Associate Program in
Big Data Analytics

Part-Time | Blended | 6-Month

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Associate Program in Big Data Analytics

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IBM - Edtech Partner

Associate Program in
Big Data Analytics

Part-Time | Blended | 6-Month

 


+91-98406-68844

About APBDA
Program Benefits
Curriculum
Pedagogy
Instructors
Admission Process
FAQ

The size of the businesses keep increasing, and hence the volume of the data (as well as the variety) each business deals with is also increasing significantly day-by-day. To harness the potential of the internal and external data available, corporates have to apply the analytical techniques and tools to generate better insights leading to a better return on investments. So, Big Data Analytics has emerged as one of the brightest Data Science disciplines for individuals with multiple academic and professional backgrounds today.

GradValley’s Associate program in Big Data Analytics (APBDA) is a 6-month program offered in both direct and virtual modes with a comprehensive curriculum developed in order to meet both the academic and industrial needs. This program has been carefully designed for the big data analytics aspirants to accumulate the expertise in Machine Learning, Data Analytics using R, Python, Hadoop, Business Intelligence, and Data Visualization tools.

Our APBDA program provides a holistic approach towards the analytics arena and enables learners to appear for the Associate Big Data Analyst (ABDA) Certification from the Data Science Council of America (DASCA). The ABDA™ by DASCA is the world’s most credible 3rd–party, vendor–neutral certification for young business–school learners and those graduating with majors like Statistics, Applied Mathematics, and Economics, dreaming of making exciting careers in Big Data. You can trust the internationally accepted and recognized ABDA™ Credential to prove best, your proficiency, potential, and promise for Big Data Analytics.

DASCA Certification

The professional certification from DASCA is an edge to your Data Science career (like any other certification from IBM, Oracle, etc) and is highly regarded as a ticket to vouch the data science knowledge and skills that an individual claims to possess. So, any Data Science company’s trust on the certified individual makes them stand out in the crowd.

GradValley; IBM’s Edtech Partner

IBM, being a pioneer in the information systems and data science, has endorsed GradValley’s curriculum and also provide training, again authenticating the knowledge and skill-set a data science professional acquires by being a GradValley learner. The Program certificate will also carry IBM logo as an added proof.

Guaranteed Placement Assistance

GradValley provides guaranteed placement assistance for the candidates who have an excellent track record of academics, data science knowledge, application of their skills, and the attendance during the learning period with GradValley along with successful completion of the internship or the Capstone Project.

Live and Recorded Classes

Be it virtual or direct, GradValley offers live, instructor-led sessions that are recorded and added to the library for the learner's future references during the program.

Learning Management System (LMS)

All of our live calssroom sessions are recorded and added to our resource library in our comprehensive Learning Management System (LMS) specifically for our registered learners for future reference during the program. LMS can be accessed over the internet 24/7 for the video lectures and course content. Mobile application is also available for accessing the LMS on-the-go.

Hands-On Learning

Our program curriculum is well-crafted to strengthen foundational concepts of mathematics, statistics, programming, machine learning, analytics, visualization, and big data. Learning is mainly through hands-on training with the application of concepts using relevant tools and techniques.

State-of-the-Art Andragogy:

Our industry-standard curriculum is framed to bridge both academic and industrial needs with an innovative, andragogic approach. The learners are self-driven to learn through problem solving exercises, cracking case studies, developing use cases, and other innovative approaches.

Experiential Learning

Be it an internship or a Capstone Project, GradValley strongly believes in learning by solving real-world problems. The learner's involvement in such projects help them experience holistic learning and experiment on innovative solutions for the pain points of the industry.

Innovation Smart Lab

GradValley's R&D wing is highly associated with industry research projects in various domains offering many data science projects acting as the Center of Excellence (CoE) for GradValley. GradValley and the Innovation Smart Lab gp hand-in-hand to bridge the gap between academia and industry.

Industry Mentorship

GradValley has tied up with many experienced data science professionals and researchers within the industry and corporations to guide and mentor our learners during the course of the program.

Career Guidance

Workshops on resume preparation, mock interview sessions, language proficiency, communication and soft skills, how to be a continuous learner, and many more in line are conducted for the learners to experience.

Industry Sessions; Ready, Set, Go!

