In the current world, the discipline of Data Science and engineering is strategically located to lead organizations in their conversion of unstructured data into useful knowledge. It comprises techniques from various fields of study including statistics, computer science, and even the actual problem domain that is eventually applied in the process of instilling useful patterns and knowledge from an extensive collection of data. It is this analytics ability that is transforming fields from health care to banking, from retail to other fields.
Thus, data science can be characterized as the field involving the use of methods, approaches, algorithms, and systems for discovering valuable information from a large amount of structured and unstructured information. It extends a broad category of methods and encompasses data mining, machine learning, statistics, and visualization in a general sense to identify complex and concealed structures as well as patterns, and relations that are helpful in decision-making and creativity.
NPTEL data science for engineers initiated offers a course in partnership with IIT Madras. The objectives of this course are: To provide learners with a knowledge of the R programming language, review the essentials of math used in data science, work through the basic algorithms of data science, and, present a step-by-step approach to the practical problem in data analysis.
The expected learning outcomes of this data engineering course include how to employ R for data science solutions, how to assess results and generate sample applications to confirm methods. The course is open to any learner as a prerequisite, 10 hours of pre-course materials are required to take this course. Supporting companies in the industry are HONEYWELL, ABB, FORD, and GYAN DATA PVT. LTD. The course is open for anyone to enroll and gain knowledge and training from; however, to obtain a certification exam, one has to pay a fee, and this can only be taken physically at examination centers.
This is one of the top data science courses for engineers offered on Coursera. This course covers skills in data cleaning and visualization using R, the data science process from data acquisition to publication, and an introduction to GitHub for project management.
This specialization for data science for engineers also includes regression analysis and inference that uses regression models. The subject involves enhancing participants’ experiences in using GitHub, machine learning, R programming, and regression analysis; at the end of the program, participants are issued a career certificate. This is a completely online data engineering course.
The concepts of data science, statistics, and machine learning are introduced in this course. They get to know how to interpret big numbers, derive forecasts based on probability distribution and statistical sampling, and use algorithms of machine learning. The identified MicroMasters provide preparation for employment in positions like Data Scientist, Data Analyst, etc. with instructor-led courses along with particular assignments and exam timelines within over 1 year and 2 months.
As a part of the Data Scientist Nanodegree at Udacity, the students get acquainted with the concepts of data pipelines and experimental design alongside recommendation systems. A participant takes approximately 4 months to obtain the knowledge and skills in the scikit-learn machine learning library, statistical modeling, and relational database, and upon finishing the practical projects, the participants are issued with completion certificates.
Springboard’s Data Science Bootcamp equips learners for a career in data science by offering a 6-month part-time program. These models are supported with 1-on-1 mentorship, there are 28 mini-projects and 3 capstone projects that help learners become job-ready in machine learning and AI. It has employment assurance; the school promises to refund the tuition if they are unable to find the student's employment.
The understanding data science course by Datacamp is one of the recommended data science for engineers to take to learn about concepts in data science without coding. People learn about data scientist positions, the technique of A/B testing, and the analysis of time series and machine learning while being equipped with important skills; upon their course completion, they receive a statement of accomplishment.
IBM’s professional certificate in data science is aimed at equipping learners with real-world coding skills in Python, different functions in SQL, data analysis, visualization, and even machine learning. They apply what they learn within practical, end-of-unit projects, which provide verifiable results, as well as an industry-standard certificate from IBM upon the course’s end.
HarvardX currently provides the learners with a professional certificate in Data Science and comprises topics such as the R programming language, statistics, and machine learning. These include; Open-ended questions- for example, US Crime Rates; and Open-ended questions- for example, Election Forecasting; Participants develop practical skills in RStudio, Unix/Linux, and GitHub.
The Statistics and Data Science MicroMasters program by MIT
The MicroMasters program of MIT consists of four courses and an exam to ensure the credibility of the student. People master all the concepts of probability and statistics data analysis techniques and machine learning to make them ready for further studies or data scientist jobs.
Professional Data Engineer by Google
Google’s professional data engineer certification program focuses on making the candidate ready for collecting, transforming, and publishing data. It tests examinees on data processing systems design, data handling, ingestion and processing, analysis, and emerging technologies for data automation suitable for candidates with more than three years of experience in the industry.
The role of data science, therefore, can be said to be manifested in the fashion of how data has been realized as an important resource in today’s business environment. By leveraging advanced analytics and predictive modeling techniques, data scientists can help organizations:
a. Make Informed Decisions: This is by giving out information that is derived from facts rather than gut feeling.
b. Enhance Efficiency: By resource and process improvement as a result of analysis of various data concerning the Firm.
c. Predict Trends: Predict market and customers’ needs, opportunities, and threats.
d. Drive Innovation: Create new products and services about gathered information and needs of consumers.
e. Improve Customer Experience: Use and apply individualistic factors in people when providing social services and attending to or communicating with them.
Therefore, despite sounding like a discipline or method within the education system, data science for engineers has emerged as one of the most revolutionary tools for the advancement of industries and a promoter of change at the global level. Since business entities are in the process of creating and acquiring a giant data set and analyzing such data becomes highly essential, the position of the data scientist becomes highly valuable in providing solutions that can be instrumental in the provision of important business insights and strategies.
In other words, by embracing the opportunities of data, we create conditions for a new efficient, better, and more innovative world, which relies on the data approach.
Data Science involves extracting insights from data through various methods like statistics and machine learning. It's crucial for making informed decisions, enhancing efficiency, predicting trends, driving innovation, and improving customer experiences.
NPTEL's Data Science for Engineers course covers R programming, essential math for data science, basic algorithms, and practical data analysis. It's supported by industry partners like Honeywell and ABB.
Coursera's specialization includes data cleaning, visualization with R, GitHub for project management, and regression analysis. Participants earn a career certificate upon completion.
The edX course introduces data science, statistics, machine learning algorithms, and prepares learners for roles like Data Scientist or Data Analyst through instructor-led courses and assignments.
Udacity's program covers data pipelines, experimental design, recommendation systems, and skills in scikit-learn and statistical modeling, with completion certificates awarded after practical projects.
Springboard offers a 6-month part-time program with 1-on-1 mentorship, 28 mini-projects, and 3 capstone projects, preparing learners for careers in machine learning and AI with job placement support.
Datacamp's course provides an introduction to data science concepts without coding, covering A/B testing, time series analysis, and machine learning, culminating in a statement of accomplishment.
IBM's program includes coding in Python, SQL, data analysis, visualization, and machine learning, with practical projects and an industry-standard certificate upon completion.
HarvardX's program covers R programming, statistics, and machine learning, with practical skills development in RStudio, Unix/Linux, and GitHub.
MIT's program includes courses on probability, statistics, data analysis techniques, and machine learning, preparing learners for advanced studies or careers as data scientists.