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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 Become a Data Scientist in USA? 

Nitika Sharma
Nitika Sharma Last Updated : 20 Sep, 2023
10 min read

In today’s day and age, data science roles have become essential for organizations to survive. As per Glassdoor, the role of a Data Scientist has consistently ranked among the most sought-after job roles in the USA for four consecutive years. Wondering how to become a data scientist in USA? Don’t worry! We have got you covered. Read on to know data science career trends, job market, learning path and more! 

Table of contents

Data Science Career Trend in USA

Before we discuss how to become a data scientist in USA, let’s look at some facts that strongly prove rise in the demand for these professionals: 

Growing Data Science Careers in the USA

According to Glassdoor, Data Scientist is one of the most sought-after job roles in the USA, consecutively for 4 years. The United States Bureau of Labour Statistics has reported that by 2026, the demand for skilled and knowledgeable data scientists will boost, leading to a 27.9% rise in employment. 

Data Scientist Jobs
Source: Fortune

The Challenge

This soaring demand has brought a significant challenge – a shortage of qualified professionals.To bridge this gap, educational institutions have stepped up their efforts. They are actively revamping existing data science programs or creating new ones with industry-linked curricula.

One of the prominent choices for data science enthusiasts is Analytics Vidhya’s Data Science Bootcamp. The course covers everything you need to know to become a data scientist!

Career Outlook in Data Science

A data science career offers not only promising job opportunities but also enticing salaries, transcending industry boundaries. The Bureau of Labour Statistics anticipates robust growth rates between 2019 to 2029 for various data science careers. These include operations research analysts (25% growth), computer systems analysts (7% growth), information and computer research scientists (15% growth), and market research analysts (18% growth).

Data Science Degree Program Guide
Source: Data Science Degree Program Guide

Regarding compensation, data science careers in the US tend to exceed the national average. Depending on an individual’s experience, professionals can earn salaries soaring up to $100,560 annually. However, it’s essential to note that exact salaries vary significantly based on educational qualifications, location, industry, years of experience, and the employer.

Yet, a study conducted by the McKinsey Global Institute predicts a shortage of nearly 190,000 professionals possessing data science and deep analytical skills in the United States alone by 2028. This shortfall reflects the relentless growth of the Big Data trend, compelling companies to aggressively seek qualified and skilled data scientists to harness the potential of their extensive data reservoirs.

Understanding Data Science

Before diving into the specific steps to become a data scientist, it’s essential to understand the field of data science and its significance in today’s world. Data science is an interdisciplinary field that combines expertise in mathematics, statistics, programming, and domain knowledge to extract meaningful insights from data. These insights can be used to make informed decisions, predict future trends, and solve complex problems.

Data scientists are vital in various industries, including healthcare, finance, e-commerce, and technology. They are responsible for tasks such as data collection, data cleaning, data analysis, and the development of machine learning models. By leveraging their skills, data scientists can uncover hidden patterns in data, leading to improved business strategies and increased efficiency.

How to Become a Data Scientist in USA?

The following section discusses the career pathway to becoming a data scientist in USA:

Educational Path

One of the first steps to becoming a data scientist in the USA is to choose the right educational path. Data scientists typically have a strong academic background in computer science, statistics, mathematics, or a related field. Here are some educational options to consider:

  • Bachelor’s Degree: Many data scientists begin with a bachelor’s degree in a relevant field. Common majors include computer science, mathematics, statistics, or engineering. While a bachelor’s degree is a good starting point, most data science positions require additional education.
  • Master’s Degree: A master’s degree in data science, machine learning, or a related field is often the next step. Programs like Master of Science in Data Science (MSDS) or Master of Data Science (MDS) are becoming increasingly popular. These programs provide in-depth data analysis, machine learning, and data visualization knowledge.
  • Ph.D.: For those interested in research and academia, pursuing a Ph.D. in a data-related field can be an excellent choice. A Ph.D. program allows you to delve deep into data science research and contribute to advancements in the field.

When choosing an educational path, consider your career goals and the level of specialization you desire. You can also consider taking an online course to learn relevant data skills. Our Blackbelt Plus program has helped several learners become data scientists. 