Lectures, seminars, and workshops given by data science professionals from the industry will be organised to help learners with understanding what is happening outside the classroom in the data science and analytics world within various business domains like retail, healthcare, manufacturing, finance, etc.

Learning Coverage

The program aims to develop the learners’ basic understanding of the data analytics with big data tools-like R and Python-enabling them to provide suggestions and recommendations in decision-making within a business.

The program covers six different courses with around 145 hours of instructor-led classes, inclusive of course delivery, industry sessions like guest lectures, seminars, workshops, live projects, and career guidenace sessions.

Course Learning Outcomes

1

Introduction to
Data Science and
Big Data Analytics

This course “Introduction to Data Science and Big Data Analytics” enables the learners to:

Explore about the evolution of the Data Science and Big Data.

Know how the data is being generated exponentially in businesses.

Understand on how the business data generated is to be handled with Big Data technologies, governed and secured.

2

Foundational
Statistics and EDA

At the end of this course, the learners will be able to:

Apply the concepts of averaging, variations, skewness, and kurtosis in solving a business problem.

Discuss about the formal hypothesis testing and relate testing procedures to estimation via confidence intervals.

Predict one variable from the other variables and determine the line of best fit as well as apply multiple regression and multiple correlation techniques.

3

Analysing Data
through R and Python

This course enables the learners to:

Understand the fundamentals of programming using the Data Analytic tools.

Perform analytical modelling with data.

Create graphs using the data for decision makers using various libraries in both two and three-dimensional spaces.

Mine the information hidden within the data to aid decision making in the businesses problems.

4

Understanding
Machine Learning

At the end of this course, the learners will be able to:

Handle, clean, and prep the data.

Engineer the features.

Develop a model with the data with the right supervised and unsupervised algorithms through development, validation, and testing.

Overcoming the challenges like over-fitting and under-fitting the data.

5

Big Data Ecosystems
and Frameworks
with Hadoop

This course makes the learners gain expertise in:

Map Reduce and its qualities.

Hadoop data storage principles.

Big Data tools like Hive, Pig, Yarn, Scoop, Oozie, Storm, Flume.

Creating solutions using analytics in the Big Data environment.

6

Data Visualization

This course will make the learners:

Handle, explore, and visualize the data of various business domains through business intelligence tools like Tableau and QlikView.

Present the data in the graphical format to help the business users to take decisions by discovering new patterns.

Pedagogy

Our Pedagogy hexplains how we implement the instruction of our well-framed curriculum. It involves three different phases: Foundation, Specialization and Realization.

Foundation Phase

During this first and foremost phase of the program, the foundational concepts of statistics, programming, data science and analytics will be covered.

Specialization Phase

In the second phase of the program, core concepts of machine learning, big data frameworks, and data visualizations are covered. In this phase, the learners can test their understanding through problem solving exercises, case studies, etc.

Realization phase

This is the most important phase because the learners will be applying their knowledge in data science for a real-life problem and try to figure out the best alternative solution.

Industry Lectures

In addition to the course content, GradValley will be offering seminars, guest lectures, and workshops by data science professionals from the industry in parallel to the course curriculum for a better understanding among the learner community. It also aids in bridging the gap between the academia and industry.

Mentoring Through Industry Experts

To ensure the overall development of our learners, we provide regular mentoring sessions. Each and every learner enrolled for the program will be assigned to a data science professional in the corporate and contact sessions will be arranged on a one-on-one basis.

Capstone Project/Internship

As a part of the program completion, the learners have to complete the capstone project or do an internship.

Durga Gadiraju

Durga Gadiraju

Founder & Technology Evangelist, IT Versity LLC

Koushik M L N

Koushik M L N

Head of Engineering IT Versity LLC

Maitreyi Mandal

Maitreyi Mandal

Lead Data Scientist, Freshworks Inc

Mohan Raj

Mohan Raj

Product Specialist at Informatica

Sudalai Rajkumar

Sudalai Rajkumar

Lead Data Scientist, Freshworks Inc

Tanmay Bakshi

Tanmay Bakshi

Keynote & TEDx speaker, Algorithmist, Author, Watson & Cognitive Developer, YouTuber

Vinay Chandragiri

Vinay Chandragiri

Data Scientist IIT Guwahati

Program Prerequisites:

Should have completed or pursuing (final year) Bachelor’s/Master’s Degree in Computer Science/Mathematics/Statistics/Applied Sciences/Economics/Social Sciences/Engineering/Technology/Finance/Management/other related disciplines.

