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Full-Stack Data Science Bootcamp

  • SQL, Python, Statistics

    There are fundamental skills that you need to acquire to be a good data scientist. This includes being able to retrieve, analyse, interpret, present and organize data. In this phase, the bootcamp content is designed to make sure that you master these fundamental skills: SQL, the standard language to communicate with database, Python, the most widely used programming language (one third of new software development uses this language), and Statistics.

    This bootcamp provides theoretical knowledge and homeworks, hands-on office hours where you can master the curriculum. The bootcamp schedule lets you acquire theory, and practice what you’ve learned with office hours.

  • Fundamentals of Machine Learning

    In the second phase of the bootcamp, you will learn data preprocessing such as handling missing values, standardization, normalization, and feature scaling. You will learn the popular machine learning concepts such as clustering, classification, regression, ensembling, and dimensionality reduction. You will get your hands on training data, to discover potentially predictive pattern. You will learn how to train algorithms with training data, and to predict the outcome for future datasets. You will also learn about cross-validation, a technique to prevent overtraining.

    In this phase you’ll utilize modern data science techniques with industry experts. This phase will help you get started in data science career and become ready to machine learning applications.

  • Natural Language Processing Phase – Cloud (Spark, AWS, Azure)

    NLP is one of the most challenging and revolutionary areas of AI. This phase will start teaching you the basics of NLP, and move to the main concepts. You will learn the latest frameworks including NLTK, TextBlob, Gensim, SpaCy, Keras, and Tensorflow. With that, you’ll get comfortable with text representation / processing / understanding, choosing efficient ML algorithms for NLP, embedding spaces and word vectors. In addition to machine learning, you will get a chance to practice a bit of Deep Learning in the context of NLP.

  • Landing a Job Phase (Projects, resume building, mock interviews)

    You are being acquired many theoretical knowledge and had a chance to apply those with many homework and practices. Now, it’s time to challenge yourself with real projects and see how data science is being applied in the industry. In this phase, you are working on real-time projects where you will master the whole bootcamp content. You will be placed in the work groups and have a mentor. Your mentor will track your progress, advice for your weaknesses and guide you to land your dream job. You will also build a portfolio which is necessary for your job placement. With the help of resume building and mock interviews, you will become an open target for the headhunters.



This bootcamp’s 15 weeks of schedule is flexible. The weekly schedules consist of 2 project sessions with mentors and workgroups and 1 review session.

November 9 - February 22

15 weeks - online

Application deadline November 9

SQL, Python, Statistics Phase

week 1,2,3

Fundamentals of Machine Learning Phase

week 4,5,6,7

Natural Language Processing Phase – Cloud

week 8,9

Landing a Job Phase

week 10-15

There are fundamental skills that you need to acquire to be a good data scientist. This includes being able to retrieve, analyze, interpret, present, and organize data. In this phase, the bootcamp content is designed to make sure that you master these fundamental skills: SQL, the standard language to communicate with a database, Python, the most widely used programming language (one-third of new software development uses this language), and Statistics.

 

This bootcamp provides theoretical knowledge and homework, hands-on office hours where you can master the curriculum. The bootcamp schedule lets you acquire theory, and practice what you’ve learned with office hours.

In the second phase of the bootcamp, you will learn data preprocessing such as handling missing values, standardization, normalization, and feature scaling. You will learn popular machine learning concepts such as clustering, classification, regression, ensembling, and dimensionality reduction. You will get your hands on training data, to discover potentially predictive patterns. You will learn how to train algorithms with training data, and to predict the outcome for future datasets. You will also learn about cross-validation, a technique to prevent overtraining.

 

In this phase, you’ll utilize modern data science techniques with industry experts. This phase will help you get started in a data science career and become ready to machine learning applications.

NLP  is one of the most challenging and revolutionary areas of AI. This phase will start teaching you the basics of NLP, and move to the main concepts. You will learn the latest frameworks including NLTK, TextBlob, Gensim, SpaCy, Keras, and Tensorflow. With that, you’ll get comfortable with text representation/processing/understanding, choosing efficient ML algorithms for NLP, embedding spaces and word vectors. In addition to machine learning, you will get a chance to practice a bit of Deep Learning in the context of NLP.

You are being acquired much theoretical knowledge and had a chance to apply those with many homework and practices. Now, it’s time to challenge yourself with real projects and see how data science is being applied in the industry. In this phase, you are working on real-time projects where you will master the whole bootcamp content. You will be placed in the workgroups and have a mentor. Your mentor will track your progress, advice for your weaknesses, and guide you to land your dream job. You will also build a portfolio that is necessary for your job placement. With the help of resume building and mock interviews, you will become an open target for the headhunters.