Essential Skills for Data Scientists

Becoming a data scientist requires a diverse skill set that combines technical and soft skills. These skills are essential for effectively analyzing data and communicating your findings. Here are some key skills you’ll need:

  • Programming: Proficiency in programming languages such as Python and R is crucial for data scientists. These languages are commonly used for data analysis, data visualization, and machine learning.
  • Statistics: A strong understanding of statistics is essential for conducting hypothesis testing, making predictions, and drawing meaningful conclusions from data.
  • Machine Learning: Data scientists should have a solid grasp of machine learning concepts and techniques. This includes knowledge of algorithms, model evaluation, and feature engineering.
  • Data Cleaning: Data is rarely clean and ready for analysis. Data scientists must excel in data preprocessing, which involves cleaning, transforming, and structuring data for analysis.
  • Data Visualization: The ability to create clear and informative data visualizations is vital for conveying insights to non-technical stakeholders.
  • Domain Knowledge: Depending on your industry, domain-specific knowledge can be a significant advantage. It allows you to understand the context of the data and make more informed decisions.
  • Problem-Solving: Data scientists are problem solvers at their core. They should be able to tackle complex issues and develop innovative solutions.
  • Communication: Effective communication is key for presenting your findings to technical and non-technical audiences. Data scientists should be able to tell a compelling story with data.
  • Teamwork: Data science projects often involve collaboration with cross-functional teams. Being a good team player and collaborator is essential.
  • Continuous Learning: The field of data science is constantly evolving. Data scientists should be committed to continuous learning and up-to-date with the latest tools and techniques.

Also Read: Top 14 Must Have Data Science Skills in 2023

Specializations in Data Science

Data science is a broad field with various specializations you can explore based on your interests and career goals. While data scientists possess a diverse skill set, some specialize in specific areas. Here are a few data science specializations:

  • Machine Learning Engineer: Machine learning engineers focus on building and deploying machine learning models. They work on natural language processing, computer vision, and recommendation systems.
  • Data Engineer: Data engineers are responsible for designing and maintaining data pipelines. They ensure data is collected, stored, and made available for analysis.
  • Data Analyst: Data analysts focus on analyzing data to provide actionable insights. They often work with structured data to answer specific business questions.
  • Big Data Analyst: Big data analysts work with large and complex datasets. They are skilled in technologies like Hadoop and Spark.
  • Business Intelligence (BI) Analyst: BI analysts focus on creating reports and dashboards to help businesses make data-driven decisions. They often use BI tools like Tableau or Power BI.
  • Research Scientist: Research scientists often work in academia or research institutions. They conduct experiments and research to advance the field of data science.

Building a Strong Foundation

Once you’ve chosen your educational path and developed essential skills, building a strong data science foundation is important. This involves gaining a deep understanding of core concepts and tools. Here are some steps to consider:

  • Online Courses and MOOCs: Coursera, edX, and Analytics Vidhya offer various data science courses and specializations. These courses cover data analysis, machine learning, and deep learning.
  • Textbooks and Resources: Invest in textbooks and reference materials that cover data science fundamentals. Read our article on data science books to get the updated reading list. 
  • Coding Practice: Practice coding regularly to reinforce your programming skills. Work on coding challenges on platforms like LeetCode, Kaggle and HackerRank.
  • Data Science Projects: Undertake data science projects that allow you to apply your knowledge to real-world problems. You can find datasets and project ideas on websites like Kaggle and GitHub.
  • Online Communities: Join online data science communities and forums, such as Stack Overflow and Reddit’s r/datascience. These communities are valuable for asking questions, sharing knowledge, and networking with other data enthusiasts.
  • Competitions: Participate in data science competitions on platforms like Kaggle. Competitions provide an opportunity to solve challenging problems and learn from the solutions of others.
  • Data Science Blogs and Podcasts: Follow data science blogs and podcasts to stay updated on industry trends and best practices. 

Gaining Practical Experience

Practical experience is invaluable for data scientists. It allows you to apply your skills to real-world problems and build a portfolio that showcases your abilities. Here are some ways to gain practical experience:

  • Internships: Seek data science internships to gain hands-on experience in a professional setting. Internships provide exposure to real data and industry practices.
  • Freelance Projects: Consider freelancing or consulting on data science projects. You can find freelance opportunities on platforms like Upwork and Freelancer.
  • Personal Projects: Work on personal data science projects that interest you. These projects can be based on your hobbies or areas of curiosity.
  • Collaboration: Collaborate with peers or colleagues on data science projects. Group projects allow you to learn from others and tackle more significant challenges.
  • Open Source Contributions: Contribute to open-source data science projects. Open-source contributions are a great way to gain experience and give back to the community.

Also Read: Top 10 GitHub Data Science Projects with Source Code

Networking and Communities

Networking is a critical aspect of building a successful data science career. Connecting with others in the field can lead to valuable opportunities, collaborations, and insights. Here’s how to network effectively:

  • LinkedIn: Create a strong LinkedIn profile that highlights your skills and experiences. Connect with professionals in the data science industry.
  • Professional Organizations: Join data science-related professional organizations and associations. Examples include the Data Science Association and the American Statistical Association.
  • Meetups and Conferences: Attend data science meetups, conferences, and workshops virtually in your area. These events provide opportunities to meet like-minded professionals.
  • Online Forums: Participate in online data science forums and communities. Answer questions, share knowledge, and engage in discussions.
  • Mentorship: Seek mentorship from experienced data scientists. A mentor can provide guidance and advice as you navigate your career.

