project

This course is an intermediate level in English with subtitles in 8 different languages. Data science is still in its early phases as a dynamic and growing career and requires skills based in SQL. This course will give you a powerful understanding and skills for applying SQL to analyse data and solve business problems. This project will help you to apply knowledge and skill you have acquired in leaning SQL. A data set will be provided from which you will develop a project proposal which will are acquire you to explore your data and perform some statistics calculations. You will discover analytics for qualitative data and evaluate new metrics to understand the patterns that are becoming apparent in your analysis. Finally, you will present your work and tell the story of your findings. The syllabus covers: Week 1 - Getting Started and Milestone I: Project Proposal and Data Selection/Preparation; Week 2 - Milestone 2: Descriptive Stats and Understanding your Data; Week 3 - Milestone 3: Beyond Descriptive Stats (Dive Deeper/Go Broader) and Week 4 - Milestone 4: Presenting Your Findings (Storytelling). The medium of instruction is by videos and readings and a few quizzes to consolidate.

Skills Acquired: Presentation Skills, Data Analysis, SQL, Creating Metrics, and Exploratory Data Analysis. Duration: Approx 35 hours over 4 weeks. Rating: 4.3

21 Genomic Data Science Specialization

Genomic data science specialization

The course is an intermediate level in English with subtitles in 9 different languages. Genomics inspired a revolution in medical science and it is vital to understand the Genome and to be able to utilize and understand the data and information from the Genomic data sets. Genomic Data Science applies statistics and data science to the Genome information. This specialization explores the tools and concepts you will require to understand, analyze and interpret data from advancing sequencing experiments. You will learn about the tools used in Genomic data science utilizing the command line, together with tools like Python, R, and Bioconductor. This specialization can serve as a standalone introduction or it can compliment a degree or postgrad degree in biology, genetics or molecular biology to gain familiarity in data science and statistics to foster your interaction with data I everyday work. As it is a specialization you may choose the course you would like to start with. Course1 - Introduction to Genomic Technologies; Course 2 - Python for Genomic Data Science; Course 3 - Algorithms for DNA Sequencing; Course 4 - Command Line tools for Genomic Data Science; Course 5 - Bioconductor for Genomic Data Science and Course 6 - Statistics for Genomic Data Science.

Skills Acquired: Bioinformatics, Statistics, Data Science, Computational Biology, Biopython, Python Programming, Genomics, Bioinformatics Algorithms. Algorithms, Algorithms on Strings, Samtools, and Unix. Duration: Approx 6 months at a pace of 2 hours per week - 6 courses. Rating: 4.3

20 Fundamentals of Scalable Data Science

Fundamentals of scalable data science

This course is a beginner level in English and has subtitles in 8 different languages. Apache Spark is the standard for large scale data processing. It is crucial to learn a scalable data science platform as memory and CPU restrictions limit ability when it comes to building advanced machine learning models. This course will lead you through the basics of Apache Spark using Python and Pyspark and you will learn its application in computing exploratory and data pre-processing tasks. You will be exposed to fundamental statistical measures and data visualization technologies. On completing the course you will understand how basic statistical measures reveal patterns in data, recognize data characteristics, patterns, trends, deviations, and outliers and use techniques for working with big data like dimensional reduction and feature selection methods. It is recommended that you have a basic knowledge in Python, mathematics, and SQL. The following technologies will be used but no previous knowledge is necessary - Jupyter notebooks, Apache Spark, and Python. The syllabus covers: Week 1 - Introduction to the Course and grading environment; Week 2 - Tools that support Big Data Solutions; Week 3 - Scaling Math for Statistics on Apache Spark and Week 4 - Data Visualization of Big Data.

