Data Science Bootcamp

The Data Science Career Course Is An Extensive 8-Month Program Designed To Provide Students With A Comprehensive Understanding Of Data Science Concepts And Practical Skills. The Course Covers Essential Topics Such As Python And Data Structures, Data Retrieval And Storage, Mathematical Concepts For Machine Learning, Introduction To Data Science, Advanced Data Science Techniques, Dashboarding, And Specialization In MLOPS, NLP, Computer Vision (CV), And Geospatial Data. Through Hands-On Exercises And Real-World Examples, Students Will Gain Practical Experience In Data Manipulation, Analysis, And Visualization, As Well As Advanced Machine Learning And Deep Learning Techniques. By Completing This Course, Students Will Be Well-Prepared To Pursue A Successful Career In The Data Science Field And Contribute Effectively To Data-Driven Organizations.

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Course Date

1st Oct 2023-(4th Cohort)

Duration

8 Months (240 Hrs.)

Delivery Format

Online Live

Why to Join This Course

Earn a program completion certificate from IKIGAI LAB
Utilize Ikigai’s Job Assist feature to enhance your visibility to leading hiring companies.
Attend masterclasses conducted by renowned faculty.
Engage in hands-on projects tailored to different industry sectors.

Join Our Data Science Bootcamp Today!

Ready to embark on a transformative learning journey? Don’t miss this opportunity to gain valuable skills and elevate your career. Enroll in our comprehensive course today and join a community of learners dedicated to excellence. Take the first step towards unlocking your full potential!

Course Price

₹75,000.00

Key Features

Earn a program completion certificate.
Utilize Ikigai’s Job Assist feature to enhance your visibility to leading hiring companies.
Enjoy smooth access to integrated labs for a seamless learning experience
Top-notch curriculum with integrated labs
Engage in hands-on projects tailored to different industry sectors.
Explore and apply practical tools and frameworks that can significantly enhance your work.
Conclude the program with capstone projects spanning three distinct domains

Curriculum for Data Science Bootcamp

Stage 1
Basics of Python and Data Structures

    • Python Basic Syntaxes and Data Structures
    • Python Programming Constructs and Functions
    • Developing Logic in Programming
    • Data Structures
    • I/O, Error Handling and Best Practices
    • Functional Programming (Filter, map, reduce, lambda)
    • NumPy and Pandas-Walkthrough of Major Syntaxes
    • Manipulating Databases through Pandas & Built-in Functions
    • API creation and Management using Python
    • Data visualization using python

Stage 2
Understand about how to retrieve and store data in various commonly used data sources

    • Understand about how to retrieve and store data
    • MySQL
    • Postgress
    • MongoDB

Stage 3
Interpretation and deduction level understanding of key mathematical concepts required in ML

  • Mean, Median, Mode, Standard deviation/variance, Correlation coefficient and the covariance matrix, Probability distributions (Binomial, Poisson, Normal), p-value, Bayes’ theorem
  • Sampling
  • Permutation Combination
  • Basics of integration & differentiation
  • Laplace Transform
  • Fourier Transformation
  • Solution of Matrix by Gauss’s Elimination Method
  • Defining Vector Space and Linear Combination
  • Brief on Eigen Values and Eigen Vectors

Stage 4
Introduction to Data Science

    • Data Science Life Cycle Management
    • Type of Learning- Supervised, Unsupervised, Semi-supervised, Reinforcement
    • Ability to perform EDA using SQI
    • Ability to perform EDA using python function
    • Ability to perform EDA using excel
    • Ability to draw hypothesis by analyzing EDA outcomes
    • Ability to create time-based features
    • Ability to create relationship-based features
    • Ability to create frequency-based features
    • Ability to create frequency-based algorithms
    • Ability to identify important features
    • Introduction to linear regression
    • Introduction to unsupervised learning K-means and Hierarchical
    • Introduction to unsupervised learning ability to choose optimum number of clusters and key metrics
    • Introduction to forecasting- ARIMA
    • Introduction to tree based algo- Decision tree
    • Model accuracy calculation

 

Stage 5
Data science 2
Advance regression Techniques

  • Nonlinear regression
  • Elastic net regression
  • Rasso and Ridge regression

Advance Tree based Algorithm

  • Bagging and Boosting
  • Random forest
  • Types of boosting trees -GBT, Cat boosting/Ada Boosting
  • Optimization of Tree based algorithm
  • SVM