Work experience is not mandatory.

Familiarity of the following concepts will be helpful during the program:

Basic concepts of statistics and math/p>

Comfortable with handling databases

Basic understanding of how big data is used in business

To apply for the Associate Program in Big Data Analytics, click the apply now button.

Admission Process:

Interested candidates need to apply through our online application form.

Interested and eligible candidates will go through the GradValley Entrance Test.

Based on the aggregate marks scored, the candidates will be shortlisted.

TThe selected candidates will be notified within two weeks after appearing for the entrance test with the conditional admission offer letter.

After receiving the conditional admission offer letter, the registration fees should be paid on the due date mentioned in the conditional admission offer letter sent to confirm the seat.

Please note that though the candidate has been selected for the program, if they have not confirmed the admission by paying the registration fees within the due date the candidature becomes invalid as the admission will be closed upon reaching the required number of candidates.

For more information, reach us @ +91 98406-68844

What should I expect from programs in Big Data Analytics?

You can expect to learn the basic science of how data can be understood, visualized, analysed, reported, and utilized for businesses. It is a well-rounded program with hands-on lab activities with possible running use case/s. We make sure to support, assist, and organize mentoring sessions from industry experts through guest lectures, seminars, internships, projects, etc.

What are the topics to be covered during the program?

Please visit the link to know the modules to be covered during the program. To check out the entire curriculum, please reach us @ +91 98406 68844

What kind of job roles will I be eligible for after successful completion of the Big Data Analytics program?

After successful completion of the program, the candidates can apply for the job roles like Junior Data Analyst, (Big) Data Analyst, Data Scientist, Analytics Manager, Business Analyst etc. based on their domain experience and analytics skill-set.

How can we differentiate a data scientist from a data analyst?

Data Scientist Data Analyst
Predict the future based on past patterns Curates meaningful insights from data
Estimates the unknown Looks at the known from new perspectives
Expected to generate their own questions Finds answers to a given set of questions.
Finds business problems Addresses business problems
Need strong business acumen and data visualization skills Does not need a strong business acumen and data visualization skill
Examines data from multiple disconnected sources Looks at data from a single source
Build statistical models and has to be well-versed in ML Does not build statistical models and is not required to be well-versed in ML

The size of the businesses keep increasing, and hence the volume of the data (as well as the variety) each business deals with is also increasing significantly day-by-day. To harness the potential of the internal and external data available, corporates have to apply the analytical techniques and tools to generate better insights leading to a better return on investments. So, Big Data Analytics has emerged as one of the brightest Data Science disciplines for individuals with multiple academic and professional backgrounds today.

GradValley’s Associate program in Big Data Analytics (APBDA) is a 6-month program offered in both direct and virtual modes with a comprehensive curriculum developed in order to meet both the academic and industrial needs. This program has been carefully designed for the big data analytics aspirants to accumulate the expertise in Machine Learning, Data Analytics using R, Python, Hadoop, Business Intelligence, and Data Visualization tools.

Our APBDA program provides a holistic approach towards the analytics arena and enables learners to appear for the Associate Big Data Analyst (ABDA) Certification from the Data Science Council of America (DASCA). The ABDA™ by DASCA is the world’s most credible 3rd–party, vendor–neutral certification for young business–school learners and those graduating with majors like Statistics, Applied Mathematics, and Economics, dreaming of making exciting careers in Big Data. You can trust the internationally accepted and recognized ABDA™ Credential to prove best, your proficiency, potential, and promise for Big Data Analytics.

The professional certification from DASCA is an edge to your Data Science career (like any other certification from IBM, Oracle, etc) and is highly regarded as a ticket to vouch the data science knowledge and skills that an individual claims to possess. So, any Data Science company’s trust on the certified individual makes them stand out in the crowd.

IBM, being a pioneer in the information systems and data science, has endorsed GradValley’s curriculum and also provide training, again authenticating the knowledge and skill-set a data science professional acquires by being a GradValley learner. The Program certificate will also carry IBM logo as an added proof.

