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Data Science Bootcamp
This data science Bootcamp is an intensive training course that aims to equip participants with the skills and knowledge
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12 WEEKS
15 MODULES
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About the Bootcamp
This data science Bootcamp is an intensive training course that aims to equip participants with the skills and knowledge needed to pursue a career in the field of data science.
This bootcamp typically spans several weeks and covers a range of topics such as statistics, NLP, machine learning, data visualization, and programming languages such as Python and R.
During the boot camp, participants will engage in hands-on exercises, projects, and assignments to gain practical experience in working with real-world data sets. They also receive mentorship and guidance from experienced data science professionals and network with peers in the field.
PROGRAM DETAILS
Program Duration
The duration of the Program will be delivered 12 Weeks. There will be 2 classes per week
Classes Details
Every Saturday: 10:00 AM - 12:00 PM Every Sunday: 10:00 AM - 12:00 PM
Program Fee
Please contact our team for Pricing, ISA, Scholarships and Financial Aid available.
Program Curriculum
Our curriculum development team analyses the 100's of job postings in the local and international markets, followed by a discussion with hiring managers to keep the focus on the area more relevant to you and companies.
Modules
MODULE 1 : Introduction to the course
A general introduction to the course covering all the important aspects that you should know when you are in the Data Science industry. This module will be a basic module that will help you in framing your analytical perspective.
Inside the Module:
1. What is Data Science?
2. Need for Data Science in today’s Industry
3. Introduction to Problem-Solving
4. Framing the problem
5. Analyzing the problem
6. Implementing the problem with a proper solution
7. Different problem-solving frameworks to adopt
MODULE 2 : Python for Data Science [PRE-RECORDED]
Python is one of the most important language when it comes to Data Science.
Due to rich python libraries data analysis tasks become quite easy and interesting. So
in this module we are going to start with Python for Data Science where all the important needful concepts will be covered in detail.
Inside the Module:
1. Basics of Python
2. Data Structures in Python
3. Introduction to Problem-Solving
4. Control Structures (if, if-Else, elseif, nested if-else, etc.)
5. Loops in Python
6. Functions
7. Object-Oriented Programming
8. Exception Handling and Database Programming
MODULE 3 : Statistics for Data Analytics
Data Science is all about statistical analysis and learning. In this module, we are going to learn about different statistical concepts which are not important for Data Science and also from an interview point of view.
Inside the Module:
3.1) Introduction to Statistics
1) Type of statistics
2) Type of Data
3) Different Sampling Techniques
4) Measures of Central Tendency (Mean, Median, Mode)
5) Measures of Dispersion (Variance and Standard Deviation)
3.2) Intermediate Statistics
1) Introduction to Probability
2) Permutations and Combinations
3) Conditional Probability and Bayes Theorem
4) Introduction to Gaussian Distribution and properties
5) Central Limit Theorem
6) Covariance and Correlation
7) Pearson And Spearman Rank Correlation
8) Binomial Distribution
9) Bernoulli Distribution
10) Poisson Distribution
3.3) Advanced Statistics
1) Introduction to Confidence Intervals
2) Z-test and t-test
3) Hypothesis Testing
4) ANNOVA
5) AB Testing
Note: All the concepts of the module will be covered theoretically and practically as well.
MODULE 4 : Data Wrangling
Once you have the core skill of programming covered – dip your feet in the nitty – gritties of working with data by learning how to wrangle and visualize them.
Inside the Module:
1. Reading CSV, JSON, XML, & HTML files usi HTML files using Python
2. NumPy & Pandas
3. Relational databases and data manipulation
4. Scipy libraries
5. Loading, cleaning, transforming, merging & reshaping data
MODULE 5: RDBMS and SQL for Data Science
Data is stored in Databases and databases can be of various types. But when we talk
about Databases what first comes in our mind is SQL (Structured Query Language). Even in most of the data analytics job description SQL is always mentioned.
Inside the Module:
5.1) SQL
1) Introduction to Databases
2) What is SQL?