Skills Acquired: Statistics, Data Science, Internet of Things (IOT), and Apache Spark. Duration: Approx 22 hours over 4 weeks. Rating: 4.3/87%

19 Data Science for Business Innovation

Data science for business innovation

This is a beginner level course in English with subtitles in 4 other languages. This course provides the basis of must-have expertise in data science to enable executives and middle management to garner data driven innovation. This course will teach you the value Data Science creates, the main types of problems that it can solve, the difference between descriptive, predictive and prescriptive analytics, and the role played by machine learning and artificial intelligence. From a technical angle the course looks at supervised, unsupervised, and semi-supervised methods and explains what can result from classification, clustering, and regression techniques. The course also covers the role of NoSQL data models and the impact of cloud based computation platforms. The syllabus covers: Week 1 - Introduction to Data-Driven Business; Week 2 - Terminology and Foundational Concepts; Week 3 - Data Science Methods for Business; and finally Week 4 - Challenges and Conclusions. Skills Acquired: Data Science, Business Analytics, Decision-Making, Data Analysis, and Big Data. Duration: Approx 7 hours over 4 weeks. Rating: 4.3/89%

18 Executive Data Science Specialization

Executive data science specialization

The course is a beginner level in English with subtitles in 14 different languages. These 4 courses which comprise the specialization will teach you everything you require to know to assemble and lead a data science business even though you may have never worked in a data science environment before. The course provides an accelerated course in data science to create familiarity with data science and to help you understand your role as a leader. Further you will be taught recruitment, assembling a team, evaluating the team and then developing the team with complimentary skill sets and roles. The structure of the data science pipeline, the goals at each stage, and keeping your team on target is also taught, and finally you will learn some practical skills to overcome the common challenges which frequently derail data science projects. The 4 courses cover: Course 1 - A Crash Course in Data Science; Course 2 - Building a Data Science Team; Course 3 - Managing Data Analysis; Course 4 - Data Science in Real life and finally Course 5 - Executive Data Science Capstone.

Skills Acquired: Data Science, Data Management, Data Analysis, Communication, Leadership, Machine Learning, Project, Team Building, Management, Team Management, Interpretation, and Explanatory Data Analysis. Duration: Approx 2 months at a pace of 6 hours per week - 5 Courses

Rating :4.5

17 Applied Data Science with Python Specialization

Applied data science with Python specialization

The course is an intermediate level in English with subtitles in 10 different languages. This specialization covers 5 courses and introduces you to data science through Python programming language. This specialization is for learners with basic Python for programming skills and who want to apply statistical, machine learning, information visualization, test analysis, and social network analysis techniques through Python toolkits like pandas, matplotlib, scikit learn, nltk, and networkx to gain insight into data. Course 1, 2 and 3 need to be taken in order and thereafter courses 4 and 5 can be taken in any order. You will learn to conduct inferential statistical analysis, determine whether a data visualization is good or bad, enhance a data analysis with applied machine learning and how to analyze the connectivity of a social network. The 5 courses include: Course 1 - Introduction to Data Science in Python; Course 2 - Applied Plotting, Charting, and Data Representation into Python; Course 3 - Applied Machine Learning in Python; Course 4 - Applied Test Mining in Python and Course 5 - Applied Social Network Analysis in Python. Skills Acquired: Text Mining, Python Programming, Pandas, Matplotlib, Numpy, Data Cleansing, Data Virtualization, Data Visualization (DataViz), Machine Learning (ML) Algorithms, Machine Learning, Scikit Learn, and Natural Language Toolkit. Duration: Approx 5 months at a pace of 7 hours per week - 5 Courses. Rating: 4.5

