Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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 are the Data Scientist Qualifications in the USA?

Rachel Williams
Rachel Williams Last Updated : 06 Oct, 2023
6 min read

Introduction

In today’s data-driven world, companies have recognized the role of data in attracting and satisfying the audience. This realization has led to an accumulation of data, but to harness its potential, companies seek expert manpower and human intelligence. Data scientists play a key role in leveraging the potential of data by empowering machines to process this information. The lack of opportunities in native countries and excess options in the USA attract the candidates. This article will guide you to gain the qualifications required to become a data scientist in the USA.

Table of Contents

Why Choose to Become a Data Scientist in the USA?

Being the hub of numerous top technological companies, going to the USA is the dream of numerous candidates. With the provision of high salaries, promising career opportunities, and the right place for recognition by a showcase of skills, the Data Scientist career in the USA is blooming. Moreover, the lack of talent among native residents, increasing data organization challenges, and expanding requirements among multiple industries are among the top reasons attracting talents from different countries of the world. Did your expectations soar high, too? Get on the track immediately and begin working towards the dream.

Educational Qualifications for Data Scientists in the USA

The job role of a data scientist is offered by companies to the candidates having the following qualifications:

  • Bachelor’s Degree: It introduces the candidates to core concepts and builds the base. A bachelor’s degree in a relevant field like computer science, statistics, or mathematics is preferable.
  • Master’s Degree: The post-graduation provides a deeper and conceptual understanding of the topics, along with hands-on experience in practical aspects. Companies prefer candidates with master’s degrees for senior roles. Candidates can also get into research and academics with this level of qualification.
  • Online Courses: The option is more suitable for gaining information on real-world requirements and for career transition. These are more job-focused, such as the BlackBelt+ program from Analytics Vidhya offered specifically for candidates interested in becoming data scientists with worldwide acceptance of our certificate.
Data Science Program at Analytics Vidhya | BlackBelt Plus

Technical Skills for Data Scientists in the USA

When it comes to technical knowledge, there are two levels of skills you must possess to be a data scientist in the USA: core technical skills and specialized technical skills.

Core Technical Skills Expected from Data Scientists

1. Programming languages (Python, R, SQL)

It is vital for data handling and model development. Python libraries like Pandas, NumPy, and scikit-learn are important for data tasks. R is a specialized language for data analysis and statistics with packages like dplyr and ggplot2. SQL is necessary for querying and database management.

2. Data manipulation and analysis

The obtained needs to be cleaned and preprocessed before the beginning of functioning and modeling for accuracy. Different functions like feature engineering, hypothesis testing, model validation, decision-making, and others are performed using programming languages.

3. Machine learning and statistical modeling

It is crucial for building predictive models and data-driven decisions. Knowledge of algorithms and frameworks helps accomplish daily tasks specific to the work.

4. Data visualization and reporting tools

Effective communication through visualization and reporting tools is essential with the audience, regardless of technical background. Familiarity and expertise in Matplotlib, Tableau, Power BI, Seaborn, and others is essential.

Data visualization tools for data scientists in the USA

Specialized Technical Skills for Data Scientists

Specialized skills in areas like natural language processing (NLP), computer vision, and deep learning are required to be a successful data scientist. These skills will keep you equipped with the technical know-how to handle the variety of real-life applications of data science.

NLP is focused on human and computer interaction, aiding in computer understanding. NLTK, Transformers, spaCy, and others are the prime requirements. Computer vision is associated with the interpretation and understanding of visual information with proficiency in OpenCV, YOLO, PyTorch, and others. Deep learning is crucial for representing complex patterns in data with the experience of Keras.

Soft Skills for Data Scientists in the USA

These are the most important soft skills to have as a Data Scientist in the USA:

  • Analytical thinking and problem-solving: New and unknown challenges require a focused approach and the ability to face the situation. It also encompasses coming up with unique ideas under pressure to deliver results timely, efficiently, and accurately.
  • Communication and data storytelling: Keeping the audience engaged and attentive is the responsibility of the presenter. The storytelling ability with clarity in communication for both technical and non-technical audiences is the key expectation by recruiters.
  • Collaboration and teamwork: With multidisciplinary objectives being a norm in the industry, collaboration and teamwork are required not only for personal and professional but also for the company’s growth.
  • Business acumen and domain knowledge: Familiarity with significant aspects of the domain eases the tasks and aids in decision-making while saving time.

Other Requirements to Become a Data Scientist in the USA

After gaining the educational qualifications and developing the skillset, there are a few more things you must do to become a successful data scientist in the USA. This includes gaining relevant work experience, creating a comprehensive portfolio of your skills and work, and committing to continuously update yourself professionally.

Relevant Work Experience

The students can opt for internships and co-op programs during college life. It indicates their hunger for a practical approach to learning. Entry-level positions are the best positions to learn more and apply gained knowledge.

After gaining exposure to real-life core problems, understanding one’s area of interest becomes easy. Move forward to gain specialization and, subsequently, experience in the domain, which guides to mid-level and senior data scientist roles in the respective field. To obtain the experience, work on personal projects under the guidance of professors or seniors, take up online courses and certifications, or work on open source contributions or others’.

Comprehensive Portfolio

Building a portfolio and showcasing the work is of immense importance. It indicates the will to learn and remain consistent. It is also important to exhibit the knowledge of different skills, libraries, and frameworks and understand the area of interest. The recruiters can themselves judge the candidate’s suitability for the role while learning about their expertise based on the projects completed.

Candidates who do not have projects in hand can get numerous ideas and actual projects from Google search. Giving a personalized touch through different perspectives also speaks for your skills and capabilities that can be beneficial for the company.

Profile of a Data Scientist in the USA.
Source: Pinterest

Commitment to Continuous Learning and Professional Development

The newer versions of software and tools are launched regularly. Moreover, new technologies in the market are common, too. Hence, like other fields of medicine, engineering, research, law, and many more, continuous learning is crucial for the professional development of data scientists. Numerous sources offer knowledge and practical examples, such as online courses, blogs, competitions, online forums and communities, advanced specialization, mentorship, and others.

Conclusion

Data science is a booming career option in the USA. The data science eligibility in the USA is listed and explained in the article to help you gain familiarity. The decision about the career option followed by planning further steps is the only path to a data scientist career. Devise a plan to gain data science qualifications followed by applying for the job roles. Ensure to understand the strengths, weaknesses, and previous domain knowledge to leverage your own potential in a new career.

Frequently Asked Questions

Q1. Can I become a data scientist in the USA?

Ans. Yes, you can become a data scientist in the USA with the right educational qualifications and skillset. Data science jobs are on the rise in the USA, and they are open to hiring data scientists from around the world.

Q2. What qualification is required for data scientists in the USA?

Ans. To be a data scientist in the USA, you need to have a bachelor’s degree in a relevant field like computer science, statistics, or mathematics, followed by a related master’s degree. Additionally, online courses like the BlackBelt+ program from Analytics Vidhya can train you in real-world requirements, especially if you’re a beginner or looking for a career transition.

Q3. Is data scientist a good job in USA?

Ans. Yes, data scientist is a good job in the USA, as there are plenty of job opportunities offering high salaries in this domain. According to Glassdoor, a data analyst’s average salary is $77,537 per year in the United States.

careerdata sciencedata scientistdata scientist in USAGuidejobqualificationrolesskillsUSA
Rachel Williams
Rachel Williams

Hi, I'm Rachel Williams. I'm a data scientist working at a tech company in Seattle. Over the years, I've gained experience in various related fields such as Data Mining, Data Analytics, Data Exploration using AI tools, etc. I also occasionally monitor the performance of ML models.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details