edX data science course certification program is offered by Harvard University. This HarvardX data science certification program is designed to prepare participants with the essential skills and knowledge base to help them solve real-world problems. This edX data science course covers many concepts such as inference, regression, probability, and machine learning and helps participants learn the tools and methodologies of R programming, ggplot2, data wrangling, etc. This program includes many case studies which help students to understand the real-world applications of the knowledge. R software environment is used throughout the program.
edX is an educational platform that doesn’t need any introduction. It is one of the most popular online learning platforms which provides many courses on different subjects and specializations.
edX data science institute is an online learning platform from the digital education company 2U, inc. which is a leading online education company. It was founded by MIT and Harvard University as an experiment to make the world’s best education available to everyone.
edX has been delivering courses on almost every subject from many disciplines like data science, artificial intelligence, digital marketing, robotics, and public health.
edX courses on many disciplines have been taken by more than 86 million people all over the globe. All the courses offered by edX Data Science Institute are made in collaboration with well-renowned universities and organizations at the forefront of their fields.
Find Some More Courses,
- Psychology and Data Science
- Blockchain Data Science
- Data Science Programming
- Behavioral Data Science
- Data Science Technologies
Scope of Data Science-
Data science is used in almost every field. It has many applications which are being used widely in different industries.
1. Manufacturing and Production– In the field of manufacturing and production, the applications of data science are being used widely to prevent defects and reduce the chances of waste of raw materials and resources.
The techniques of statistical analysis are being used in monitoring operations and products to detect defects and minimize them.
Data science has applications in keeping track of the maintenance of the machinery by analyzing the records of usage and repairs. It can help prevent the failure of machinery or determine the date on which it should be replaced to minimize losses.
2. Transportation– in the field of transportation, data science has many useful applications. Travel agencies use records to find out the best time for a trip and manage its expenses. The shortest ways to commute can be identified by analyzing the records of past traveling experiences so that losses can be prevented.
Traffic authorities use data science tools to analyze the records of traffic congestions to identify the hotspots. By identifying the hotspots, they can make informed decisions on diverting the traffic to other ways or optimizing the signal for less congestion.
3. Education- in the education sector, data science has been used by schools, colleges, and other institutions to analyze the performance of students. They analyze the past data of their performance like test marks, attendance, and extracurricular activities.
This way they can identify the students in need of more attention from their parents and teachers. The same data can be used to identify their strengths and weaknesses so that they get personalized guidance from the faculty toward their overall improvement.
4. Advertising and Marketing- in advertising and marketing, data science is used to analyze the data collected by marketing agencies through ad campaigns, social media platforms, sales records, and surveys.
This data is very valuable and can be used for many applications. It can be used to identify the interests of different segments of customers.
It can be used to find out the demand for certain products in a specific age group or demographic segment. This way data science tools can be used to tailor profitable ad campaigns.
5. Healthcare- The healthcare industry has been revolutionized by the applications of data science. Data science is now being used by healthcare providers to tailor personalized treatment plans for a patient by analyzing their past medical records like diseases, complications, genomic data, and drug use.
It’s one of the most efficient ways of treatment. The use of data science applications has been very advantageous for the development of medicines with the use of statistical tools to prevent side effects.
A widespread disease can be tracked down by collective analysis of a population so that chances of getting infected are reduced by providing people with the best treatment and proper guidelines.
Explore Now,
- Data Science and Machine Learning
- Data Science from Scratch
- Data Science and Business Analytics
- Data Science Companies
6. Finance– Finance is a sector that has been using data science for a long time. Data science has many applications in the world of finance including keeping track of all the resources including the capital of the company.
The analysis of previous records of market fluctuations helps in the prediction of future trends in the market. It allows companies to allocate their resources wisely according to the situation. Machine learning tools are used in profitable trading by analyzing the fluctuations in stock prices.
7. Energy- Energy providers use the previous data on the usage of electricity in different areas to identify opportunities to save electricity by optimizing production and distribution.
This helps them to prevent the peak load. The weather patterns can be analyzed to predict the weather conditions. It can be used to identify the opportunities to use solar energy and wind energy to produce electricity.
This can help in reducing the carbon footprint and save the environment from some extra pollution.
Overview of the edX Data Science Course Certification Program-
Harvardx’s Data Science Professionals Certificate Includes the Following Courses.
1. Data Science: R Basics
edX data science course offers the basic knowledge of R. It helps students to build a strong foundation in R. This course includes data wrangling, analysis of data, and data visualization.
edX data science course is self-paced, so students can progress at their own speed. On average this course takes 8 weeks with the suggested commitment of 1-2 hours of studying per week.
edX data science institute focuses on retaining the knowledge of R by solving real-world specific problems. edX data science course uses real-world datasets about crime in the USA.
