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The 12 Best Free Online Big Data and Data Science Courses to Enhance Your Skills

  • July 14, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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For professionals in a variety of industries, knowledge of big data and data science is now crucial in today's data-driven world. There are several online courses available to help you understand and master the principles of big data and data science, whether you're an experienced data analyst trying to broaden your skill set or a newbie interested in entering the profession. What's best? Anyone with an internet connection can access several of these courses for nothing at all. This article will examine the top 12 free online courses in big data and data science that cover a variety of subjects. You will get invaluable knowledge and useful skills from these courses that will help you succeed in your career and keep up with changing market trends. So let's explore the world of free online courses for big data and data science if you're prepared to start a learning adventure and advance your data science knowledge.

Best Free Online Big Data and Data Science Courses

Introduction to Big Data and Data Science

You will obtain a thorough understanding of the fundamental ideas behind big data and data science in this course. You will investigate the several elements of big data, such as volume, Velocity, diversity, and truthfulness. You will gain knowledge of the various phases of the data science life cycle, from data collection and cleaning through analysis and visualisation, through interesting lectures and real-world examples. You will have a firm understanding of the underlying ideas and procedures that underpin big data and data science at the end of this course.

Examining the relevance and applications of these fields: You will learn about the various uses and importance of big data and data science in the modern world in this course. You'll learn how these professions are altering sectors including marketing, finance, healthcare, and more. You will learn how businesses use big data and data science to make smart decisions, streamline operations, and gain a competitive edge through real-world case studies and examples. big data and data science will be highlighted in this course for their societal effects, as well as their contributions to solving current problems and influencing the future.

Data Visualisation and Analysis

You will master the fundamental methods and strategies used in data analysis in this course. Various statistical approaches for descriptive and inferential analysis, as well as methods for cleaning and preparing data, will be covered. You will get the knowledge and abilities necessary to draw insightful conclusions from data and make data-driven decisions through practical activities and real-world datasets.

Investigating tools and techniques for data visualisation: This course will expose you to the effectiveness of data visualisation in presenting complex information. You will gain knowledge of various visualisation methods and technologies, including interactive dashboards, graphs, and charts. You will gain practical expertise in producing visually appealing and educational visualisations that convey essential findings and trends in data through exercises and projects.

Machine Learning and Artificial Intelligence

You will gain a firm understanding of the essential ideas and guidelines guiding machine learning and artificial intelligence from this course. You will gain knowledge of several machine learning algorithms, including reinforcement learning, unsupervised learning, and supervised learning. The fundamentals of artificial intelligence, such as intelligent agents, expert systems, and natural language processing, will also be covered.

Examples and practical machine learning projects: You will have the chance to work on real-world projects and machine learning applications in this course. On real-world datasets, you will use a variety of methods and strategies to address issues like classification, regression, and clustering. You will receive hands-on experience using machine learning models, assessing their performance, and optimising them for the best outcomes through these projects.

Python for Data Science

The Python programming language, which is widely used in the field of data science, will be covered in this course. The fundamentals of Python grammar, data types, control structure, and functions will be covered. You will hone your coding abilities and grow comfort using Python for data manipulation and analysis through practical coding exercises and projects.

Python data analysis and manipulation libraries: You will examine the various Python libraries, including NumPy, pandas, and Matplotlib, that are frequently used for data manipulation and analysis in this course. You will discover effective methods for working with arrays and matrices, for transforming and manipulating data, for running statistical calculations, and for making visualisations. By utilizing these powerful libraries, you will be equipped with the tools to handle and analyze large datasets effectively.

SQL and Database Management

This course offers a thorough introduction to SQL, covering the language's fundamental ideas and syntax. The ability to retrieve, modify, and manage data contained in relational databases will be taught to you through the use of SQL queries. You will get hands-on experience using SQL to connect with databases and carry out activities like data retrieval, data manipulation, and database administration through real-world examples and exercises.

Concepts of database design and management: The ideas and best practises of database design and management are the main topics of this course. Database normalisation, entity-relationship modelling, and database schema design will all be covered. You will also study subjects including indexing, data integrity, and database security. You will be able to create scalable databases and administer database systems efficiently if you comprehend these ideas.

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Statistical Analysis and Probability

The basic statistical concepts and methods that are employed in data analysis are introduced in this course. You will study statistical inference, probability distributions, hypothesis testing, and descriptive statistics. You will learn how to analyse and evaluate data, make data-driven decisions, and draw insightful conclusions through exercises and real-world situations.

Data science uses of probability theory: The study of probability theory and data science applications is covered in this course. Probability distributions, conditional probability, and statistical models will all be covered. Additionally, you will comprehend how many data science applications, like machine learning, data mining, and predictive modelling, use probability theory. You will use probability theory to address issues with real-world data through practical exercises.