  • SQL, Python, Statistics Phase

    There are fundamental skills that you need to acquire to be a good data scientist. This includes being able to retrieve, analyze, interpret, present, and organize data. In this phase, the bootcamp content is designed to make sure that you master these fundamental skills: SQL, the standard language to communicate with a database, Python, the most widely used programming language (one-third of new software development uses this language), and Statistics.

     

    This bootcamp provides theoretical knowledge and homework, hands-on office hours where you can master the curriculum. The bootcamp schedule lets you acquire theory, and practice what you’ve learned with office hours.

  • Fundamentals of Machine Learning Phase

    In the second phase of the bootcamp, you will learn data preprocessing such as handling missing values, standardization, normalization, and feature scaling. You will learn popular machine learning concepts such as clustering, classification, regression, ensembling, and dimensionality reduction. You will get your hands on training data, to discover potentially predictive patterns. You will learn how to train algorithms with training data, and to predict the outcome for future datasets. You will also learn about cross-validation, a technique to prevent overtraining.

     

    In this phase, you’ll utilize modern data science techniques with industry experts. This phase will help you get started in a data science career and become ready to machine learning applications.

  • Natural Language Processing Phase – Cloud

    NLP  is one of the most challenging and revolutionary areas of AI. This phase will start teaching you the basics of NLP, and move to the main concepts. You will learn the latest frameworks including NLTK, TextBlob, Gensim, SpaCy, Keras, and Tensorflow. With that, you’ll get comfortable with text representation/processing/understanding, choosing efficient ML algorithms for NLP, embedding spaces and word vectors. In addition to machine learning, you will get a chance to practice a bit of Deep Learning in the context of NLP.

  • Landing a Job Phase

    You are being acquired much theoretical knowledge and had a chance to apply those with many homework and practices. Now, it’s time to challenge yourself with real projects and see how data science is being applied in the industry. In this phase, you are working on real-time projects where you will master the whole bootcamp content. You will be placed in the workgroups and have a mentor. Your mentor will track your progress, advice for your weaknesses, and guide you to land your dream job. You will also build a portfolio that is necessary for your job placement. With the help of resume building and mock interviews, you will become an open target for the headhunters.

Are you feeling not ready to start? Don't worry!

Get started for free with our mini bootcamps. Our mini bootcamps will teach you Python basics and machine learning algorithms. During these mini bootcamps, you will explore a hands-on experience that ignites your enthusiasm, and gain the confidence to learn data science.

Meet Your Mentors

Murat Baday

Murat Baday is the co-founder and Chief Strategy Officer at Smartlens – a clinical stage medical technology company that envisages prevention of blindness due to Glaucoma. He is passionate about everything related to data science, education and entrepreneurship. Under Murat’s supervision, Smartlens has developed the first electronic-free contact lens, which accurately measures eye pressure and its fluctuations. Murat obtained his Ph.D. in Biophysics from University of Illinois at Urbana-Champaign and his M.S. in Physics from University of Pittsburgh. He completed his B.S. in Physics from Bogazici University.

M. Enes Sen
Yasin Ceran

Yasin Ceran is passionate about all things data and holds a vast experience in data analysis, mathematical modeling and Apache Spark, and in SQL, Python and R. Since 2013, he has been working at Santa Clara University as an assistant professor in the Information Systems and Analytics department (earlier Operations Management and Information Systems department), teaching applied machine learning, software platforms, and computer networks. He holds extensive experience in working on various data intense business platforms. Some of the notable projects he contributed to include the development of a predictive inventory allocation algorithm for Blockbuster with regard to the relationship between sales and customer reviews. Later, he created a mathematical model to capture the decision-making process of online customers based on their collection of data from different sources on the Web. He also developed a probabilistic model to detect churning users for Stumbleupon.com. Yasin is the proposer of an algorithm that helps to reduce bias in electronic formats. His present role involves working on optical DNA sequencing based on actionable big data.

Mudasser Shaik

Mr. Shaik is highly motivated Senior BigData / DataScience Engineer in industry. He has been sharing his passion for Data for nearly a decade now. He is passionate about Distributed Machine Learning and the intersection of Big Data & Data Science (AI/ML/DL). He possesses extensive working experience in designing and building Distributed Pipelines, Streaming Analytics, Scalable Machine learning Models and solving Data Quality Issues. He presently working as Senior Big Data Engineer at Ultimate Software, where he is responsible for developing, maintaining and evaluating Big data solutions for Data Science Teams. He is responsible for architectural overview of Data Platforms, Data Governance and building DataLake for Data Science team. He is also in charge of building large-scale data processing systems using Apache Spark, Kafka, Hadoop Eco system with security and compliance enabled across multiple Data centers and GCP.

