Applied Data Science Bootcamp

Become a data-driven decision maker with live teaching from MIT faculty, hands-on projects, and mentorship from industry practitioners

  • 12 Weeks
  • Live Classes by MIT Faculty
  • Mentorship by Experts

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Program Delivered By Faculty From:
Massachusetts Institute of Technology
Institute for Data, Systems, and Society (IDSS)

Why Join the Applied Data Science Bootcamp

Live Teaching by
MIT Faculty
  • Weekly Live online classes from award-winning MIT faculty
  • Curriculum designed to build industry-valued skills: Machine Learning, NLP, Deep Learning, and Python.
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Personalized Mentorship and Support
  • Live mentorship and guidance from data science practitioners on weekends
  • Collaborative yet personalised sessions in small groups
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Practical, Hands-on Training
  • Complete hands-on exposure through 6 projects under the guidance of industry experts
  • Final 3-week Capstone Project on a real-world business problem
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Working Professional
Live online classes every week from award-winning MIT faculty
Career Support
Personalised mentorship and guidance from data science practitioners
Salary Hike
Hands-on training via 6 projects and 1 Capstone
Industry Project
Curriculum covering Machine Learning, Deep Learning, NLP, and Python

Accelerated Data Science Bootcamp for Working Professionals

Live Classes by MIT Faculty | Mentorship from Experts | 12 Weeks

Certificate awarded on completion

QS World

World #1

MIT Rank in World Universities

QS World University Rankings, 2020

us news

U.S #3

MIT Rank in National Universities

U.S News & World Report Rankings, 2020

Cohort Start Dates

Great Learning-Online Center

Online

19th Oct 2020

Frequently Asked Questions

Program details
What will the timings of the live sessions?
The live classes with MIT faculty will be held on Mondays, Wednesdays, and Fridays at 10 AM ET. The mentorship sessions with industry experts will be held in small groups of learners on weekends. The exact timings will be determined based on the time zones of the learners in a particular mentorship group.
What is the weekly time commitment required?
Each week involves 6-8 hours of live teaching sessions, which includes hands-on practical applications and problem-solving. Additionally, based on your background, you should expect to invest between 2 to 4 hours every week to self-study and practice. So that amounts to a time commitment of 8-12 hours per week.
Is the program completely online?
Yes, the program has been designed keeping in mind the needs of working professionals. Thus, you can learn the practical applications of data science from the convenience of your home and within an efficient 12-week duration.
Will I have to spend extra on books, online learning material, or license fee?

No. All the requisite learning material is provided online to candidates through the Learning Management System.

Admissions and Eligibility
Are there any prerequisites for the program?
You should possess a working knowledge of computer programming and statistics.
What is the admission process?
You will need to complete your online application form. On receiving the application, the admissions committee will review it to determine your fit with the program. If selected, you will receive an admission offer for the upcoming cohort. Secure your seat by paying the fee.
Fee and Payment
What are my payment options?
Candidates can pay the course fee through Bank Transfer and Credit/Debit Cards. For further details, please get in touch with us at adsb.mit@mygreatlearning.com.
Can my employer sponsor the program fee?
We accept corporate sponsorships and can assist you with the process. For more information, please write to us at adsb.mit@mygreatlearning.com.

Still have queries? Contact Us

Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at adsb.mit@greatlearning.in or +91 9606053237

Application Deadline 1st Oct 2020

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Check out the program and fee details in our brochure

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Applied Data Science Bootcamp: A Future to Rely On

Data Science has emerged to be one of the most-talked-about topics in the recent past. It focuses on collecting data and drawing meaningful conclusions to aid business growth. A plethora of data is available to us today, and this number is increasing as the days go by. In the age of big data, this can be a great asset to any company, if used efficiently. Until 2010, the main focus was around being able to store this valuable information that would later be used to draw insights. With the introduction of frameworks such as Hadoop, the storage of data has been taken care of. The focus is now on the processing of data.

Data science can be broadly defined as the study of data, what it represents, and ways in which it can be transformed into a valuable input or resource to create business and IT strategies. By enrolling in our Data Science Bootcamp, you can become a successful data-driven decision-maker. Our Applied Data Science Bootcamp provides you with live teaching from MIT faculty, hands-on projects, and mentorship from industry experts.