Prerequisites- An up-to-date browser is required to enable programming directly on the browser interface.
The curriculum of the edX data science course includes Basic R syntax, and foundational R programming concepts such as data types, vector arithmetic, and indexing. Performing operations like sorting, data wrangling, and making plots using R are also included.
2. Data Science Visualisation-
edX data science course offers a thorough knowledge of basic principles of data visualization and their applications using ggplot2. Many good examples of ggplot2 are used.
The course starts with simple data sets and gradually leads to case studies on different topics to make participants understand everything.
Three motivational examples are used and ggplot2 a data visualization package for statistical programming language is used as well.
Apart from that, the occurrences of unexpected problems due to systematic errors or biases are also introduced so that the participants learn to handle the data set with care.
Students learn about data visualization principles, the application of ggplot2, and the weaknesses of some widely used plots.
edX data science course takes 8 weeks to complete with 1-2 hours of studying per week. The course is self-paced and allows students to learn on their schedule.
3. Data Science: Probability-
Probability theory is very important for building the mathematical foundation of statistical inference which is essential for the analysis of data affected by chance.
edX data science course offers theoretical knowledge of probability and then explains its practical aspects by using case studies on the financial crisis of 2007-08.
edX data science course demonstrates the downside of underestimating the risk of some securities sold by financial institutions. Many important concepts such as random variables, independence, Monte Carlo simulations, expected values, and standard errors are included in the curriculum by edX Data Science Institute.
Students learn important concepts in probability theory, performing a Monte Carlo simulation, standard errors, and the importance of the Central limit theorem from the edX data science course.
This course takes 8 weeks to complete with 1-2 hours of studying per week.
Also Check,
- Can Data Scientists Work From Home
- Can Data Scientist Become Software Engineer
- Can Data Scientists do Freelance
- R for Data Science
- Python for Data Science
- Artificial Intelligence and Data Science
- Introduction to Data Science
4. Data Science: Inference and Modelling-
This course covers two of the most widely used data science statistical tools: inference and modeling. These tools are indispensable for analyzing data affected by chance. This course uses a case study on election forecasting for example.
This course shows the effectiveness of inference and modeling as statistical tools with the usage of R. edX data science institute teaches the participants to make predictions and also provides an estimate of the precision of a forecast.
After learning that, participants can learn confidence intervals and p values, Bayesian modeling.
Students learn to estimate the margins of error of parameters, population, and standard errors for making predictions about Data science. edX data science institute also offers knowledge of the basics of Bayesian statistics and predictive modeling.
This course takes 8 weeks to complete with 1-2 hours of weekly commitment to studying.
5. Data Science: Productivity Tools-
This course helps students to learn to keep their projects organized and produce reports using git, Unix, Rstudio, and GitHub. A data analysis project typically has many different parts which include different data files and scripts.
It can be a difficult task to keep it organized. This course explains the application of different tools like Unix/Linux for managing files and directories to keep the system organized.
edX data science institute includes git as a tool to keep track of changes in scripts and reports. The use of GitHub is also included in this course.
The duration of this course is 8 weeks. 1-2 hours of studying per week is suggested.
6. Data Science: Wrangling-
This course teaches the process of converting raw data into different formats needed for analysis. Data wrangling includes many steps like importing data into R, tidying data, string processing etc.
These steps are not necessarily used in every case but every data science will have to use one of these situations at some point.
Data is not easily accessible as it is most of the time present in different formats like databases, web pages, tweets, and PDFs. The data has to be imported into R for tidying the data.
This step of converting raw data into tidy form is called data wrangling. This is a very crucial step for a data scientist. The duration of this course is 8 weeks. Students have to study for 1-2 hours per week.
Find Out Some More Courses,
- Data Science Course Syllabus
- Data Science Courses For Beginners
- Data Science Courses After Graduation
- Are Data Science Certificates Worth It
- Are Data Science Jobs Safe From AI
7. Data Science: Linear Regression
This course teaches the application of R for the implementation of linear regression. It is one of the most common statistical modeling approaches in data science. It is generally used to quantify the relationship between two variables.
In data science practices it’s very commonly required, as the relation between two variables is required to be identified on many occasions. The case study of the construction of a baseball team is used to simplify the process.
This technique is very powerful yet can’t be used everywhere. It’s important to know the appropriate situations to apply this technique. This technique was originally developed by Galton.
8. Data Science: Machine Learning-
This course introduces the science behind one of the most important and successful data science techniques. Machine learning has many applications. These applications are nowadays widely used for postal services, speech recognition, movie recommendation systems, and spam detectors.
This course by edX Data Science Institute includes the most popular machine learning algorithms and principal component analysis. Students learn its practical application by building a movie recommendation system.
