This 15-week bootcamp in data science education includes theoretical and practical applications. Each week, students have 7 hours of lectures and 3 office hours. During office hours, students can practice what they have learned and work on projects that have been assigned to them. Instructors are available during these office hours to answer questions and provide guidance.
The course curriculum includes job interview studies and CV preparation guidance. Students will attend several meetup events about job interview questions during the course, organized by MBecome a Full-Stack Data Scientist in 15 weeks.
Explore 4 phased Full-Stack Data Science Bootcamp:
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, 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 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 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. agnimind Academy.