Why is Data Science important to a business?

Compared to the olden times, we have come a long way. We now work with large mines of structured and unstructured data instead of small sets of structured data. Traditional BI tools fall short, and data science comes with a range of advanced tools to work with. An online data science Bootcamp can help you learn how to make sure of your data in the right manner and thus, increase your chances of a successful career ahead. A few examples of the use cases of data science are as follows.

1. Data Science can be applied to predictive analytics. We can perform weather forecasting with the help of data collected from satellites, radars, and ships. A forecast model can be built to predict natural calamities with precision. This process can help us in making the right decisions and avoiding possible damage.

2. Product recommendation systems have become extremely precise. Vast volumes of data can be used to train models better and more effectively to show more accurate recommendations to the user. Some great examples of these systems would be Spotify or Netflix.

3. Data Science also aids in decision making. An intelligent vehicle will be able to collect real-time data from surrounding radars, cameras, and lasers. Based on this data, decisions regarding speeding, stopping, and turning can be taken. The Applied data science Bootcamp program can help you gain all the required skills and information.

Data Science Applications

Data science has paved its way into almost every industry. Its applications range from fraud detection to gaming. Some areas where data science is commonly used are listed below.

1. Fraud & risk detection, and credit scoring

2. Healthcare

3. Energy Management

4. Internet search and targeted advertising

5. Recommendation Systems

6. Image and Speech Recognition

7. Gaming

8. Augmented Reality

Why choose the applied data science Bootcamp?

The data Bootcamp has personalized mentorship and support. The weekly live online classes are conducted by award-winning MIT faculty, and the curriculum has been designed to build industry-relevant skills such as Python, NLP, Deep Learning, and Machine Learning. You can experience hands-on exposure through six projects and a final 3-week capstone project on a real-world business problem. Enrolling, in a data science and machine learning Bootcamp with Python, covers most of the relevant skills required in the data science field today.

Essential Skills to Become a Data Scientist

Data Science as a field is the confluence of mathematical skills, strong business acumen, and technology skills as well. These skills build the foundation of our knowledge and are required for you to grow in your career. Our online data science Bootcamp covers all the essential skills to become a Data Scientist. Here are some of the skills that are required.

1. Mathematical Expertise: There is a common misconception that Data Analysis is all about statistics. Bayesian statistics and classic statistics are very crucial to Data Science. But, concepts such as quantitative techniques and linear algebra, which are the support system for many inferential methods and machine learning algorithms, are crucial too.

2. Strong Business Acumen: As a data scientist, you are the source of deriving useful information. This information is highly critical to the business. You will also be responsible for sharing this knowledge with the relevant teams, and individuals to be applied in business solutions. Data Scientists are critically positioned to contribute to the business strategy as they have exposure to data like no one else. Hence, joining this data science Bootcamp can help you gather these skills and enable you to fulfil your responsibilities effectively.

3. Technology Skills: As a data scientist, you are required to work with complex algorithms and sophisticated tools. Another principal responsibility is to prototype quick solutions with the help of programming languages. These programming languages can range from Python, R, SQL, and SAS, to sometimes Java, Scala, Julia, and others. Being able to navigate their way through technical challenges that might arise and avoiding any bottlenecks or roadblocks that might occur due to a lack of technical soundness is another essential skill. Joining the Applied data science Bootcamp will allow you to learn these skills comprehensively, with the help of industry experts and skilled faculty.

Who is a Data Scientist?

Professionals who work on sourcing, gathering, and analyzing massive sets of data are known as data scientists. Business decisions are powered by data analysis, and thus data scientists are crucial to any business. A data scientist is required to have exceptional communication skills, industry knowledge, and technical expertise to meet business needs. Thus, a data analysis Bootcamp will help in upgrading your skills to meet the requirements.

Roles in the field of data science

Apart from the role of a data scientist, you can get other opportunities as well. Let's take a look at a few different jobs roles that you work in after you complete the applied data science bootcamp.