Ebru Akbas

Ebru Akbas is a data scientist and co-founder of Magnimind Academy. Akbas obtained her Master degree in New York from City University of New York and her undergraduate degree in computer engineering from Turkey. Ebru Akbas also is working as a data scientist on a cancer research project at Stanford Radiology department.

Paul Starrett

Paul Starrett is a licensed private investigator (CA, IL) and attorney (CA) specializing in high-profile investigations and legal consulting especially where electronic data is central. He is founder and CEO of Starrett Consulting, Inc., a full-service investigations firm that leverages API’s from open-source and commercial data-science applications to analyze structured and unstructured data. He is former General Counsel and Chief Global Risk Officer of an international, publicly-held data management corporation heading their global legal, operations and risk-management groups. His 25 year career began in law enforcement and corporate security and later progressed into information-security engineering, electronic discovery and information management. Paul’s education includes a Master of Science in Predictive Analytics from Northwestern University and a Master of Laws (LL.M.) in Taxation from Golden Gate University. He is also a Certified Fraud Examiner (CFE) and EnCase Certified Computer Forensics Examiner (EnCE).

Arafat Mokhtar

Arafat is a graduate student from Tel Aviv University and then continued his education at Hamburg and Colorado. Now, he is working as a Data Scientist at Stanford University. He has 25 years of data experience and teaching. Arafat Mokhtar is a Business Intelligence Engineer at Stanford School of Medicine, who develop Python code for data collections, validation, cleansing, and analytics to provide actionable data insights. Arafat holds a Ph.D. in Physics. His work expands into fields of research, finance, and operation. Arafat has several years of Python, R, SQL, and Tableau work experience.

Dane Miller

Over the past five years, Dane has been working as a science educator in Palo Alto. Where he teaches science, builds hands-on science programs, and mentors students. His passion is science! Prior to being a science educator Dane worked in the field of paleontology for 10 years. He worked on several paleontology projects focused on data collections and curatorial work with the Denver Museum of Nature and Science and the Smithsonian National Museum of Natural History. In 2010, he was part of a major paleontological discovery in the state of Colorado’s history. Working with a team from the Denver Museum of Nature and Science, we unearthed a treasure trove of fossils 30,000+ vertebrate fossils from mammoths, mastodons, camels, deer, bison, bear, and many other animals. The discovery of this ice age ecosystem drew news coverage from the New York Times, NOVA, and National Geographic. The fossil conifer cones from this discovery led to his graduate research at the University of Wyoming, Laramie.

Murali Mandayam

Mr. Murali Mandayam is a seasoned professional and a long time Silicon Valley resident having worked with companies such as Intellisync and Nokia. Murali jumped into the data science pool recently and has modeled automobile driver and stock market data using classification and time-series modeling.

Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar is a Sr. Principal Engineer at ON Semiconductor, where he works on state-of-the-art semiconductor technology development and applies AI/ML techniques for design automation, AI-centered hardware development, and predictive analytics. He contributes regularly to publications such as KDnuggets and TDS on diverse topics related to data science and machine learning. He has authored data science books and contributes to open source software. Tirthajyoti holds a Ph.D. in EE and is working on an M.S. degree in Computational Data Analytics.

Abraham Kang

Mr. Abraham Kang is fascinated with the nuanced details associated with programming languages and their associated APIs. Kang has a B.S. from Cornell University. He currently works for Samsung as a Senior Director Software helping to drive security and development in Samsung. Prior to joining Samsung, he worked as Principal Security Researcher for HP in their Software Security Research group.
Abraham is focused on AI/ML, application, framework, blockchain smart contracts, intelligent assistants, and mobile security and has presented his findings at Black Hat USA, DEFCON, OWASP AppSec USA, RSA, and BSIDES.







Outcomes : Who Has Hired Our Data Science Students


Our bootcamp prepares grand to work at todays most impressive companies

Outcomes : Who Has Hired Our Data Science Students


Our bootcamp prepares grand to work at todays most impressive companies
Testimonials

what they say about us

Bootcamp Info Session

Curious about the Magnimind bootcamp? Join us on Wednesday, May 13 from 6pm - 7pm PT for a bootcamp info session!

SCHEDULE A CALL

Come explore one of our campuses and meet our team! We’d love to show you around.

Take The First Step

Ready to take the first step toward becoming a Data Scientist? Start your application.