Data Science

Data Mining and Text Analytics

Natural language processing (NLP) and text mining are introduced in this course. Techniques for gleaning insightful information from textual data, including sentiment analysis, topic modelling, and text categorization, will be covered. You will gain practical experience using text mining and NLP approaches on actual datasets through hands-on projects.

Practical data mining and text analytics projects: You will engage in practical projects in this course that involve text analytics and data mining. When analysing massive datasets, you'll use a variety of data mining techniques, including clustering, classification, and association rule mining, to uncover interesting patterns and insights. You will also work on projects involving text analytics, where you will use NLP strategies to examine and extract data from text. These tasks will sharpen your practical abilities and give you invaluable data mining and text analytics expertise.

Data Engineering and ETL Processes

An overview of data engineering and the ETL process is given in this course. Techniques for data integration, extraction, transformation, and loading will be covered. You will learn how data engineering is essential for data integration and creating effective data pipelines through real-world examples and case studies.

Introduction to the building of data pipelines and data integration: You will learn how to create data pipelines and study the theories and methods of data integration in this course. You'll comprehend the significance of data transformation, data validation, and data quality in the data integration process. Using well-known tools and frameworks, you will design data pipelines through practical activities.

Hands-on tasks for creating data pipelines are provided in this course to help you remember what you've learned. Building end-to-end data pipelines requires applying your expertise of data integration, data transformation, and data loading to real-world applications. You will gain practical experience in creating and implementing effective data pipelines by finishing these assignments.

Ethical Considerations in Data Science

The ethical issues raised by data science are thoroughly covered in this course. You will study subjects like algorithmic fairness, data bias, and responsible data usage. You will comprehend the ethical issues data scientists face and discover solutions through case studies and debates.

Regulations for handling sensitive data and privacy issues: The legalities and privacy concerns related to handling sensitive data are the main topics of this course. The topics of personally identifiable information (PII), data anonymization, and data protection laws will all be covered. Knowing the privacy landscape will help you handle data ethically and guarantee that privacy laws are followed.

est practices for maintaining data privacy and security:The best practises for preserving data security and privacy throughout the data lifecycle are covered in this course. Data encryption, access controls, data masking, and data governance frameworks will all be covered. You will learn about securing data assets and guarding them against unauthorised access through real-world scenarios and case studies.

Data Science in Business

This course examines how data science is used in a variety of fields, including marketing, finance, healthcare, and retail. You will gain knowledge of how data science methods and models are applied to address problems unique to particular industries and generate profit. You will explore how data science is utilised in actual business contexts through case studies and examples.

Case studies demonstrating the effectiveness of data science in business: You will examine case studies in this course that demonstrate how data science has aided in corporate success. You will look at real-world examples of businesses using data science to improve customer experiences, streamline operations, make data-driven choices, and gain a competitive advantage. You can learn more about the influence of data science on business outcomes by studying these case studies.

Understanding data science's role in decision-making: The role of data science in decision-making processes is the main topic of this course. You will discover how predictive models and data-driven insights enhance organisational decision-making at various levels. You will learn how data science enables decision-makers to make educated and strategic decisions through real-world examples and debates.

Big Data Technologies and Tools

Popular big data technologies and tools, such Hadoop and Spark, are introduced in this course. You will discover more about their architecture, features, and applications for handling massive amounts of data. You will obtain a thorough understanding of these technologies and how they relate to big data analytics through lectures and practical examples.

Hands-on experience with big data processing frameworks: You will get first-hand exposure to big data processing frameworks in this course. In order to execute data ingestion, data transformation, and data analysis operations on huge datasets, you will work with tools like Apache Hadoop and Apache Spark. You will gain useful skills for utilising big data frameworks through guided activities and projects.

Exploring data storage and processing in distributed systems: The principles and methods of data processing and storage in distributed systems are the main topics of this course. You will study parallel processing, data replication, and distributed file systems like Hadoop Distributed File System (HDFS). You will comprehend how effective big data handling in distributed systems is done through real-world examples and conversations.

Capstone Projects and Real-world Applications

You will work on practical capstone projects as part of this course, allowing you to put the ideas and abilities you've learnt throughout the programme to use. Working with actual datasets, setting up data analysis pipelines, and gaining insightful conclusions are all aspects of these initiatives. You may demonstrate your mastery of big data and data science by finishing these tasks.

Demonstrating real-world applications of big data and data science: You will examine big data and data science's practical applications in this course across a range of sectors. You will discover how big data tools are utilised to resolve complicated issues, enhance decision-making, and stimulate creativity through case studies and examples. You can learn more about the practical effects of big data and data science in various fields by understanding these applications.