16 Tools for Data Science

Tools for data science

This is a beginner level course in English with subtitles in 9 different languages. This course discusses some of the most popular data science tools, their features, and how to use them. You will learn about Jupyter notebooks, JupyterLab, Rstudio IDE, Git, GitHub, and Watson Studio. Each tool is discussed along with its use, the programming languages they can execute, and their features and limitations. The tools are hosted in the cloud on Skills Network Labs, where you will be able to access each tool and follow instructions to run simple code in Python, R, or Scala. To finalize the course you will create a project with a Jupyter notebook on IBM Watson Studio and show your ability in preparing a notebook, writing Markdown and sharing your work. Some of the things you will learn include the creation and management of source code for data science in GitHub and how IBM data science tools can be used by data scientists, aside from what is explained above. The syllabus cover: Week 1 - Data Scientist’s Toolkit; Week 2 - Open Source tools; Week 3 - IBM Tools for Data Science; and finally in Week 4 - Final Assignment: Create and Share your Jupyter Notebook. Skills Acquired: Data Science, Github, Python Programming, Jupyter Notebooks, and Rstudio. Duration: Approx 20 hours over 4 weeks. Rating: 4.5/84%

15 Data Science Specialization

Data science specialization

This is a beginner level course in English with subtitles in 12 different languages. The goal of this specialization is to cover the tools and concepts you will require through the entire data science pipeline, from asking the correct questions to making inferences, and publishing results. The final Capstone project will see you applying the skills learnt by building a data product using real data after which you will have a portfolio which demonstrates your ability in using this material. You will learn to use R to clean, analyze, and visualize data, navigate the data science pipeline from the acquisition of data to the publication stage, the use of Github to manage your projects and also how to perform regression analysis, least squares, and inference using regression models. The are 10 courses in this specialization: Course 1 - The Data Scientist toolbox; Course 2 - R Programming; Course 3 - Getting and Cleaning Data; Course 4 - Exploratory Data Analysis; Course 5 - Reproducible Research; Course 6 - Statistical Inference; Course 7 - Regression Models; Course 8 - Practical Machine Learning; Course 9 - Developing Data Products and Course 10 - Data Science Capstone. Skills Acquired: Github, Machine Learning, R Programming, Regression Analysis, Data Science, Rstudio, Data Analysis, Debugging, Data Manipulation, Regular Expression (REGEX), Data Cleansing, and Cluster Analysis. Duration: Approx 11 months at a pace of 7 hours per week - 10 Courses. Rating: 4.5

14 Python Project for Data Science

Python project for data science

This is an intermediate level course in English only. This is a mini course for you to demonstrate your basic Python skills for working with data. The completion of this course will have you working on a project to develop a simple dashboard using Python. The pre-requisite is that you complete the Python for data science, AI and development course from IBM. You will lean to apply Python basics and data structures and how to work with data in Python. You will explore the role of a data scientist working on an authentic Python project and then build a dashboard from Jupyter notebook using Python and some Python libraries. The syllabus covers: Week 1 - Crowdsourcing Short squeeze Dashboard. The medium of instruction is through 2 videos, 8 readings and quizzes to consolidate your learnings. Skills Acquired: Data Science, Python Programming, Ipython, Data Analysis, and Pandas. Duration: Approx 8 hours - 1 week. Rating: 4.5/90%

13 Introduction to Data Science in Python

Intro to data science in python

This is an intermediate course in English and has subtitles in 10 other languages. You will be introduced to the basics of Python programming including techniques such as lambdas, reading and manipulating CSV files, and the Numpy library. You will learn data manipulation and techniques using Pandas data science library and be introduced to the Series and DataFrame as data structures for data analysis. There are also tutorials on using functions like groupby, merge, and pivot tables. Finally you will manipulate and clean tabular data and run basic inferential statistical analyse. The syllabus cover: Week 1 - Fundamentals of Data Manipulation with Python; Week 2 - Basic Data Processing with Pandas; Week 3 - More Data Processing with Pandas; and Week 4 - Answering Questions with Messy Data. The medium of instruction is through videos and readings with quizzes to asses your understanding. Skills Acquired: Python Programming, Numpy, Pandas, and Data Cleansing. Duration: Approx 31 hours over 4 weeks. Rating: 4.5/92%

