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Reading list

Machine Learning Basics for a Newbie
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
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
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 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

What is the Role of a Mentor in Your Data Science Journey?

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 26 Nov, 2023
5 min read

Introduction

Data Science is a fast-booming domain that has seen exponential growth in recent years. It involves collecting, processing, and analyzing data using math, statistics, specialized programming, artificial intelligence, machine learning, and more.

Although Data Science has expanded beyond the IT and CS sectors and is now a part of almost all fields of work, the requirements for different roles across different industries broadly vary. This has given rise to a plethora of courses in Data Science pertaining to various fields and jobs. With such diverse topics to explore and a vast directory of courses, it can often be overwhelming to choose a particular subject or plan a channeled career path. This is where the role of a mentor in Data Science comes into play.

Whether you are about to start a career in Data Science or looking to upskill, having a mentor can be really helpful. This article aims to explain the role of a mentor in your Data Science journey by listing out the need, the support, and the benefits of having one.

Table of contents

Who Requires a Mentor in Data Science?

  • Students who wish to explore the field of Data Science.
  • Fresh graduates who are looking to get jobs in Data Science.
  • Engineers and other professionals who wish to make a career change into Data Science.
  • Data scientists who wish to upskill and expand their career options.
  • Anybody who has an interest in Data Science and wants to gauge their scope in the domain.

Looking for Data Science Mentorship? Here’s your solution! Sign-up for our AI/ML Blackbelt Plus Program and get personalized mentorship for career excellence!

How Can a Mentor Help You in Data Science?

Importance of a Mentor

People often develop an interest in Data Science through their peers or by seeing specific job profiles. While they all want to be a part of the ever-progressing domain, most do not know where to start or how to go about it. They tend to try a hand at many different things related to Data Science and end up not having much to contribute to any particular role. A mentorship in Data Science helps mentees decide what they wish to achieve, stay focused on their aim, and plan how they can get there. Now let’s see how a mentor can help you in your Data Science journey.

Planning a career path

A mentor helps set up a detailed career path for you and guides you through it, at your own pace. Your mentor will initially help you choose the right courses based on your skills, strengths, weaknesses, and interests. They will then plan for you a customized roadmap into Data Science with well-defined short-term goals, long-term goals, and practical challenges.

Staying focused and supported

Your mentor will always be there to guide you through your journey. They will regularly check on you, ask you questions, answer your queries, and sit with your problems and roadblocks every step of the way. Their dedication and support will give you a sense of accountability that pushes you forward.

Learning the trade

Mentors will share the knowledge, technical resources, tips, and tricks they have accumulated over their many years of experience in the industry. They will also train you in solving real-world problems from which you will learn through practice and get industry-ready.

Expanding your network

Having an industry expert as a mentor gives you access to a broader network of people in the Data Science community that will help you go further and plan the next steps in your career.

Finding a job

Your mentor helps you find the right job/role depending on what you know and what topics interest you to learn further. During your mentorship, you will also be prepped for job interviews in the field of Data Science.

How to Plan your Data Science Mentorship?

Once you find the right mentor, your personal and professional goals become a team effort with their guidance and support. Your mentor will always be there to guide you in the right direction with regular feedback and constructive criticism throughout your Data Science journey. Here’s a short guide on how to plan your Data Science mentorship to make the most of it.

Benefits of Having a Mentor

Find the Right Mentor

The first and most important step in your Data Science mentorship is finding the right mentor. When choosing a mentor, do your background checks and make sure they are experienced in the fields you wish to pursue. What they have to offer must be in sync with your goals to guarantee a fruitful mentorship.

Set Up Goals

Your journey must be Before you start, sit with your mentor and tell them what exactly you wish to achieve. If you’re not sure, talk to them about it and let them help you figure it out. A good mentor must be able to chart their mentee’s journey and prepare a personalized step-by-step guide for them to achieve their specific needs and goals.

Fix a Schedule

Speak to your mentor and schedule regular 1:1 meetings, at a mutually convenient time. These meetings will help you stay on track, let you to clear your doubts directly from your mentor, discuss any Data Science related topic, and even work alongside your mentor on real-world projects.

Prepare for the Meetings

Make sure you do your homework well before each scheduled meeting. Respect the time of your mentor and value their input, advice, and suggestions. Remember that your mentor can only help you for as long as you are regularly working on their guidelines and getting back to them with your work.

Request for Mock Interviews

If you are preparing to take up a particular role or have job interviews coming up, do ask your mentor to set you up for mock interviews in advance. This will give you an idea of what to expect and cover the most important topics and questions you might be asked.

Conclusion

Data Science is integral to most industries today, and numerous courses cater to beginners and professionals seeking to learn, upskill, and stay abreast of evolving trends. However, aspiring individuals often find it challenging to navigate this data-driven ecosystem. The crucial first step is finding a mentor whose knowledge aligns with your aspirations. A mentor plays a vital role in establishing a career path and guiding you through every step of your Data Science journey. Ready to embark on your Data Science journey? Explore our AI/ML Black Belt program, featuring mentorship for personalized guidance.

Key takeaways

  • People from any field of work, who may be at any stage of their career, can benefit from having a mentor to guide them into Data Science.
  • Mentors help mentees select suitable courses, plan a career path, and stay focused and guided throughout the journey.
  • A mentor-mentee relationship is highly personalized.
  • A mentorship in Data Science involves regular performance tracking, advising, and constructive feedback from the mentor.
  • Mentees get direct access to an industry expert mentor and can learn from them through the 1:1 meetings and Q&A sessions.
  • Mentees also get trained in solving real-world problems and gain practical knowledge during their mentorship, making them job-ready.

Frequently Asked Questions

Q1. What is the role of a mentor in science?

A. A mentor in science offers guidance, insights, and support, providing a roadmap for personal and professional growth.

Q2. How can a mentor help you along the journey?

A. A mentor assists by sharing knowledge, offering advice, and providing a valuable perspective, smoothing the path to success.

Q3. What are the roles and responsibilities of a mentor?

A. A mentor’s roles include advising, guiding, and supporting mentees, fostering their development and helping them navigate challenges.

Q4. How can a mentor guide you on your career journey?

A. A mentor guides your career journey by offering insights, sharing experiences, and helping you make informed decisions for long-term success.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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