GradValley provides guaranteed placement assistance for the candidates who have an excellent track record of academics, data science knowledge, application of their skills, and the attendance during the learning period with GradValley along with successful completion of the internship or the Capstone Project.

Be it virtual or direct, GradValley offers live, instructor-led sessions that are recorded and added to the library for the learner's future references during the program.

All of our live calssroom sessions are recorded and added to our resource library in our comprehensive Learning Management System (LMS) specifically for our registered learners for future reference during the program. LMS can be accessed over the internet 24/7 for the video lectures and course content. Mobile application is also available for accessing the LMS on-the-go.

Our program curriculum is well-crafted to strengthen foundational concepts of mathematics, statistics, programming, machine learning, analytics, visualization, and big data. Learning is mainly through hands-on training with the application of concepts using relevant tools and techniques.

Our industry-standard curriculum is framed to bridge both academic and industrial needs with an innovative, andragogic approach. The learners are self-driven to learn through problem solving exercises, cracking case studies, developing use cases, and other innovative approaches.

Be it an internship or a Capstone Project, GradValley strongly believes in learning by solving real-world problems. The learner's involvement in such projects help them experience holistic learning and experiment on innovative solutions for the pain points of the industry.

GradValley's R&D wing is highly associated with industry research projects in various domains offering many data science projects acting as the Center of Excellence (CoE) for GradValley. GradValley and the Innovation Smart Lab gp hand-in-hand to bridge the gap between academia and industry.

GradValley has tied up with many experienced data science professionals and researchers within the industry and corporations to guide and mentor our learners during the course of the program.

Workshops on resume preparation, mock interview sessions, language proficiency, communication and soft skills, how to be a continuous learner, and many more in line are conducted for the learners to experience.

Lectures, seminars, and workshops given by data science professionals from the industry will be organised to help learners with understanding what is happening outside the classroom in the data science and analytics world within various business domains like retail, healthcare, manufacturing, finance, etc.

Learning Coverage

The program aims to develop the learners’ basic understanding of the data analytics with big data tools-like R and Python-enabling them to provide suggestions and recommendations in decision-making within a business.

The program covers six different courses with around 145 hours of instructor-led classes, inclusive of course delivery, industry sessions like guest lectures, seminars, workshops, live projects, and career guidenace sessions.

Course Learning Outcomes

1

Introduction to
Data Science and
Big Data Analytics

This course “Introduction to Data Science and Big Data Analytics” enables the learners to:

Explore about the evolution of the Data Science and Big Data.

Know how the data is being generated exponentially in businesses.

Understand on how the business data generated is to be handled with Big Data technologies, governed and secured.

2

Foundational
Statistics and EDA

At the end of this course, the learners will be able to:

Apply the concepts of averaging, variations, skewness, and kurtosis in solving a business problem.

Discuss about the formal hypothesis testing and relate testing procedures to estimation via confidence intervals.

Predict one variable from the other variables and determine the line of best fit as well as apply multiple regression and multiple correlation techniques.

3

Analysing Data
through R and Python

This course enables the learners to:

Understand the fundamentals of programming using the Data Analytic tools.

Perform analytical modelling with data.

Create graphs using the data for decision makers using various libraries in both two and three-dimensional spaces.

Mine the information hidden within the data to aid decision making in the businesses problems.

4

Understanding
Machine Learning

At the end of this course, the learners will be able to:

Handle, clean, and prep the data.

Engineer the features.

Develop a model with the data with the right supervised and unsupervised algorithms through development, validation, and testing.

Overcoming the challenges like over-fitting and under-fitting the data.

5

Big Data Ecosystems
and Frameworks
with Hadoop

This course makes the learners gain expertise in:

Map Reduce and its qualities.

Hadoop data storage principles.

Big Data tools like Hive, Pig, Yarn, Scoop, Oozie, Storm, Flume.

Creating solutions using analytics in the Big Data environment.

6

Data Visualization

This course will make the learners:

Handle, explore, and visualize the data of various business domains through business intelligence tools like Tableau and QlikView.

Present the data in the graphical format to help the business users to take decisions by discovering new patterns.

Pedagogy

Our Pedagogy hexplains how we implement the instruction of our well-framed curriculum. It involves three different phases: Foundation, Specialization and Realization.