12 A Crash Course in Data Science

A crash course in data science

This is a beginner level in English with subtitles in 12 different languages. This 1 week course delivers a compact tutorial on data science and big data terms, what they mean, and how they participate in successful organizations. This class is suited for those wishing to learn about the data science action and what it is about and also for those who will eventually manage a team of data scientists. The intention behind the course is to rapidly get you on the go in data science without any padding. This is really a course with distilled essentials. After completing this course you will understand the role data science plays in different contexts, the role played by statistics, machine learning and software engineering in data science, the description of the structure of a data science project and know the terms and tools used by data scientists. And finally you will come to understand the role of the data science manager. Skills Acquired: Data Science, Data Analysis Machine Learning, and Project. Duration: Approx 7 hours - 1 week. Rating: 4.5/93%

11 Data Science Math Skills

Data science math skills

The course is a beginner level in English with subtitles in 9 different languages. Anyone taking a data science course will know that math is included. This course will teach the math you will require to be successful in any data science math course and has been generated for those who have basic math ability but have not taken algebra or pre-calculus. This course will introduce the math you require for data science without added complexity, or introducing unfamiliar ideas. You will learn the vocabulary, notation, concepts, and algebraic rules all data scientists will require before advancing in future data science studies. The syllabus covers: Week 1 - Building Blocks for Problem Solving; Week 2 - Functions and Graphs; Week 3 - Measuring Rates of Change; and finally Week 4 - An Introduction to Probability theory. The medium of instruction is through videos and readings. Skills Acquired: Bayes’ Theorem, Bayesian Probability, Probability, and Probability Theory. Duration: Approx 13 hours over 4 weeks. Rating: 4.5/96%

10 Data Science: Statistics and Machine Learning Specialization

Data science: statistics and machine learning specialization

This is an intermediate level course in English with subtitles in 9 different languages. This specialization of 5 courses continues on and develops the material learnt from data science: Foundations using R specialization. This course covers statistical inference, regression models, machine learning, and the development of data products. You will also learn to build and apply prediction functions, you will learn to develop public data products and understand how to draw conclusions about populations or scientific truths from data.The Capstone project will grant you the opportunity to apply your newly learnt skills by building a data product using actual data. These course cover: Course 1 - Statistical Inference; Course 2 - regression Models; Course 3 - Practical Machine Learning; Course 4 - Developing Data Products and finally Course 5 - Data Science Capstone.

Skills Acquired: Machine Learning, Github, R Programming, Regression Analysis, Data Visualization (DataViz), Statistics, Statistical Inference, Statistical Hypothesis Testing, Model Selection, Generalized Linear Model, Linear Regression, and Random Forest. Duration: Approx 6 months at a pace of 6 hours per week - 5 Courses. Rating: 4.6

09 IBM Data Science Professional Certificate

IBM data science professional certificate

This is a beginner level course in English with subtitles in 11 different languages. Data Science continues to be one of the most sort after professions in this decade and the demand for scientists with the ability to analyze data and communicate results to inform data driven decisions is exploding. NO prior knowledge of computer science is or programming language is required but you will develop the skills and tools to have a competitive edge if you are seeking and entry level position as a data scientist. This program of 10 courses including the Capstone, will let you experience in the latest tools and skills, including open source tools and libraries, Python, data bases, SQL, data visualization, data analysis, statistical analysis, predictive modelling and machine learning algorithms. In addition to earning a professional certificate from Coursera you will also receive IBM’s digital badge. You will learn what data science entails and the activities conducted by a data scientist and how to think and work like a data scientist. You will also develop skills using tools, languages, and professional data scientist libraries. Further you will learn to import and clean data sets, analyze and visualize data and build and evaluate machine learning models and pipelines using Python. The courses cover: What is Data Science; Data Science tools; Data Science Methodology; Python for Data Science, AI, and Development; Python Project for Data Science; Data Bases and SQL for Data Science with Python; Data Analysis with Python; Data Visualization with Python; Machine Learning with Python and Applied Data Science Capstone. Skills Acquired: Data Science, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Python Programming, Jupyter Notebooks, Rstudio, Methodology, Data Analysis, and Pandas. Duration: Approx 11 months at a pace of 4 hours per week - 10 Courses. Rating : 4.6