The presentation of the project's final results and lessons gained: In the program's final phase, you must present the results of your capstone projects and discuss the lessons you gained along the way. You will have the chance to present your work, share your findings, and consider the difficulties and new perspectives you have learned over the programme. This presentation will not only showcase your abilities but also give your classmates and professors important new information.

Conclusion

For professionals looking to develop their abilities and stay competitive in today's data-driven environment, the field of big data and data science provides a wealth of chances. It is now simpler than ever to get access to top-notch learning resources and pick up useful knowledge in this quickly expanding profession thanks to the availability of free online courses. You can build a strong foundation in important topics, tools, and methodologies by enrolling in one of these 12 top free online big data and data science courses.

These courses cover a wide range of topics and are appropriate for students with various levels of expertise, whether you are a beginner trying to learn the fundamentals of data science or an experienced professional looking to broaden your skill set. There is something for everyone, ranging from basic courses on big data technology and data analysis to advanced courses on machine learning and data visualisation.

You'll learn technical skills as well as insights into practical applications and industry best practises for data science by completing these courses. You may use your knowledge in real-world situations thanks to the practical exercises, projects, and examples provided in these courses, which will also help you develop a solid portfolio of projects.

Keeping up with the most recent trends and technology is essential in the dynamic world of big data and data science, so keep in mind that learning is a continual process. Utilise these no-cost online courses to increase your knowledge, sharpen your talents, and open up new job prospects in this fascinating field.

What are you still holding out for? Begin your education today and take the first step towards becoming a skilled big data and data science expert. You're about to enter a world of data-driven insights and opportunities!

Data Science Placement Success Story

Data Science Training Institutes in Other Locations

Agra, Ahmedabad, Amritsar, Anand, Anantapur, Bangalore, Bhopal, Bhubaneswar, Chengalpattu, Chennai, Cochin, Dehradun, Malaysia, Dombivli, Durgapur, Ernakulam, Erode, Gandhinagar, Ghaziabad, Gorakhpur, Gwalior, Hebbal, Hyderabad, Jabalpur, Jalandhar, Jammu, Jamshedpur, Jodhpur, Khammam, Kolhapur, Kothrud, Ludhiana, Madurai, Meerut, Mohali, Moradabad, Noida, Pimpri, Pondicherry, Pune, Rajkot, Ranchi, Rohtak, Roorkee, Rourkela, Shimla, Shimoga, Siliguri, Srinagar, Thane, Thiruvananthapuram, Tiruchchirappalli, Trichur, Udaipur, Yelahanka, Andhra Pradesh, Anna Nagar, Bhilai, Borivali, Calicut, Chandigarh, Chromepet, Coimbatore, Dilsukhnagar, ECIL, Faridabad, Greater Warangal, Guduvanchery, Guntur, Gurgaon, Guwahati, Hoodi, Indore, Jaipur, Kalaburagi, Kanpur, Kharadi, Kochi, Kolkata, Kompally, Lucknow, Mangalore, Mumbai, Mysore, Nagpur, Nashik, Navi Mumbai, Patna, Porur, Raipur, Salem, Surat, Thoraipakkam, Trichy, Uppal, Vadodara, Varanasi, Vijayawada, Visakhapatnam, Tirunelveli, Aurangabad

Data Analyst Courses in Other Locations

ECIL, Jaipur, Pune, Gurgaon, Salem, Surat, Agra, Ahmedabad, Amritsar, Anand, Anantapur, Andhra Pradesh, Anna Nagar, Aurangabad, Bhilai, Bhopal, Bhubaneswar, Borivali, Calicut, Cochin, Chengalpattu , Dehradun, Dombivli, Durgapur, Ernakulam, Erode, Gandhinagar, Ghaziabad, Gorakhpur, Guduvanchery, Gwalior, Hebbal, Hoodi , Indore, Jabalpur, Jaipur, Jalandhar, Jammu, Jamshedpur, Jodhpur, Kanpur, Khammam, Kochi, Kolhapur, Kolkata, Kothrud, Ludhiana, Madurai, Mangalore, Meerut, Mohali, Moradabad, Pimpri, Pondicherry, Porur, Rajkot, Ranchi, Rohtak, Roorkee, Rourkela, Shimla, Shimoga, Siliguri, Srinagar, Thoraipakkam , Tiruchirappalli, Tirunelveli, Trichur, Trichy, Udaipur, Vijayawada, Vizag, Warangal, Chennai, Coimbatore, Delhi, Dilsukhnagar, Hyderabad, Kalyan, Nagpur, Noida, Thane, Thiruvananthapuram, Uppal, Kompally, Bangalore, Chandigarh, Chromepet, Faridabad, Guntur, Guwahati, Kharadi, Lucknow, Mumbai, Mysore, Nashik, Navi Mumbai, Patna, Pune, Raipur, Vadodara, Varanasi, Yelahanka

 
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