Data Science Boot Camp

According to Livemint, the world is all set to see a fourth industrial revolution, and this time the technology that is going to change everything, from our lives to the kind of jobs available in the market, is Data Science.

According to Information Age, by 2020, it’s estimated that every human being is going to generate 1.7 MB of data every second!

According to Financial Experts, India by 2030 will be among the top leaders in AI-driven technologies.

Following China, which has invested around $21 billion in the AI research since 2017, the Indian Government is going to commit heavily in the Data Science domain.

Reliance Industries, one of the world’s biggest companies based in India, have invested close to 1000 crores in the AI industry and AI-based learning for schools and colleges.

There is an acute shortage of Data Science professionals right now, but with Data Science being taught in schools and colleges every working professional in the next 5 years would be expected to know Data Science.

According to the Economic Times, more than 50,000 Data Science and Machine Learning jobs are lying vacant in India due to the lack of trained professionals.

As per Glassdoor, the average salary of a Data Scientist is INR 1,040,000/year.

A skilled Data Scientist in India, with an average experience of 3–7 years, can earn up to INR 4,000,000/year in EMC² corporation.

Big Data professionals, business intelligence and business analysts, information architects, statisticians, developers who want to master machine learning, and learners who want to take up data science roles in top MNCs.

• Bachelor of Engineering degree or equivalent from an accreditation body
• Fundamental knowledge of probability and statistics
• Programming experience is a must

• Online Test containing statistics, probability, and basic programming
• Personal interview

1. Once you pass your online test and personal interview, your payment page will be unlocked
2. Once payment is done, you will be confirmed a batch
3. Comprehensive training for 6 months, every Saturday and Sunday (4 hours each day) will be provided to you
4. 24/7 lifetime technical support/query resolution is offered to all the learners
5. During the training, all 14 minor projects should be completed. This would be followed by 2 major projects, once the training is complete
6. Once the training is complete, if you meet the eligibility criteria (i.e., more than 80% attendance in the live sessions and the completion of all major and minor projects), your placement training module will be unlocked
7. Within 6 months from the starting of your placement training, you will be guaranteed a job

If no placements are given within 6 months from the starting of the placement training, you will be eligible for a 100% refund; no further questions will be asked.

In this course unit, you will learn Python programming, how to follow best coding practices, and how to use the powerful Python-based tool ecosystem.

Covered topics:

1. Python Pandas
2. Matplotlib, Seaborn – Visualization Tools in Python
3. Write clear, concise, performance-tuned, and readable code in Python using the PEP8 standard

Data wrangling or data transformation is an integral part of the Data Science process, that helps a learner perform a set of tasks to understand the data and prepare it for machine learning. This simple terminology happens to consume the maximum time of a Data Scientist, thus, extremely important, and indispensable.

Covered topics:

1. Python Pandas
2. Working with Data from a variety of file formats like .txt, structured and nested formats, such as JSON and CSV.
3. Working with database: Overview of RDBMS and NoSQL databases, and practice using SQL to query data
4. Working with API: Data collection using API from Internet

Data, when organized and presented in a readable format presents a story that is easy to grasp. Thus, in contrast with the largely misunderstood technical bias that Data scientist has to bear every day, i.e., data science pertains to working with mathematical formulas, algorithms, and analysis. A data scientist is a modern-day storyteller, but with factual information. Don’t you want to be one? Couple with visualizations, you will awe the audience.

This particular unit is designed to help you learn from the fundamental to extensive concepts of data sets and how to use those to come up with valuable insights. You will be learning to draw techniques to reveal valuable stories (insights).

Inferential Statistics is used to infer (make generalizations) when a data set (sample) is large. It uses random data obtained from a sample to describe and infer the details of the sample. It is valuable while examining a large data set – when it is inconvenient (or impossible) to examine each data. Learning this concept can help you derive the key trends, dig deeper, and create predictive models.

Covered topics:

1. Inferential statistical theory
2. Statistical significance
3. Parameter estimation
4. Hypothesis testing
5. Correlation and regression
6. Exploratory data analysis
7. A/B testing

Machine learning unites the key aspects of statistics and programming to derive useful insights and predictions. Learning ML can help you make predictions, recommendations, or generalizations to classify (find) groups and categories in complex data sets.

This module will make you understand the critical machine learning algorithms (supervised and unsupervised), usage of algorithms, what assumptions they contain, and more.

Covered Topics:

1. Scikit-learn
2. Supervised and unsupervised learning
3. Machine learning techniques: naive Bayes, Linear, and logistic regression, decision trees, clustering, support vector machines,
4. Ensemble learning with random forests and gradient boosting
5. Machine learning best practices
6. Evaluation and tuning of machine learning systems
7. Testing of the solution

With this course module, you will learn how Apache Spark enables the processing of extended data and get to know how a data scientist helps businesses cut costs, improve product quality, find new opportunities, and more. All these are done by the use of cluster computing.

Covered Topics:

1. Introduction to Big Data
2. MapReduce
3. Apache Spark & MLlib

Parallel to the online classroom training, you will work on 14 minor projects and 2 Capstone projects, which are well-designed to help you understand, assess, and apply your data science learning in real-world scenarios. You will work with your mentor who will provide you data sets and problem statements. You will implement your learning to come up with reasonable solutions to each problem statements.

Our project-based learning is an important part of our Bootcamp where our instructors train learners to master the different facets of data science. Once you have completed the course and project, you will learn about the most commonly used data science tools, packages, and algorithms needed for the data scientist role, and more.

Minor Projects:

The minor project will give you the necessary skills and hands-on experience to create static/dynamic visualizations for data and models. You will also get familiar with the current Big Data tools, Hadoop stack, learn how to use and apply them in Big Data problem statements.

The minor projects will be on

• SQL Data Manipulation
• Python Data Manipulation
• Web Scraping using Python
• Random Forest
• Logistics Regression
• Time Series
• PySpark and Spark MLlib
• Data Visualization, and more

Capstone Projects:

In the middle of the course, you will complete your first Capstone project from start to finish. The project will test your Python programming skills, and after completing it, you will be a skilled coder in Python.

With the second Capstone project, you can apply and implement your knowledge of machine learning algorithms in real-world scenarios.