Become a part of Analytics Vidhya Community and connect with data experts across the globe, ask questions, and get guidance from real experts!

Job Search and Application

When you’re ready to start your job search as a data scientist, it’s essential to approach it strategically. Here are steps to consider:

  • Crafting Your Resume: Create a data science resume highlighting your skills, experiences, and projects. Tailor your resume to each job application.
  • Cover Letter: Write a compelling cover letter that explains why you’re a strong fit for the position. Customize your cover letter for each application.
  • Job Search Platforms: Use job search platforms like LinkedIn, Indeed, Glassdoor, and company websites to find data science job openings.
  • Networking: Leverage your network to discover job opportunities. Reach out to connections for referrals and recommendations.
  • Application: Follow the application instructions carefully and ensure that all required documents are submitted. Pay attention to deadlines.
  • Interview Preparation: Prepare for data science interviews by reviewing common interview questions, practicing coding challenges, and studying relevant topics.
  • Interview Performance: During interviews, demonstrate your problem-solving skills, domain knowledge, and communication abilities.
  • Follow-Up: Send a thank-you email after interviews to express your gratitude and reiterate your interest in the position.

Interview Preparation

Data science interviews can be challenging, but thorough preparation can boost your confidence and chances of success. Here are some tips for interview preparation:

  • Review Core Concepts: Brush up on fundamental data science concepts, statistics, and machine learning algorithms.
  • Coding Practice: Practice coding problems and data manipulation tasks in Python or R.
  • Behavioral Questions: Prepare for behavioral questions that assess your problem-solving abilities, teamwork, and communication skills.
  • Case Studies: Be ready to tackle data science case studies that involve analyzing data and presenting findings.
  • Machine Learning: If you’re interviewing for machine learning roles, be prepared to discuss algorithms, model evaluation, and feature engineering.
  • Portfolio Review: Review your data science portfolio and be prepared to discuss your past projects and their impact.
  • Mock Interviews: Consider participating in mock interviews with peers or mentors to gain experience and receive feedback.

Why Choose BlackBelt Plus Program?

The BlackBelt Plus Program offers a compelling pathway to becoming a data science professional in the USA for several reasons. It stands out for its comprehensive and tailored approach to data science education. The program provides a robust curriculum covering a wide spectrum of data science topics, ensuring participants acquire a well-rounded skill set. Moreover, its emphasis on hands-on experience and real-world projects equips students with practical skills that are highly valued in the job market.

How to Become Data Scientist in USA

Data Science Salary in USA

The average annual compensation for a Data Scientist in the United States area is estimated to be approximately $152,308, with a median salary of $117,673. In addition to the base salary, there is an estimated additional pay of around $34,636 per year, which may encompass components such as cash bonuses, commissions, tips, and profit sharing.

Here is the city-wise breakup of data science salary in USA:

CityData Science Salary
New York                    $132,826     
New Hampshire $128,704   
California                       $127,388
Vermont                        $121,599   
Idaho                             $120,011   
Massachusetts                  $119,234 
Wyoming                        $118,644 
Maine                           $117,802 
Washington                    $116,118 
Hawaii                          $115,887 
Source: Ziprecruiter

Conclusion

Becoming a data scientist in the USA is an exhilarating path filled with endless opportunities. It demands dedication, honing technical skills, and hands-on experience. Whether through formal education, bootcamps, or self-study, the journey offers flexibility. Continuous learning and networking within the data science community are vital for staying relevant. Perseverance, adaptability, and a profound passion for deciphering data insights are the driving forces behind success. With these attributes, anyone can thrive as a sought-after data scientist, contributing to innovation and societal impact.

Good luck for your data science journey!

Frequently Asked Questions

Q1. What qualifications do you need to be a data scientist?

A. You need to have a strong background in mathematics, python/R and statistics to start your data science career.

Q2. How long does it take to become a data scientist?

A. On average, for someone without prior coding or mathematical experience, it typically takes about 7 to 12 months of focused study to reach an entry-level data scientist level.

Q3. Who gets paid more data analyst or data scientist?

A. It comes as no surprise that data scientists earn significantly more money than their data analyst counterparts. To know more, read our article on Data Scientist vs Data Analyst.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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Responses From Readers

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Madhura Shete
Madhura Shete

Thank You So Much for providing such informative content. Keep sharing. Courses and training will definitely help boost knowledge in this field. Unified Mentor is an online learning platform that offers user-friendly certification courses. Learn and excel in your chosen field with ease. I hope you will like our platform and courses.

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Madhura Shete
Madhura Shete

Great content. Keep sharing. Courses and training will definitely help boost knowledge in this field. Unified Mentor is an online learning platform that offers user-friendly online certification courses and training. I hope you will like our platform and courses.

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