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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 Analyst in the USA?

Rachel Williams
Rachel Williams Last Updated : 05 Dec, 2023
7 min read

Introduction

Data analytics is a discipline that is flourishing with potential since businesses from all sectors are making substantial investments in big data and expanding their analytics teams. Data analyst jobs have particularly been on the rise in America’s cell phone service, insurance policy, marketing, banking, medical care, and technology sectors. This article will guide you on how to become a data analyst in the USA.

Table of Contents

Career of Data Analysts in the USA

A data analyst is in a position to manage and organize data associated with sales figures, market studies, administrative tasks, language studies, or other types of behavior. They use technological expertise to ensure that data is authentic and of excellent quality.

How to Become a Data Analyst in the USA?

There are several ways to enter into this extremely competitive field, whether you’re shifting to an entirely different career or just starting yourself in the workplace. A certain set of technical skills, including coding languages like Python, R, etc., statistical studies, relational databases, SQL queries, visualization software, and data collection and preparation, often prove necessary for careers in data analysis.

Career Growth of Data Analysts in the USA

Since the early 2000s, the essential function of a data analyst has grown in magnitude, with job prospects ranging from banking to advertising to social media platforms. With the rise in the data-driven marketplace, since 2020, 65% of multinational corporations have boosted their analytics investment extensively. The constant evolution of the tools that data scientists employ is one factor why the role of the data analyst changes.

Data science is becoming more and more autonomous in some areas. This can increase the efficiency of data analysis and allow for the emergence of professionals with simpler quantitative backgrounds.

Salary Growth of Data Analysts in the USA

The salary growth of a data analyst in the USA is significantly based on the years of experience.

Job RoleAverage Base Salary in the USA (per year)
Data Analyst$77,568
Senior Data Analyst$116,428
Data Analyst IV$119,886
Principal Data Analyst$166,923
Chief Data Analyst$185,629
Director of Data$240,885
Vice President of Data$352,781

Growth in Job Demand for Data Analysts in the USA

Is there a demand for data analysts? The short answer is definitely yes. The demand for data analysts has increased significantly in recent years. The number of job opportunities for analyst jobs is reported to increase by 23% between the years 2021 and 2031 by the US Bureau of Labor Statistics.

In 2021, the big data analytics marketplace was estimated to be worth over 240 billion dollars. The global market will likely considerably grow and will have a value worth more than 650 billion dollars by 2029. So, if you are looking to start your career as a data analyst in 2024, get powered up with an extensive, data-oriented skill set, strong networking, industry experience, and an eye-catching portfolio.

Job Role of a Data Analyst in the USA

In the USA, a data analyst plays a pivotal role in helping organizations make data-driven decisions by collecting, processing, analyzing, and interpreting data. Their responsibilities encompass various aspects of data management and analysis:

Hard and soft skills to become a data analyst in the USA.
  • Data Collection: Data Analysts gather data from diverse sources, such as databases, spreadsheets, and APIs. They ensure data accuracy, completeness, and reliability.
  • Data Cleaning and Preprocessing: They clean and preprocess raw data, addressing missing values, outliers, and inconsistencies to ensure data quality.
  • Data Analysis: Data Analysts employ statistical techniques and data analysis tools to uncover trends, patterns, and insights within the data. They use software like Python, R, SQL, and data visualization tools.
  • Data Visualization: They create visual representations of data through charts, graphs, and dashboards to make complex information more accessible to non-technical stakeholders.
  • Reporting: They generate reports and summaries of their findings, often presenting actionable insights to inform decision-makers.
  • Predictive Modeling: Some data analysts use algorithms and machine learning to forecast future trends or outcomes based on historical data.
  • A/B Testing: They design and conduct A/B tests to evaluate the impact of changes or interventions, helping businesses optimize strategies.
  • Data Governance: Ensuring data security, privacy, and compliance with relevant regulations is part of their role, particularly in industries like healthcare and finance.
  • Collaboration: Data Analysts collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to align data analysis with organizational objectives.
  • Continuous Learning: Staying updated with the latest data analysis techniques, tools, and industry trends is crucial for data analysts to remain effective.

How to Become a Data Analyst in the USA?

