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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Tips and Tricks to Crack Campus Placement in Data Science

Yana Khare
Yana Khare 10 Aug, 2024
8 min read

Introduction

Wow, it’s that time of the year again—placement season! Are you experiencing a sense of being trapped or feeling inundated while trying to find your way to your ideal data science job? Do not fret; you are not by yourself. Securing that ideal position may seem overwhelming, but using the right strategy can leave a lasting impact and distinguish yourself. Here are some tips and tricks for campus placement in data science.

Tips and Tricks to Crack Campus Placement in Data Science

Table of contents

Tips and Tricks to Crack Campus Placement in Data Science

Here are some tips and tricks to help you crack campus placement and land that role in data science.

Craft an ATS-Friendly Resume

  • Understand How ATS Works:
  • Use Relevant Keywords: First, read the job description several times and try to identify the most frequently used keywords and phrases. Some of these may include skills, positions, or special career applications
  • Simple and Clean Formatting:  
  • Quantify Your Achievements: Put a numerical value on your specific results where possible

Also Read: A Learning Path to Become a Data Scientist in 2024

Research the Industry

  • Identify Key Trends and Technologies:
  • Understand the Market Demand:
  • Analyze Top-Paying Companies: This involves considering the available data on the major employers of data scientists and their competitive remuneration. The demand for data scientists is often remarkably high in fields such as IT, finance, healthcare,
  • Study the Competitive Landscape: Assess
  • Learn About Company Culture:  
  • Explore Salary Expectations:  

Maintaining an Updated LinkedIn Profile

  • Showcase Your Work:  
  • Optimize for Keywords:  

Master the Basics and Advanced Concepts

  • Core Concepts:  
  • Programming Proficiency:
  • Machine Learning Algorithms:
  • Data Wrangling:  
  • Model Evaluation: Know the model-evaluation techniques, such as cross-validation, confusion matrix, precision, recall value, F1 value, ROC-AUC, etc. Comparing current and developed models can be valuable when cross-verifying your models.

Also Read: Top 10 In-Demand Data Tech Roles in Data Science

Practicing Coding Regularly

  • Solve Problems Daily:  
  • Focus on Data Structures and Algorithms: Data structures like arrays, linked lists, trees, and graphs, as well as some algorithms like sorting, searching, and dynamic programming, are prerequisites for interviews for data science-related jobs. Regular reinforcement helps fix these ideas in the students’ minds
  • Simulate Timed Tests:  
  • Explore Real-World Projects: Besides

Sharpen Your Communication Skills

  • Clarity and Conciseness:  
  • Storytelling with Data:  
  • Practice Presentations:  M

Taking Part in Mock Interviews

  • Simulate Real Interviews: Another tip for campus placement in data science is to participate in realistic interviews that mimic the actual interviews, including technical, behavioral, and HR parts. This lets you get acquainted with the building’s layout
  • Reduce Anxiety: P

Interview Etiquette and Tricks

  • Dress Appropriately: Another tip for campus placement in data science is to wear clothes suitable for the organization’s corporate culture. If you are unsure, choose business formal,
  • Punctuality:  
  • Polite Greeting:  
  • Active Listening:  Practice active listening to give the questions due attention. Do not hesitate to ask for an explanation if something is unclear. This will show the interviewer that you are keen
  • Positive Body Language: It is essential to keep open and positive body language during the interview.
  • Express Gratitude:  

Asking Relevant Questions to the Interviewer

  • Impact and Expectations: Show some concern about how you wish to change your organization. Ask what is expected after the first three months of assuming the new position or how that role aligns with the company’s goals and achievements.
  • Feedback and Next Steps: Ask about the following stages in the hiring process. Also, ask the interviewer whether you could hear their feedback regarding your interview performance afterward. This is a sign of openness to feedback.

Following Up After the Interview

  • Send a Thank-You Email:  
  • Highlight Your Fit:  
  • Be Professional:  

Conclusion

Also Read: Top 11 Data Science Internships in India

Campus Placement in Data ScienceCrack Campus Placementcrack data science placementtips for data science placement
Yana Khare
Yana Khare 10 Aug, 2024

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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