ROADMAP TO
DS/ANALYST/
ML
INTERNSHIP
RIYA GUPTA
PRAYASH DASH
RISHABH KAMBOJ
MASTERCARD
ARYABHATTA RESEARCH INSTITUTE
OF OBSERVATIONAL SCIENCES
STANDARD CHARTERED
ROLE OF A
DATA SCIENTIST/ANALYST/ML INTERN
DATA SCIENTIST / ANALYST
Data processing, perform statistical analysis on data, predictive
modeling, working with database systems. Combine data analysis,
software engineering, and machine learning to create statistical models.
MACHINE LEARNING ENGINEER
Performing statistical analysis on data, researching and designing ML
algorithms, running machine learning tests, training and retraining
systems when needed, shape and build efficient self-learning applications
BUT WHAT’S THE
SYLLABUS
PROGRAMMING LANGUAGE
ML ALGORITHMS AND STATS
A data science and ML friendly
programming language like Python and R.
Strengthen understanding of basic
probab/stat concepts and ML algorithms
like linear and logistic regression, KNN,
Naive Bayes, SVM, Boosting and Bagging
algos.
BASIC DSA
To get familiar with coding and to
sharpen problem solving skills.
LEARN TO USE LIBRARIES
Get familiar with libraries like numpy,
pandas, matplotlib, seaborn, etc.
DEEP LEARNING
Learn basics of Perceptron Algorithms,
Neural Networks, Convolutional Neural
Networks, Computer Vision and
Natural Language Processing.
PERFECT RESUME
(IT DOESN’T EXIST)
THE RESUME WILL CONTAIN YOUR ACADEMIC PROFILE, SKILLS, PROJECTS, PORS,
ACHIEVEMENTS, ETC.
INCLUDE 2-3 PROJECTS OF WHICH YOU HAVE A GOOD UNDERSTANDING ON HOW THE PROJECT
WORKS AND THE MATHEMATICS BEHIND THE ALGORITHMS USED FOR BUILDING THE MODELS.
PROJECTS SHOULD BE OF AN INTERMEDIATE LEVEL (NOT BEGINNER PROJECTS LIKE CAT-DOG
CLASSIFIER OR MNIST CLASSIFIER)
AVOID WRITING TOPICS OR CONCEPTS IN YOUR RESUME WITH WHICH YOU ARE NOT
COMPLETELY FAMILIAR WITH.
HAVING AN ML/DS INTERNSHIP IN YOUR WORK EXPERIENCES WILL STRENGTHEN YOUR
RESUME.
NAILING THE INTERNSHIP
CODING EXAM
1. Aptitude based questions
2. MCQs related to Data Science, Probability and Statistics
3. Basic to Medium Data Structure and Algorithm
TECHNICAL INTERVIEW
Questions.
1. Questions on projects (whole rundown of
4. Sometimes creating an ML model for the given dataset
what you did, how you did).
using sklearn(easy one).
2. Be familiar with the basic concepts like
gradient descent, loss function, regularization,
HR INTERVIEW
confusion matrix, f1 score, correlation, etc., and
ML algorithms like linear regression, logistic
1. What are your strengths and weaknesses?
regression, KNN, Naïve Bayes, SVM, etc.
2. What were the challenges you faced during your
3. Basic DSA Questions
project?
3. In case of a group project, were there any clashes within
the team, and then how were you able to resolve them?
COMPANIES
AND MANY MORE...
CONCLUSION
Having a basic knowledge of data structures and algorithms is essential.
Study in a sequential manner, keep your basics sound and don't skip things
out of peer pressure or anything. (For eg. - if you don't know logistic
regression, there is no point doing projects on neural networks. So even if
people around you are working with neural nets, you study logistic
regression first and then go ahead).
Study without any fear or nervousness and don't get affected by what
students around you are doing/saying.
Stay healthy and talk to your parents/friends/seniors whenever you feel the
need to.
Check out Intern Experiences in the TPC Portal.
GOT QUESTIONS?
REACH OUT!
RISHABH
9084485193
RIYA
8529174323
PRAYASH
8018179474