08 SQL for Data Science

SQL for data science

This is a beginner level course in English with subtitles in 8 different languages. The need for people who have been skilled in using and interacting with data, and being able to think critically to provide insights and so optimize decision making for business has become of paramount importance. The skills required to be an excellent data scientist include being able to obtain and work with data, and so to be well versed in SQL which is the language for communicating with data base systems. This course will teach you the fundamentals of SQL and working with data so you can start analyzing for data science purposes. You will learn to ask the correct questions and then establish good answers to deliver insights for your organization. The course begins with the basics under the assumption you have no knowledge or skill in SQL and then builds up on the basis to have you writing simple and complexed queries to assist you in selecting the right data from tables. The types of data you will become familiar with include strings and numbers and will discuss methods to filter and pare down your results. From here you will learn to create new tables into which data will be moved and also you will learn operators and how to combine the data. You will learn to interpret the structure, meaning, and relationships in source data and use SQL to shape your data for analytical purposes. The syllabus covers: Week 1 - Selecting and retrieving Data with SQL; Week 2 - Filtering, Sorting, and Calculating Data with SQL; Week 3 - Subqueries and joins in SQL and lastly in Week 4 - Modifying and Analyzing Data with SQL. Skills Acquired: Data Science, Data Analysis, Sqlite, and SQL. Duration: Approx 14 hours over 4 weeks. Rating: 4.6/91%

07 Data Science Methodology

Data science methodology

This is a beginner course in English with subtitles in 10 different languages. Even with increased computing ability and greater access to data, our ability to utilize the data to maximum benefit in decision making has become lost or diminished through not having a solid grasp of the questions being asked or how to apply adequately to the problem being experienced. This course shares a methodology to ensure that data utilized in problem solving is both relevant and properly manipulated to address the problem at hand. In this course you will learn the steps involved I handling a data science problem, the steps in practicing data science from forming a problem to collecting and analyzing data, to building a model and understanding the feedback after implementing the model. The syllabus covers: Week 1 - From Problem to Approach and from Requirements to Collection; Week 2 - From Understanding to Preparation and from Modelling to Evaluation; Week 3 - From Deployment to Feedback. Skills Acquired: Data Science, Data Mining, and Methodology. Duration: Approx 8 hours over 3 weeks. Rating: 4.6/93%

06 Python for Data Science, AI & Development

Python for data science, AI & development

This course is in English and has subtitles in 2 other languages. In this course you will learn Python for data science and programming in general with this introduction to Python. No previous programming experience is required and you will learn the fundamentals of Python programming including data structure and analysis through exercises throughout the modules and create a project demonstrating your skills. By the conclusion of this course you will be creating basic programs working with data and solving problems, further you will be gaining a strong foundation for more advanced learning in the field. You will work with data in Python using Pandas and Numpy libraries. The syllabus covers: Week 1 - Python Basics; Week 2 - Python Data Structures; Week 3 - Python Programming Fundamentals and Week 4 - Working with Data Python and Week 5 - IPI’s and Data Collection. Skills Acquired: Data Science, Python Programming, Data Analysis, Panda, and Numpy. Duration: Approx 21 hours over 5 weeks. Rating: 4.6/93%