Chat With Admissions

Have more questions about our data science program? Our admissions team is happy to speak with you.

  • What are the outcomes of this program?

    This program is designed to meet the specific needs of applicants and help launch their data science careers with hands-on projects. Most students approach us with a good theoretical background in data science but don’t have good hands-on experience. They lack qualified mentors and don’t have an idea of industry standards. We provide education to fill the gap and provide the best mentorship program in silicon valley where each student will interact with at least three mentors.
    Our students graduate from our 1on1 Machine Learning Project Mentorship Bootcamp with enough skillsets that will help them to find industry placements in related positions. Our students are trained for technical interviews, resume preparations, and necessary hands ‘on requirements. With these checkmarks on your profile, you will be a strong candidate in the job market.

  • Is this program job guaranteed?

    Magnimind Academy 1on1 Project Mentorship Bootcamp is specifically designed to place its students. Not only during the bootcamp timeline but also after graduating from the program, Magnimind helps its students with recruiter connections and job placement studies. The program is specifically not a job guaranteed, but it’s the only purpose is to place its graduates in the industry.

  • What are the payment alternatives?

    Currently, the only payment alternative for the program is the up-front payment.

  • Can I apply for a scholarship?

    With the slogan of providing education for everyone, Magnimind offers scholarship opportunities to the candidates with low income. You need to apply with the Magnimind Academy Scholarship Form to be considered for competence. Our admission team informs applicants before the bootcamp starts with their eligibility report.

  • How old is this program?

    Our project-based learning program is not only new for Magnimind Academy, but also a pioneer for the whole technical learning industry. Currently, Magnimind is serving with its project based learning bootcamp for the last 1 year. However, we have been offering different bootcamps in the last two years.

  • Do you have any support after the program?

    Magnimind Academy is organizing its program to reach out to your dream position in the industry. The guidance of Magnimind continues with career coaching, recruiter connections, and internships after the bootcamp timeline. We also provide bonus educations for some of our students.

  • How Magnimind helps me to get hired?

    The reputation of the 1on1 Project Mentorship Bootcamp Curriculum is the ultimate source that will help you to get hired. Other than the quality of the education, the Magnimind recruiter network will take you one step above between your competitors. Compared to other bootcamps, Mangimid mentorship bootcamp offers in-depth industry-based training where you only focus on the industry’s standard hands-on education with real-life projects. We provide a variety of pieces of training that cover every aspect of job placements.

  • Will I work with a team during the program?

    Every student will work with a workgroup on each project. This experience will help you to taste a team-based working environment. You will have the support of your workgroup where you got stuck on a project as well.

  • How does the mentor meetings system work?

    We determine meeting times based on your workgroup’s and mentor’s availability. Since the program is part-time, most of the meetings are convenient with non-business hours. You also have the chance to organize one-on-one meetings with your mentor.

  • Are they all real projects?

    The projects of the bootcamp are from real technology companies and/or Kaggle projects with real datasets.

  • Will I have specific tasks while working on the projects?

    You will have an end-to-end experience in different sub-topics of data science while working on the projects. You will collect data, build models, and get actionable insights with your hands’ on experience during the bootcamp.

  • Can I choose a project field that I’m interested in?

    The projects of the bootcamp are determined by Magnimind Academic Staff and assigned to you. Magnimind admissions team makes sure to understand your readiness to start these projects during the assessment process. However, the Magnimind team would be able to guide you in your project of interest after the bootcamp.

  • Can I continue working on these projects after the bootcamp?

    You can continue to your learning and hands ‘on experience with the internship opportunities from Magnimind network. Magnimind meets you with startups and technology companies where you can find paid or unpaid internship opportunities and gain more experience before landing your first position. However, the internship is based on your progress during the regular program.

  • What is the weekly time commitment this program requires?

    Magnimind Academy - 1on1 Project/Mentorship Bootcamp is an intense 10-week program designed to meet the specific needs of applicants and help launch their data science career with hands-on projects. Each student is expected to work 15 hours a week on their projects and also to meet with mentors to benefit from their experience.

  • What if I can’t finish the projects on time?

    One big challenge for you is the timing during the bootcamp. You will learn how to meet with project deadlines and become ready for timelines of the industry projects. Our mentors help you to catch up on your workgroup and finish the projects on time. Our program managers also assess your progress with weekly bootcamp overview meetings.

  • Are three weeks enough to finish a project?

    On each project, you will have a compressed and intense experience where you will learn different aspects of working on a data science project. The projects also cover different sub-topics of data science during the bootcamp. At the end of the program, you will find the courage to work on many fields of data science with the experience that you gained from these projects.