Foundation Phase

During this first and foremost phase of the program, the foundational concepts of statistics, programming, data science and analytics will be covered.

Specialization Phase

In the second phase of the program, core concepts of machine learning, big data frameworks, and data visualizations are covered. In this phase, the learners can test their understanding through problem solving exercises, case studies, etc.

Realization phase

This is the most important phase because the learners will be applying their knowledge in data science for a real-life problem and try to figure out the best alternative solution.

Industry Lectures

In addition to the course content, GradValley will be offering seminars, guest lectures, and workshops by data science professionals from the industry in parallel to the course curriculum for a better understanding among the learner community. It also aids in bridging the gap between the academia and industry.

Mentoring Through Industry Experts

To ensure the overall development of our learners, we provide regular mentoring sessions. Each and every learner enrolled for the program will be assigned to a data science professional in the corporate and contact sessions will be arranged on a one-on-one basis.

Capstone Project/Internship

As a part of the program completion, the learners have to complete the capstone project or do an internship.

 Durga Gadiraju

Durga Gadiraju

Founder & Technology Evangelist, IT Versity LLC

Koushik M L N

Koushik M L N

Head of Engineering IT Versity LLC

Maitreyi Mandal

Maitreyi Mandal

Lead Data Scientist, Freshworks Inc

Mohan Raj

Mohan Raj

Product Specialist at Informatica

Sudalai Rajkumar

Sudalai Rajkumar

Lead Data Scientist, Freshworks Inc

Tanmay Bakshi

Tanmay Bakshi

Keynote & TEDx speaker, Algorithmist, Author, Watson & Cognitive Developer, YouTuber

Vinay Chandragiri

Vinay Chandragiri

Data Scientist IIT Guwahati

Program Prerequisites:

Should have completed or pursuing (final year) Bachelor’s/Master’s Degree in Computer Science/Mathematics/Statistics/Applied Sciences/Economics/Social Sciences/Engineering/Technology/Finance/Management/other related disciplines.

Work experience is not mandatory.

Familiarity of the following concepts will be helpful during the program:

Basic concepts of statistics and math/p>

Comfortable with handling databases

Basic understanding of how big data is used in business

To apply for the Associate Program in Big Data Analytics, click the apply now button.

Admission Process:

Interested candidates need to apply through our online application form.

Interested and eligible candidates will go through the GradValley Entrance Test.

Based on the aggregate marks scored, the candidates will be shortlisted.

TThe selected candidates will be notified within two weeks after appearing for the entrance test with the conditional admission offer letter.

After receiving the conditional admission offer letter, the registration fees should be paid on the due date mentioned in the conditional admission offer letter sent to confirm the seat.

Please note that though the candidate has been selected for the program, if they have not confirmed the admission by paying the registration fees within the due date the candidature becomes invalid as the admission will be closed upon reaching the required number of candidates.

For more information, reach us @ +91 98406-68844

What should I expect from programs in Big Data Analytics?

You can expect to learn the basic science of how data can be understood, visualized, analysed, reported, and utilized for businesses. It is a well-rounded program with hands-on lab activities with possible running use case/s. We make sure to support, assist, and organize mentoring sessions from industry experts through guest lectures, seminars, internships, projects, etc.

What are the topics to be covered during the program?

Please visit the link to know the modules to be covered during the program. To check out the entire curriculum, please reach us @ +91 98406 68844

What kind of job roles will I be eligible for after successful completion of the Big Data Analytics program?

After successful completion of the program, the candidates can apply for the job roles like Junior Data Analyst, (Big) Data Analyst, Data Scientist, Analytics Manager, Business Analyst etc. based on their domain experience and analytics skill-set.

How can we differentiate a data scientist from a data analyst?

Data Scientist Data Analyst
Predict the future based on past patterns Curates meaningful insights from data
Estimates the unknown Looks at the known from new perspectives
Expected to generate their own questions Finds answers to a given set of questions.
Finds business problems Addresses business problems
Need strong business acumen and data visualization skills Does not need a strong business acumen and data visualization skill
Examines data from multiple disconnected sources Looks at data from a single source
Build statistical models and has to be well-versed in ML Does not build statistical models and is not required to be well-versed in ML