Course Content
Follow These, To Crack Interviews
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Tell Your Story, Not Just Your Skills
00:00
Master the STAR Method for Behavioral Questions
00:00
Highlight how you can solve specific problems during the interview
00:00
Emphasize how your skills will contribute to business goals
00:00
Practice Mock Interviews
00:00
Show Curiosity with Strategic Questions
00:00
Turn Weaknesses into Strengths
00:00
Prepare for the “Why You?” Question
00:00
Practice Whiteboard Thinking for Problem-Solving Rounds
00:00
Prepare for Technical Depth(To Explain Non-Tech Audience)
00:00
Showcase Soft Skills During Technical Interviews
00:00
Demonstrate a Growth Mindset
00:00
Prepare a Closing Statement
00:00
Foundation and Core Concepts
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Introduction to the workshop and Understanding the Learners
00:00
Understanding the hiring process for Data Science roles (resume screening, technical rounds, case studies, HR)
00:00
Key tips for 10X impact during interviews
00:00
Technical Fundamentals and Core Concepts
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Statistics and Probability Interview Preparation with Projects
00:00
Linear Algebra for Data Science
00:00
Types of ML (Supervised, Unsupervised, Reinforcement Learning)
00:00
Key ML algorithms Interview Prep with Theory and STAR Method
00:00
Python & SQL Interview Screening Preparation
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Data manipulation with Pandas with Interviews Task
00:00
Data visualisation with Matplotlib/Seaborn/Plotly for Interviews Task
00:00
Coding challenges based on real-world scenarios
00:00
Applied Statistics, Decision Making Interview Tasks
00:00
SQL for Data Science Interview Prep
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Writing efficient queries, joins, groupings, and window functions
00:00
Common interview SQL questions
00:00
Case Study Analysis in Data Science Interviews
0/4
Real-world business problem breakdown
00:00
Building an end-to-end pipeline: Data cleaning, feature engineering, model building & Deployment
00:00
Overfitting, underfitting, and model evaluation metrics
00:00
How to Explain Model Interpretability
00:00
Soft Skills for Interview Success
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STAR method for answering behavioral questions
00:00
Common pitfalls and strategies for communication in interviews
00:00
Mock behavioral Q&A
00:00
Advanced Machine Learning and Deep Learning Concepts
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Feature Selection and Dimensionality Reduction
00:00
Neural Networks, CNNs, RNNs, Transformers overview
00:00
Practical Implementation
00:00
MLOps and Deployment
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Introduction to MLOps principles
00:00
CI/CD pipelines for Data Science models
00:00
Deploying models
00:00
Mock Technical Interviews
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Simulated coding rounds for Python and SQL
00:00
Machine learning problem-solving in a live environment
00:00
Design Planning – for Data Science Projects
00:00
Resume Building and Job Search Strategy
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Crafting ATS-friendly resumes with impactful keywords
00:00
Strategies for networking and building a personal brand on LinkedIn
00:00
Q&A session
00:00
Resume, Mock Interviews, Documents, Certificates & Job Portal Optimization
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Data Science Resume Optimization to Increase your Interview Calls
LinkedIn & Job Portal Optimization To Increase Interview Calls
Mock Interview: How To Introduce about your self?
Mock Interview: How to Explain your Project End-to-End?
How to explain Notice period, Documentation, Certificates, e.t.c
Mindmaps for Interview Revision
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Data Science Interview Revision Mindmap
Statistics Mindmap
Python Learning Mindmap
SQL Interview Mindmap
Machine Learning Accuracy Improving Techniques Mindmap
Machine Learning Math Mindmap
Real-Time Project Procedure Mindmap
ML Framework Mindmap
Power BI Interview Mindmap
NLP Interview Mindmap
Deep Learning Interview Mindmap
Advanced Data Structures and Algorithms[Self-Study]
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Introduction to DSA
Non-Primitive Data Structures
Non-Linear Data Structures
What is an Algorithm
Theory & Code Implementation of Linked List
Stacks & Queues Assignment
Coding Stack Data Structure
Coding Queue Data Structure
Tree Data Structures
Types of Tree Data Structure
Tree Traversal
BFS Traversal
Bubble Sort Theory
Bubble Sort Code Implementation
Selection Sort Theory
Selection Sort Code Implementation
Insertion Sort Theory
Insertion Sort Code Implementation
Merge Sort Theory
Merge Sort Code Implementation
Quick, Merge Sort Performance
Quick Sort Theory
Linear Search & Bisection Search
Conquer Data Science Interviews: 16-Hours

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