Embarking on a career as a data analyst in the United States involves a clear, step-by-step journey. This high-demand field offers diverse opportunities in various industries. Whether you’re new to the professional world or transitioning careers, follow these essential steps to become a data analyst:

Source: Career Foundry
  1. Foundational Education: Begin by acquiring foundational knowledge in data analysis. While a bachelor’s degree was traditionally required for entry-level positions, alternative routes like professional certificate programs, bootcamps, or self-study courses are now viable options.
  2. Build Technical Skills: Develop specific technical skills essential for data analysis, including statistics, programming in R or Python, SQL, data visualization, and data cleaning. Tailor your learning to match job requirements.
  3. Real Data Projects: Gain practical experience by working on projects with real data. Seek out programs or courses with hands-on projects, or explore free public datasets for self-initiated projects in areas that interest you.
  4. Portfolio Development: Curate a portfolio showcasing your best work. Include projects demonstrating your ability to scrape data from various sources, clean and normalize data, create visualizations, and derive actionable insights. Highlight teamwork if you’ve collaborated on group projects.
  5. Presentation Skills: Enhance your communication skills, a crucial aspect of data analysis. Practice presenting your findings effectively, as data-driven decision-making relies on your ability to convey insights to stakeholders.
  6. Entry-Level Job: Start applying for entry-level data analyst positions once you have built a strong foundation and a compelling portfolio. Don’t hesitate to apply for roles where you meet most, but not all, qualifications.
  7. Consider Certification or Advanced Degrees: As you progress in your career, consider pursuing certifications such as our AI/ML Blackbelt Plus Program. Additionally, consider advanced degrees like a master’s in data science for more opportunities.

Data Analyst Salary in the USA

The salary of a data analyst in the USA varies depending on various factors, such as the location, the industry, and the number of years of experience you have.

Based on Experience

Experience LevelAverage Annual Salary
Entry-Level Data Analyst$50,000
Junior Data Analyst$62,000
Mid-Career Data Analyst$74,000
Experienced Data Analyst$87,000
Late-Career Data Analyst$110,000
Source: https://www.indeed.com/career/data-analyst/salaries

Based on Location

LocationAverage Annual Salary
San Francisco, CA$90,000
New York, NY$76,000
Seattle, WA$81,000
Chicago, IL$72,000
Boston, MA$72,000
Austin, TX$74,000

Based on Industry

IndustryAverage Annual Salary
Finance$91,000
Technology$87,000
Healthcare$78,000
Retail$70,000
Manufacturing$68,000
Source: https://www.payscale.com/research/US/Job=Data_Analyst/Salary

How Can AI/ML BlackBelt Plus Program Help You?

The AI/ML BlackBelt Plus Program offers a comprehensive path for individuals aspiring to become data analysts in the USA. This program encompasses a well-rounded curriculum that covers every essential aspect of data analysis, from data collection and cleansing to analysis, visualization, and presentation. It ensures that participants understand data analytics deeply, making them job-ready in this high-demand field.

How to Become Data Scientist in USA

One of the program’s strengths lies in its emphasis on practical experience. Participants work with real-world datasets and engage in hands-on projects, bridging the gap between theory and application. This practical exposure is invaluable for honing their analytical skills and preparing them to tackle real challenges in the workplace.

Technical proficiency is a cornerstone of data analysis, and the program equips learners with essential skills, including programming languages like Python and R, SQL for data manipulation, and data visualization tools. Moreover, it doesn’t just stop at technical skills but also underscores effective communication’s importance.

Ultimately, the AI/ML BlackBelt Plus Program provides a structured and supportive environment that equips individuals with the skills, knowledge, and resources needed to excel as a data analyst in the USA.

Conclusion

To become a data analyst in the USA, start with a foundational education in data analysis. Then, build technical skills in programming, statistics, and data visualization. Gain practical experience through real data projects and create a strong portfolio. Develop communication skills for effective data presentation. Begin your job search by applying for entry-level positions and internships, even if you don’t meet all qualifications. Keep learning and staying updated with industry trends, and consider certifications or advanced degrees for career growth. This structured approach will pave the way for a successful career as a data analyst in the USA.

We wish you the best of luck in becoming a data analyst in the USA!

Frequently Asked Questions

Q1. Are data analysts in demand in the USA?

Ans. Yes, data analysts are in demand in the US across various industries.

Q2. How much is a data analyst paid in the USA?

Ans. According to Glassdoor, a data analyst’s average salary is $77,537 per year in the United States. The average additional cash compensation for the role is $5,607, with a range from $4,205 – $7,849.

Q3. How do I become a data analyst in the USA?

Ans. To become a data analyst in USA, you must acquire a relevant education and develop technical programming & data analysis skills. You must then gain practical experience through projects and build a portfolio to apply for entry-level positions. Continuous learning and staying updated in the field will help you become a better data analyst over time.

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.

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