Data Analyst: The role of a data analyst serves as a bridge between business analysts and data scientists. One of the critical areas that a Data Analyst works on is working on finding results by organizing and analyzing the given data. They also work on translating technical analysis to action items and communicating these results to the concerned stakeholders. Data wrangling and data visualization skills are also required, along with mathematical skills.

Data Engineer: A data engineer works on managing large amounts of rapidly changing data. They manage data pipelines and infrastructure. This is done to transform and transfer data to the respective data scientists. They majorly work with Java, Scala, MongoDB, Cassandra DB, and Apache Hadoop.

Machine Learning Engineer: Machine learning engineers are liable to formulate and implement several machine learning algorithms like classification, regression, clustering, etc. Machine learning engineers are also responsible for developing data pipelines.

Data Scientist: The most crucial job role of a data scientist is understanding the business requirements and offering the best business solutions. Data Scientists perform data analysis, data processing to identify the trends and patterns of the data that help organisations to make better decisions.

Apart from the above described job roles, the domain of Data Science offers many other exciting job roles such as Statistician, Business Analyst, Data Architect, Data Analytics Manager and many more.

How to Become a Data Scientist?

Data Science offers a lucrative career for all enthusiasts. Candidates who have an educational background in statistics, mathematics, computer science, or Information Technology may find it easier to make the transition into a data science camp. However, ask yourself the following questions before you make the transition to data science.

  • Are you fond of programming languages?
  • Do you love working with complex datasets?
  • Are you someone who likes to find patterns in random data structures?
  • Do you have an educational background in computer science, mathematics, statistics, IT, or a similar branch?

1. Pick up Maths and Statistics Skills

Mathematics and statistics are the fundamentals of data science, but ML follows closely. You must learn the basics of ML, since it is used for several data science applications such as creating forecasts, and data modelling patterns. Having this knowledge will enable you to design and use algorithms for data modelling.

2. Learn Programming Languages

One of the many requirements to learn data science is the skill of programming. So, you should improve your coding skills. Programming languages like Python, R, and SAS help data scientists to read and analyze data sets. Due to its flexibility, Python is regarded as one of the most widely used programming languages. If you are working with querying, you will benefit from learning SQL.

3. Learn Data Munging and Reporting Skills

As an aspiring data scientist, you should learn data munging and reporting. Data munging helps us in identifying and discarding redundant data, whereas, reporting ensures that it’s put into a readable and actionable format.

These skills are required for familiarising a candidate with the basic programming and statistical models and gradually teaches them fundamentals of the domain. Before moving ahead, let us now understand the requirements for you to join the Applied data science online Bootcamp.

The only prerequisite for you to join the Applied data science Bootcamp program is a working knowledge of computer programming and statistics. Let us take a look at further details about the program.

Responsibilities of a Data Scientist

As a Data Scientist, you will be responsible for assessing the new data analysis tools and preparing reports on their effectiveness. Along with these tasks, you will also be responsible for the following:

  • Assess new data analyzing tools and prepare reports on their effectiveness
  • Develop custom data models and algorithms to analyze product specific data
  • Mine and analyze data to optimize, and improve outcomes
  • Handle various marketing techniques and business strategies
  • Working with stakeholders to identify and leverage data optimization opportunities
  • Creating predictive modelling that can be used to improve product engagement, stakeholder communication, and revenue generation
  • Building an A/B test framework and running quality checks
  • Developing tools and processes for performance management and quality management
  • Coordinate with stakeholders to implement new tools and monitor outcomes

Top Tools used by Data Scientists

Data Scientists work with numerous tools, some of which are listed below.

1. SAS: A data science tool that has been developed specifically for statistical operations, SAS is primarily used by large organizations. SAS is a closed-source proprietary software and it uses the SAS programming language to perform statistical modelling. As a data scientist, this tool can be helpful in modelling and organizing data.

2. Apache Spark: This tool is one of the most widely used tools in the world of Data Science. Apache Spark was developed specifically to work on steam processing and batch processing. Several Machine Learning APIs come with Apache Spark, enabling it to make crucial decisions. Another key feature of Apache Spark is its ability to process real-time data as opposed to historical data.

