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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

10 New Year Resolutions for Every Data Scientist

Pankaj Singh
Pankaj Singh Last Updated : 20 Dec, 2023
6 min read

Introduction

As we approach the new year, many of us engage in introspection, reflecting on the past and formulating resolutions to guide ourselves toward better goals in the future. As a data scientist, I contemplate my aspirations for the upcoming year with diverse goals. I envision harnessing cutting-edge technologies and refining analytical skills to unravel complex insights. It also includes fostering innovation and contributing to the ever-evolving landscape of data-driven solutions in the coming year.

Within the data science community, each individual holds a unique resolution, providing a wellspring of inspiration for anyone crafting resolutions as they step into 2024. In this article, you will get to know the 10 year-end resolutions a data scientist can adopt to foster continued learning and growth in the field. 

Table of contents

10 New Year Resolutions for Data Scientists

Here are year-end resolutions for data scientists that you must keep note of before stepping in 2024:

Upskilling in GenAI: Leveraging Tools for Enhanced Performance

Generative AI is more than a fleeting trend or mere hype. It is a transformative technology with far-reaching implications and business impact.

Ritu Jyoti, group vice president of Artificial Intelligence and Automation market research and advisory services at IDC
Upskilling in GenAI: Leveraging Tools for Enhanced Performance

In this era of automation, we can anticipate the shift towards generative tools for generating, analyzing, and creating new data that resemble real-world data. Since the inception of GenAI, such as OpenAI’s GPT-4, Meta’s LLaMA, Google’s LaMDA, and PaLM,  it has become essential for aspiring data scientists or experts to improve their skills using these tools to navigate the shift. So, as a New Year resolution, you can start by dedicating time to upskilling in Generative AI tools. These advanced technologies can significantly boost your performance and open new avenues for creativity and problem-solving.

Ready to upskill your GenAI skils? Enroll for our GenAI Pinnacle Program today!

Focus on Developing GenAI Tools

GenAI Tools

The advancement in GenAI has introduced a paradigm shift in the data science industry by automating their tasks, from data analysis to insights generation. However, data scientists play a pivotal role as the cornerstone of an organization, extracting valuable insights and constructing predictive models from the vast volume of generated data. 

Rather than merely using existing GenAI tools in the competitive ecosystem, make it a resolution to contribute to their development actively. Developing GenAI tools should be your focus in the coming year to keep up with the advancements. With this, you can analyze, refine, define, and act on the insights seamlessly, enabling your organization to stay at the forefront of innovation and competitive advantage in the rapidly evolving landscape. For the development, you can engage with the community, participate in open-source projects, and collaborate with fellow data scientists to create innovative GenAI solutions.

From Model Building to Deployment: Embrace MLOps

MLOps Roadmap

As we know, Machine Learning (ML) has transformed organizations’ data-driven decisions to respond quickly to the changing market dynamics. It is achieved by analyzing large data volumes in seconds and providing real-time insights. However, transitioning from development to deployment in a production setting poses numerous challenges. Thus, it is imperative to go beyond mere model construction. MLops addresses these challenges by seamlessly bridging the gap between machine learning and operations, streamlining the entire lifecycle.

You must understand the end-to-end cycle by mastering MLOps (Machine Learning Operations) and LLMOps (Large-scale Machine Learning Operations). The aim must be to learn how to deploy models into production and monitor their performance effectively in 2024. 

Enhance Critical and Structural Thinking

Enhance Critical and Structural Thinking

Enhancing critical and structural thinking skills empowers data scientists to analyze complex problems, discern patterns, and design robust frameworks for data analysis. This cognitive agility enables them to develop more effective models, make informed decisions, and uncover valuable insights, ultimately contributing to the success of data-driven initiatives. Recognize the evolving role of humans in collaboration with AI. Develop critical and structural thinking skills to define problems, understand them deeply, and guide regular reading and analyzing research papers on AI in model building. Commit to regularly reading and studying research papers to stay abreast of the latest practices.

Share Knowledge with the Community

Share Knowledge with the Community

Data science tools and techniques constantly evolve, and staying up to date requires constant peer support. So, sharing knowledge with the community in data science fosters a collaborative environment, facilitating the exchange of ideas, methodologies, and insights. It contributes to the field’s collective growth and enhances individual learning through exposure to diverse perspectives and real-world applications. Additionally, it promotes innovation and best practices, benefiting the entire data science community.

You can contribute to the growth of the data science community by sharing your knowledge. Checkout our community platform to fit the right fit to communicate with professionals. Commit to posting regular updates on LinkedIn AI and Data Science topics, writing insightful articles, and delivering talks at conferences or webinars.

Dedicate 2 Months to GenAI Learning

Allocate two months in the year to immerse yourself in intensive GenAI learning. This deep dive will allow you to grasp the intricacies of these tools and apply them effectively in your projects. GenAI for data science will help you leverage cutting-edge techniques, enhance predictive modeling, and stay ahead in a rapidly evolving technological landscape.

Read 12 Books This Year

Read 12 Books This Year

Expand your horizons by reading 12 books on data science, AI, and related fields. Choose a mix of foundational texts, emerging trends, and case studies to understand the ever-evolving landscape comprehensively. You can also read books to sharpen your critical and structural thinking skills. 

Also Read: 30 Best Data Science Books to Read in 2024

Embrace Lifelong Learning

Recognize that learning is a continuous journey. Commit to embracing lifelong learning by regularly attending workshops, webinars, and training programs to stay current with the latest developments in data science. Stick to the new year resolution for 2024 by exploring data science courses from experts in the industry. 

Build a Diverse Skill Set

Broaden your skill set by exploring complementary areas such as Natural Language Processing (NLP), computer vision, or reinforcement learning. A diverse skill set will make you more adaptable and valuable in the rapidly changing data science ecosystem.

Prioritize Work-Life Balance

Prioritize Work-Life Balance | New Year Resolution

Lastly, ensure a healthy work-life balance. While pursuing professional growth is essential, taking care of your well-being is equally crucial. Establish boundaries, allocate time for relaxation, and maintain a balance that promotes sustained success in your data science journey.

Embark on the new year with these resolutions to propel your data science career to new heights. As you embrace new challenges and opportunities, remember that continuous growth and adaptability are key to thriving in the dynamic world of data science. Cheers to a year of learning, innovation, and success!

Conclusion

With this, we have come to an end of our article on New Year Resolutions for data scientists! The path to success in the ever-evolving field of data science demands continuous learning and active engagement. By committing to reading a research paper each week, attending three AI conferences annually, utilizing diverse data analytics tools, contributing to open source projects, and building a robust GitHub portfolio with at least five projects, you set the stage for growth.

Seeking mentorship, completing a data science course, and developing intermediate database skills further enhance your expertise. For experienced data scientists, building a RAG model, mastering diffusion models and LLMs finetuning, and maintaining a weekly research paper habit ensure staying at the forefront of advancements.

We wish you a Happy New Year 🥳

data sciencedata scientistnew yearNew Year Resolutions
Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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New Year Resolutions for a Data Scientist in 2024
New Year Resolutions for a Data Scientist in 2024
Learning Path to Become a Data Scientist in 2024
Learning Path to Become a Data Scientist in 2024

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