Optimization of SVM

Introduction to Deep Learning

  • Introduction to ANN
  • Auto Encoders

Advance forecasting Techniques

  • Introduction to reinforcement learning

Stage 6

Dashboarding

  • Introduction to its architecture
  • Working with its dashboard
  • Implementation data blending and aggregation
  • Data visualization and real time analytics
  • Generated fields and special fields
  • Connecting python scripts in Tableau
  • Connections for organizing data
  • Tableau graphs, report, and calculations
  • Data Storytelling fundamentals and frameworks

Stage 7 Specialization

    • Common -MLOPS
    • NLP
    • CV
    • Geospatial

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Course Outcomes

Upon completion of this course, students will be able to:

    • Perform data manipulation, transformation, and analysis using Python and popular data libraries like NumPy and Pandas.
    • Retrieve and store data from various data sources, including relational databases and NoSQL databases.
    • Apply mathematical concepts such as probability, statistics, and linear algebra to solve data science problems.
    • Conduct exploratory data analysis (EDA) and create actionable insights from data.
    • Implement regression, decision trees, and other machine learning algorithms for predictive modeling.
    • Utilize deep learning techniques, including artificial neural networks and autoencoders, for advanced data analysis.
    • Create interactive and informative data visualizations using Tableau for effective data communication.
    • Specialize in one of the emerging areas in data science, such as MLOPS, NLP, Computer Vision (CV), or Geospatial data analysis.

FAQ for Data Science Bootcamp Course

Coding background is not required to enrol in this Data Science course. You can start from the Beginner module in which we will cover the basics of coding.

In fact, prior knowledge in Data Science or ML is also not needed. We will cover all the relevant topics from scratch.

The only prerequisite is that you should have a basic understanding of 12th grade school maths – just the basics, nothing advanced. Still, we will cover these topics in class, but some prior knowledge would be helpful

All the Maths required for understanding and implementing algorithms will be covered in this Data Science training (Probability, Statistics, Linear Algebra, Calculus, and Coordinate Geometry)

While designing the Data Science course, we did not put any limit on the duration. We included each and every concept that is important for making you a strong Data Scientist and ML Engineer. The course turned out to be 6 months long with more hands-on experience.

Notice that the course is quite rigorous; each week you will have 3 Live lectures of 2.5 hours each, homework assignments, business case project, and discussion sessions. This allows us to cover the entire depth and breadth of Data Science & Machine Learning, as much as is required for you to succeed in the role

6 data science projects will be assigned in homework and others will be covered in class.

In each case, we will focus on a particular portion that is relevant to the topic being covered that week.
Submissions from all students are posted on an internal Discussion Forum where you can read how other students have solved that particular problem.

For this course in Data Science, the admission process is quite easy. You have to follow these steps:

Step 1: Apply by filling in a simple online application form.

Step 2: Speak to our Admissions Team. Attend an aptitude test at the centre or a video interview by the faculty panel.

Step 3: An offer letter will be rolled out to selected candidates. Secure your seat by paying the admission fees.

Yes, you are eligible to enrol in this Bootcamp even if you are in the final semester of your UG degree. The classes are held for 5 working days each week in the morning/evening batches.

Data Science is a fast-growing field, and our Bootcamp is designed to be accessible to learners from all backgrounds. In fact, over 30% of our batches are made up of learners from non-tech backgrounds, so even if you don’t have a tech background, you’re still eligible and encouraged to apply to our Bootcamp.

No, it is not necessary for you to have any prior Programming knowledge before this Bootcamp. This Bootcamp will be covering all the necessary relevant tools & languages needed to launch a career in Data Science. You will be learning the basics of excel, SQL Programming, Python Programming and Tableau from scratch.

If you are a working professional, graduated in the year 2022 or after and looking to transition to the in-demand field of Data Science, this Bootcamp is ideal for you. The Bootcamp will challenge you, but the end result will be a highly rewarding career in a rapidly growing field especially if you’re currently stuck in a job with no growth opportunities or future

Yes, you are eligible for the Bootcamp even if you have a one-year gap. You just have to make sure that you comply with the eligibility requirements i.e. you should have graduated in the year 2022 or after.

Admission Process

The application process consists of three simple steps. An offer of admission will be made to the selected candidates and accepted by the candidates by paying the admission fee.
1

Submit Application 

Tell us a bit about yourself and why you want to do this program 

2

Application Review 

An admission panel will shortlist candidates based on their application 

3

Enrolment

Selected candidates can join the program by paying the admission fee 

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