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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

How to Find a Mentor Who Can Accelerate Your Career?

A
Analytics Vidhya Last Updated : 27 Jun, 2023
5 min read

90% of workers with a mentor are happier in their jobs, says CNBC. Today’s competitive job market demands from you the perfect skillset to strive in your career. To propel your career to new heights, finding a mentor who can guide you through is one of the wisest approaches. A mentor can provide guidance and support with knowledge, inspiration, and industry insights. This article will explore the steps that can unquestionably aid them in locating a qualified individual who can quicken their progress toward their goals. So, take advantage of the possibilities, learn how to find a mentor for a career, and let your mentor’s knowledge guide you to a successful future.

Table of contents

What is Mentorship?

Mentorship is the name of a relationship that speaks of a personal and professional connection between a more experienced individual (called the mentor) and a less experienced person (called the mentee). A mentor offers advice, support, and expertise to assist mentees in developing both personally and professionally. Mentors can guide us through our profession by contributing insightful counsel and chances for professional advancement.

How to find a mentor for career

Also Read: What is Mentorship and How It Can Transform Your Data Science Career?

Step 1 – Understanding the Value of a Mentor

Recognizing the benefits of having a mentor is crucial to advance your career significantly. The mentoring process brings great advice for mentees, direction, and support. A dedicated mentor guides you through difficulties and hastens professional development.

Understanding the Value of a Mentor
Source: Tenor

Step 2 – Self-Reflection and Goal Setting

You should take time and reflect on your career aspirations, weaknesses, strengths, and areas you need to seek improvement. Clear goal setting for your career is a must. Determine the specific knowledge and abilities you hope to acquire through mentoring. This will assist you in identifying a mentor who meets your needs.

Self-Reflection and Goal Setting
Source: GIPHY

Step 3 – Exploring Different Mentorship Channels

It is crucial to cast a wide net while looking for a mentor and to investigate different mentorship channels. By exploring various options, you improve your chances of finding a mentor who shares your professional objectives, industry, and personal preferences. 

One can find a mentor through various channels, including industry associations, professional networks, workplace mentorship programs, or online platforms. So, it is wise to identify the channels that best suit your preferences and goals through proper research.

Step 4 – Approaching Potential Mentors

Approach potential mentors who possess the expertise and abilities you are seeking. Make a special note expressing your respect for their work and how their professional guidance can help you. Be truthful, exhibit respect, and demonstrate your desire to learn.

Follow these unbeatable tips for approaching potential mentors:

  • Do your research
  • Leverage your network
  • Make a personal connection
  • Craft a compelling message
  • Offer value and reciprocation
  • Be flexible and patient
  • Respect boundaries and time commitments
Exploring Different Mentorship Channels
Source: GIPHY

Step 5 – Establishing an Effective Mentor-Mentee Relationship

Establish specific expectations and objectives for the connection as soon as a mentor agrees to work with you. Be transparent in your communication, pay attention when they offer suggestions and welcome criticism. Building mutual respect and trust is crucial for a successful mentoring relationship.

For productive learning through a successful mentor-mentee relationship, follow the tips given below:

  • Clearly define expectations and goals
  • Maintain open and honest communication
  • Establish a regular meeting schedule
  • Come prepared and be proactive
  • Actively listen and be receptive to feedback
  • Show gratitude and appreciation
  • Respect boundaries and confidentiality
  • Seek guidance beyond your immediate needs
  • Be flexible and adaptable
  • Maintain a long-term relationship

Step 6 – Overcoming Mentorship Challenges

Be aware that mentorship can run into problems like time restrictions or unrealistic expectations. When problems do develop, take the initiative to solve them. Keep lines of communication open, make required goal adjustments, and collaborate to discover answers.

Here are some common strategies to overcome challenges that you being a mentee, may face:

  • Find the right mentor
  • Establish clear expectations
  • Maintain regular communication
  • Bridge generation or experience gaps
  • Address personality conflicts
  • Navigate through time constraints
  • Deal well with limited feedback
  • Embrace self-responsibility

Step 7 – Maintaining and Leveraging the Mentorship Relationship

Maintain your mentoring relationship involvement and express gratitude for your mentor’s advice. Update them on the project’s status, ask for their opinion on important choices, and thank them for their assistance. Utilize your mentor’s connections and network wherever possible to increase your professional chances.

How to find a mentor

Step 8 – Resources and Tools for Finding a Mentor

Make use of a variety of tools and resources to help you find a mentor. Investigate mentorship resources, join a trade association, or attend networking events. Go in for podcasts, online sources, and books that can all offer insightful advice on mentoring. Take a look at some common resources and tools:

  • Professional networking platforms
  • Industry associations and organizations
  • Alumni networks
  • Local meetup groups and events
  • Company or organization mentorship programs
  • Professional development courses and workshops
  • Online mentorship platforms
  • Personal and professional connections
  • Mentoring programs within nonprofit organizations
  • Referrals and recommendations

Find Your Data Science Mentor

Finding a mentor who can direct and advance your career becomes essential in the ever-evolving world of professional growth. Throughout this article, we have discussed the crucial steps for finding a mentor for your career. 

Join our Blackbelt program to level up your data science career. You will connect with experienced mentors with expertise in the field. Take advantage of this chance to locate your data science mentor and quicken your development. Enroll right away in the Blackbelt program to take the next step. Find a mentor to guide you on the road to success, as now you know how to do so.

Frequently Asked Questions

Q1. How do I find a good mentor for my career?

A. One effective method is to network with professionals in your field or industry and ask for recommendations. Another option is to seek out professional associations or organizations that offer mentorship programs. You can also use online platforms such as LinkedIn or mentoring websites to find potential mentors.

Q2. Can a mentor help you get a job?

A. Yes, a mentor can certainly help you get a job. A mentor can provide valuable advice on job search strategies, help you develop your skills and expertise, connect you with industry contacts, and guide navigating the hiring process.

Q3. How do I find an executive mentor?

A. To find an executive mentor, look for individuals who have already succeeded in your desired field. Consider attending industry events, networking with other professionals, or joining professional associations. You can also consider contacting executive coaching firms or consulting agencies specializing in mentorship programs.

Q4. How do I hire a mentor?

A. Hiring a mentor will depend on your specific needs and goals. Consider working with an executive coach or consultant if you want personalized one-on-one mentorship. Alternatively, if you are looking for a more informal mentorship relationship, you could contact professionals in your network or join a mentoring program offered by a professional association or organization.

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Analytics Vidhya Content team

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Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
What is the Role of a Mentor in Your Data Science Journey?
What is the Role of a Mentor in Your Data Science Journey?
What is Mentorship and How It Can Transform Your Data Science Career?
What is Mentorship and How It Can Transform Your Data Science Career?

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