3) Introduction to Schemas and its types
4) Introduction to Relational and Non-Relational Schemas
5) Different integrity constraints in SQL
6) Details on different keys in SQL
7) Data Definition Language (DDL)
8) Data Manipulation Language (DML)
9) Data Control Language (DCL) 10) Joins in SQL
11) Data import and export
12) Functions in SQL
13) Nested Queries
14) Views in SQL
15) Stored Procedures
16) Window Functions
17) Python connectivity for SQL
MODULE 6 : Machine Learning
Machines have increased the ability to interpret large volumes of complex data. Combine aspects of Computer science with statistics to formulate algorithms that helps machines draw insights from structured and unstructured data.
Inside the Module:
1. Introduction to Machine Learning
2. Building models using the below algorithms
3. Evaluation Metrics
4. Regression Algorithms
5. Classification Algorithms
6. Clustering Algorithms
7. Association Rule Learning
8. Ensemble Techniques
9. Feature Engineering
10. Feature Scaling
11. Feature Selection
12. Time Series Analysis
13. Anomaly and Outlier Detection
MODULE 7 : Natural Language Processing
NLP helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.
- Introduction to Machine Learning
- Building models using the below algorithms
- Evaluation Metrics
- Regression Algorithms
- Classification Algorithms
- Clustering Algorithms
- Association Rule Learning
- Ensemble Techniques
- Feature Engineering
- Feature Scaling
- Feature Selection
- Time Series Analysis
- Anomaly and Outlier Detection
MODULE 8 : Deep Learning & Computer Vision
Go beyond superficial analysis of data by learning how to interpret them deeply. Use
deep learning nets to uncover hidden structures in even unlabeled and unstructured data.
Inside the Module:
1. Basics of Neural Networks
2.Linear Algebra
3.Implementation of neural networks
4.Basics of TensorFlow
5.CNNs, RNNs, ANNs
6.Generative models
7.Encoder/Decoders, Seq-to-Seq models
8.Transfer Learning
9.Pre-Trained Model
10.Introduction to Computer Vision
11.Basics of Object Detection
More Modules
MODULE 9 : Deploying ML Models using Flask (ON PREM), Streamlit (ON PREM) & CLOUD
Go beyond the model preparation by learning how to deploy a machine learning model using Flask, Streamlit and Cloud etc.
Inside the Module:
1.Concepts on Apache Flask, Waitress
2.Various cloud providers – AWS, Azure, GCP etc
3.How to prepare a model?
4.How to save a model?
5.Deploying on streamlit
6.Deploy a model using Apache Flask
7.Deploy a model using Cloud
MODULE 10 : DATA VISUALIZATION - Tableau & Power BI [PRE-RECORDED]
Learn the Art of Dashboarding and Data Visualization using Power BI and Tableau.
Inside the Module:
1. Introduction to both the tools
2. Connecting to files & databases
3. Data filters
4. Calculations and Parameters
5.Creating Dashboards
6. Data Blending
7.Creating superimposed graphs
Module 11: Spark and Data Engineering [BASICS]
Get prepared on the concepts of Data Engineering as an add-on feature.
Inside the Module:
1.Spark Architecture
2.Data Engineering using Spark
3.RDD, Datasets, Dataframes
4.Spark ML
Module 12: LIVE PROJECTS
We Provide 4-5 Projects during the course, and various industry level use case for practice. Students get one to one mentoring while solving various industry level projects too. Real world industry Projects and deployment in AWS and Azure Cloud.
Meet Your Mentors
We choose our mentors from global market leaders with a rigorous selection process. Our mentors keep you motivated and on track. And they ensure your success.
Data Analytics Training FAQs
Is this Bootcamp for you? Then Join Us Right Away!
Program Fee - $2000.00
Admissions are closed once the requisite number of participants enroll for the upcoming cohort.
Apply early to secure your seat.
Data Science Course Reviews
Arshdeep Kaur - Business Analyst
I attended BA Program at Knowledge Center. My experience has been incredible. Knowledge Center not only provides quality training and good value but also provides the whole support system which keeps students motivated while searching for jobs. All trainers are very approachable and supporting. Veer is an excellent mentor and he helped me understand Salesforce concepts very easily. Even after completion of the training he guided me for Certification and Interviews.Vivek is always approachable for any advice...