3. BigML: BigML provides an interactable cloud-based GUI used for processing Machine learning algorithms. The speciality of BigML is predictive modelling, and it uses several ML algorithms such as time-series forecasting, classification, and clustering.

4. D3.js: This is a javascript library that allows you to make interactive visualizations on your browsers. It also makes use of animated transitions and allows client-side updates. Combining D3.js with CSS will allow you to create illustrations that can help in implementing customized graphs on your webpage.

5. MATLAB: A multi-paradigm computing environment, MATLAB is used for processing mathematical information. This tool is widely used among data scientists and can be used for stimulating fuzzy logic and neural networks. It makes use of image processing and helps in creating compelling visualizations. Its features include easy integration and embedded systems. However, there is one limitation to this tool. It is a closed-source proprietary software.

Top Data Science Books 

Data Science includes various concepts such as statistics, linear algebra, and more. The subject is vast and thus paves the way for unlimited possibilities. While mastering Python for data science and machine learning bootcamp is a sensible way to gain a comprehensive understanding of the several concepts of this technology, books are another great way to do so. Courses can provide you with a lot of structured experience, but another way to continue your learning process is to read books created by experts in the field. Books will help you understand the different perspectives and also to compare these approaches to choose the most optimal solution. Here’s a list of the top Data Science books that will help you deep-dive into concepts and apply them to your work. 

Future of Data Science

Data Science is the future. Most of the upcoming businesses and organizations will not be able to keep up without integrating some form of data science into their daily operations. A large number of transitions have already happened worldwide. Businesses are seeking more data-driven decisions; more such choices are to follow suit. Data science quite rightly has been said to be the most sought after job. So, if you are considering pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and great returns, but also a lot of job security. In India, there are around 15, 000+ existing job opportunities for various job roles in the domain of Data Science. Here are the top data science interview questions and answers for entry level and mid-level job seekers.

Payscale of Data Science in India

The domain of Data Science is establishing new dynamics and is observed to extend its territory. Data science is already being applied in many fields such as healthcare, finance, marketing, e-commerce, gaming, robotics, and many more. This domain also claims to offer one of the highest-paid job roles in the country. The pay scale offered in this domain varies from 4 lakh to 20 lakh per annum. The average pay scale of a data scientist in India is estimated as 8 lakh per annum.

Choose the prestigious Applied DataScience Bootcamp to become a successful data scientist. Even as there are many institutions offering several programs in Data Science, why should you choose the Applied Data Science Bootcamp program offered by Great Learning?

Great Learning stands as one of the most reputed institutes in offering a wide range of professional courses in domains like Data Science, Artificial Intelligence, Machine Learning, Business Analytics and much more. Great Learning has established its standard in delivering top-quality education to its learners. Apart from the quality of education, there are many other reasons that make this program stand out of the crowd.

Mentorship

Candidates will be assigned with personal mentors that assist and guide them throughout the end of the course. These mentors are industry experts and data science practitioners. Candidates participate in live mentorship and guidance sessions that help them clear their doubts and enhance their learning.

Faculty

This program will be taught by the highly reputed and award winning faculty of MIT.As MIT stands as one of the world renowned institutes, you will get to learn from the world’s best faculty team that empowers you to become a successful data scientist.

Curriculum

The curriculum of this program is designed by the highly experienced industry experts. The curriculum comprises industry relevant concepts and tools like Machine Learning, Deep Learning. Candidates that take up this program will gain industry relevant skills, and also learn the requisite set of tools that makes them job ready.

Hands on Learning

Candidates work on 6 different projects throughout the course under the constant guidance of industry experts to gain hands on expertise. The capstone project is the cornerstone of the learning journey as it covers all the tools and techniques the candidates have mastered during the program. The capstone project is a real-world business project that lasts for 3 weeks. This enables the candidates to employ their learning and gain complete practical expertise on all the tools.

Besides the above mentioned, there are a lot more that Great Learning offers for the ones that take up this program. If you are aspiring to become a data scientist, grab the best opportunity by enrolling yourself to this program.