Students also learn to cross-validate to avoid overtraining. The duration of this course is 8 weeks with 2-4 hours of weekly studying.
9. Data Science: Capstone-
By completing this capstone project, students apply their knowledge and skills in data analysis using the R that they have learned so far. This project tests the knowledge of students in data visualization, modeling, inference, wrangling, regression, and machine learning.
This course by edX data science institute won’t include detailed guidance and support from instructors. By completing this project, students will finally have a data product to show off to potential employers or educational programs.
It would show how much the students have learned throughout the course by indicating their expertise. This prepares students to work independently on a data analysis project. This course takes 2 weeks to complete with 15-20 hours of studying per week.
Features of The Data Science Certification Program by HarvardX
The experts from Harvard University teach students about different concepts, fundamentals, tools, techniques, and methodologies of data science using R. Almost all the essential topics are covered in this course.
The curriculum includes 9 courses designed to help students learn the important skills and tools of data science at their own pace at their schedule. The duration of this edX data science course is 1 year and 5 months which gives students more than enough time to study it in detail without rushing.
Students get to drive their careers forward with Harvard University-backed credit programs and verified certificates. Students can also try a course before they pay if they have any doubts.
Students get a supportive network of peers and professionals with whom they can communicate and collaborate on different projects.
Why Choose Data Science as a Career?
1. High Demand– Data science is a booming career. Its applications are so widely used in different industries that almost every business requires talented data scientists who can help their company improve its performance.
The demand for data science professionals is high and is expected to grow at an even faster rate as companies keep generating colossal amounts of data and the world is becoming data-dependent.
2. Global Reach– The individuals who want to progress in their careers as data scientists have many perks. As the demand for data scientists is peaking, there are work opportunities everywhere.
They can very easily find a job in their towns. Even if jobs are not available in the local area, they can opt for freelancing or remote work. They can work for global companies and clients at a high return rate due to international transactions.
They can work for well-reputed institutions which would help them to grow at a fast rate in their careers.
3. High Salary– Data scientists enjoy a high salary for their talent and skills. Their skills and expertise are Highly valued among industries as they require highly qualified data science professionals to help their businesses expand.
Gathering useful information from a bunch of raw data is a skill that is very necessary for the growth and improvement of the performance of the company. Companies understand how crucial this job is and that’s why they are ready to pay a high amount for this job.
4. Flexibility– The job of a data scientist can be very flexible. It offers many opportunities to work from home or work from the office. Data scientists can stay in their homes and work for big well-renowned companies at a high payment.
They are free to work from anywhere in the world. They can work from beaches, parks, restaurants, wherever they want. They can organize their workspace and work on their schedule without any interruption. This job is an ideal job for people who are seeking autonomy in their careers.
5. Diverse Career Paths– Every business, big or small, from any field like healthcare, education, transportation, agriculture, and many other fields generates a huge amount of data. All of them require well-skilled data science experts who are proficient and experienced.
It means that data science professionals can choose to work in one of these fields according to their interests and preferences. They can work for sports industries, agriculture, manufacturing, retail, advertising, or any other field wherever they please.
6. Job Security– As the demand for data science professionals keeps on increasing, there are now plenty of jobs for data scientists available everywhere. A data science professional does not need to worry about finding a job, as there are plenty of options.
Even if they don’t find a job in their locality, there is an opportunity to work for global companies and clients through freelancing or remote job opportunities. Data science careers are in high demand and are expected to stay that way if not grow exponentially.
7. Continuous Learning– Data science is an ideal career option for people who like to keep challenging themselves by learning new skills. Data science as a career requires active involvement in the field by learning new tools and techniques of data science.
By learning new techniques and tools, an individual can develop great problem-solving skills. This skill is highly valued by companies as they need professionals who can solve problems efficiently.
Apart from professional life, problem-solving skills are very important in personal lives. Collecting important information and using it to make informed decisions is very important in the life of a person.
Frequently asked questions-
Question 1. Are There Any Prerequisites to Enroll in Edx Data Science Institute?
Answer- Not for the first course. But as you move to further courses, you require a proper understanding of the previous one.
Question 2. When Do Courses Get Updated?
Answer- In March and October, new versions of all 9 courses are launched.
Question 3. What Should Be the Order of These Courses?
Answer- Exactly as shown in this article. All the nine courses should be followed in the very same order, as each course builds the foundation for the next courses.
Conclusion-
edX is one of the most reputable and reliable online education platforms. edX data science course covers all the essential data science topics using R. Students who want to pursue this data science career can get a kick-start by choosing this course.
With industry industry-relevant curriculum, this course is designed by the experts and experienced professionals of Harvard University. The quality of the curriculum is second to none, and it’s very hard to find a better data science course than this course.