05 Databases and SQL for Data Science with Python

Databases and SQL for data science with Python

This is a beginner level course in English with subtitles in 2 other languages. As most data is retained in data bases the language most commonly used for communicating with and extracting data from data bases is SQL (Structured Query Language). Ir you wish to become a data scientist it is necessary that you have a working knowledge of both data bases and SQL. This course introduce es you to relational data base concepts and will help you to learn and apply the SQL language. The emphasis in this course is on practical learning and so you will work with real data bases, data science tools, and real data sets. During the course you will create a data base in the cloud and through a series of labs you will build and run SQL queries and learn how to access data bases from Jupyter notebooks using SQL and Python. It is assumed that you have no prior knowledge or experience with data bases, SQL. Python, or programming. The syllabus covers: Week 1- Getting Started with SQL; Week 2 - Introduction to Relational Data Bases and Tables; Week 3 - Intermediate SQL; Week 4 - Accessing Data Bases using Python; Week 5 - The Course Assignment; and Week 6 is a bonus module: Advanced SQL for Data Engineering. Skills Acquired: Cloud Database, Python Programming, Ipython,and Relational Database Management Systems (RDBMS). Duration: Approx 37 hours over 6 weeks. Rating: 4.6/93%

04 Foundations of Data Science : K-Means Clustering in Python

Foundations of data science: K-means clustering in Python

The course is a beginner level in English with subtitles in 8 different languages. Organizations world over utilize data to predict behaviour and gain insights which help them to make informed decisions. Analyzing and managing data has become key in modern finance, retail, marketing, social science, research and development, medicine, and government. This course introduces you to data science and will prepare you for both intermediate and advanced data science courses by focusing on basic math, statistics, and programming necessary for data analysis tasks. You will use these concepts on a data clustering task which will teach basic programming skills necessary for data science techniques. As the course progresses you will do some mathematical and programming exercises and a data clustering project for a given data set. The syllabus covers: Week 1 - Foundations of Data Science: K-Means clustering in Python; Week 2 - Means and Deviations in Mathematics and Python; Week 3 - Moving from One to Two Dimensional Data; Week 4 - Introducing Pandas and using K-Means to analyze data and Week 5 - Data Clustering Project. Skills Acquired: K-Means Clustering, Machine Learning, and Programming in Python. Duration: Approx 29 hours over 5 weeks. Rating: 4.6/95%

03 Data Science Masterclass for Beginners

Data science masterclass

This course is in English. If you ever wondered how business utilize the vast data they collect and store then this course will introduce you to the skills and tools necessary to work with this extremely valuable resource. Topics covered include programming languages, data science, methodology, and collaboration. The module covered is data science basics. Introduction, programming languages, data science methodology, data science via Chatbot, libraries, API’s, data sets, and Github. On completion of this course you will be able to understand the 3 primary analyses in data mining to extract patterns: Know the difference between machine learning and deep learning; understand the factors to consider when choosing a programming language to learn; Be able to explain the steps of data science methodology, and the role of entities and intents in Chatbot development and finally understand the purpose and usage of GitHub in collaboration. Skills Acquired: Programming, Science, Data Science, Data Entry, and Programming Languages.Duration: Approx 3 - 4 hours. Rating: 5 star

02 Introduction to Data Science Specialization

Introduction to data science specialization

This is a beginner level course in English and has subtitles in 10 different languages. This 4 course specialization will give you the foundational skills any data scientist would need to prepare you in this exciting and in-demand career. You will learn what data science is and what data scientists actually do. You will uncover how widely applicable data science is and how the analyses of data can assist in making good data driven decisions. No prior knowledge is of computer science or programming languages is required as this specialization will provide you with the foundation you require for more advanced learning. Concepts like big data, statistical analysis, and relational data bases will be taught and you will gain understanding of the use of various open source tools and data science programs like Jupyter notebooks, Rstudio, GitHub, and SQL. Projects and labs will be provided to aid your learning of the methodology in tackling data science problems and you will apply your newly learnt skills and knowledge to real data sets. Course 1 - What is data Science?; Course 2 - Tools for Data Science; Course 3 - Data Science Methodology and Course 4 - Data Bases and SQL for Data Science with Python. Skills Acquired: Data Science, Relational Database Management System (RDBMS), Cloud Database, Python Programming, SQL, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Jupyter Notebooks, and Rstudio. Duration: Approx 4 months at a pace of 5 hours per week - 4 Courses. Rating: 4.7

01 What is Data Science?

What is data science

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