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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

5 Best Data Science Certification in USA

Nitika Sharma
Nitika Sharma Last Updated : 23 Sep, 2023
6 min read

Introduction

In the United States, data science is a hotbed of opportunity. Glassdoor consistently ranks Data Scientist as one of the most sought-after roles for four years. The United States Bureau of Labor Statistics predicted data science jobs to grow at the rate of 35% by 2032. This article explores the top 5 data science certifications in USA, providing the knowledge and skills needed for success in this booming field. Whether you’re an experienced data pro or just beginning your data journey, these certifications offer a pathway to a thriving career in the data science revolution.

Table of contents

Importance of Data Science Certification in USA

Data science certification holds immense importance in the USA for several compelling reasons. Firstly, it validates your data science skills and knowledge, providing potential employers with tangible evidence of your expertise. In a competitive job market, certifications set you apart, increasing your chances of landing coveted data science roles.

Moreover, data science is a rapidly evolving field with many tools, techniques, and methodologies. Certifications offer structured learning paths, ensuring you stay updated with the latest advancements and best practices. They equip you with the practical skills to tackle real-world data challenges effectively.

In the USA, where data science job opportunities flourish, certifications act as a career accelerant. Many employers actively seek certified data professionals as these credentials indicate a commitment to excellence and a strong foundation in the field.

Furthermore, data science certifications enable career versatility. They are recognized across various industries, from finance to healthcare, allowing you to pivot into different sectors seamlessly.

Also Read: How to Become a Data Scientist in USA

Top 5 Data Science Certification in USA

  1. Certified AI ML Blackbelt+ Program
  2. Introductory Data Science for Business Managers
  3. Certified Machine Learning Master’s Program (MLMP)
  4. Top Data Science Projects for Analysts and Data Scientists
  5. Getting Started with Git and GitHub for Data Science Professionals

Certified AI ML Blackbelt+ Program 

This program offers a comprehensive array of courses, designed to transform you into a proficient AI and machine learning practitioner. Whether you’re a beginner or an experienced professional, this program equips you with essential skills, practical knowledge, and real-world projects.

The program covers critical topics, including data science and machine learning, deep learning, natural language processing (NLP), computer vision, data visualization with Tableau, Excel proficiency, SQL for data science, and interview preparation.

Why Choose the Certified AI & ML BlackBelt+ Program?

  1. Embrace the AI Revolution: As artificial intelligence continues to reshape industries and functions, upskilling in AI and machine learning is essential. This program provides the perfect opportunity to do just that.
  2. Accessible Content: The courses are meticulously designed for individuals from various backgrounds, ensuring easy understanding, even for beginners.
  3. Experienced Instructors: The program materials are created by instructors with extensive industry experience and decades of teaching expertise.
  4. Industry Relevance: All courses are vetted by industry experts, guaranteeing that the content remains pertinent in today’s fast-paced job market.
  5. Real-World Problem Solving: The program emphasizes practicality with projects modeled on real-world scenarios, enhancing your industry readiness.
How to Become Data Scientist in USA

Prerequisites

No prior knowledge of data science, machine learning, artificial intelligence, or any related tool is required.

Benefits of the Certified AI & ML BlackBelt+ Program

  • End-to-End Learning: The program starts from the basics and guides you through building a portfolio of industry-relevant projects.
  • Mentorship: Benefit from monthly 30-minute mentorship calls to track your progress and receive guidance.
  • Digital Certificates: Earn shareable digital certificates for each course and the entire program upon completion, enabled by blockchain.
  • 24-Month Access: Enjoy 24 months of complete access and support, allowing you to pace your learning and work on more projects.
  • Bulk Pricing: Save up to 30% compared to individual course purchases.
  • Money-Back Guarantee: A no-questions-asked money-back guarantee is available for 7 days.
  • Mentorship Sessions: Access over 105 mentorship sessions.
  • AIConf: Gain access to AIConf with over 65 hours of content.

Click here to explore this data science certification in USA. 

Introductory Data Science for Business Managers

The data science wave is reshaping industries and redefining decision-making. Data-driven insights are now the core of strategic planning for organizations. Are you ready to harness this transformation in your business and role? The demand for data science leaders and decision-makers is outstripping supply. While data science projects are flourishing, experienced managers to guide and mentor these initiatives are scarce.

Enter “Data Science for Managers,” a meticulously crafted program tailored for decision-makers. Whether you’re a manager, team leader, CxO, or entrepreneur, data science literacy is essential, and this program is your ideal learning path. The certified program offers access to three comprehensive courses:

  • Introductory Data Science for Business Managers
  • Artificial Intelligence and Machine Learning for Business Leaders
  • Introduction to Data Science

These courses equip you with fundamental data science knowledge, from Python and core statistics to essential machine learning algorithms. Additionally, you’ll master Tableau to transform data into actionable insights.

Why Enroll in this Certified Program?

  • Prepare for the AI Revolution: Artificial Intelligence is revolutionizing various industries and functions. Now is the time to upskill in AI and machine learning; this program provides the perfect opportunity.
  • Easy-to-Understand Content: Designed for individuals from diverse backgrounds, this program offers accessible data science education, assuming no prior technical knowledge.
  • Experienced Instructors: The program’s materials are created by seasoned instructors with extensive industry data science experience and over two decades of teaching expertise.
  • Industry Relevance: Industry experts have vetted all courses, ensuring content aligns with real-world demands.
  • Real-Life Problem Solving: The program’s projects mirror real-world scenarios, emphasizing industry relevance.

Click here to explore this data science certification in USA. 

Certified Machine Learning Master’s Program (MLMP)

The Certified Machine Learning Master’s Program (MLMP) stands out among today’s top data science certifications. This comprehensive program offers an opportunity to transform from a novice to a proficient machine learning expert through a carefully structured curriculum.

The MLMP encompasses over five courses, including essential topics like Python programming and advanced algorithms in machine learning. What truly sets this certification apart are the hands-on experiences it provides through 12+ real-life projects. These projects offer invaluable practical insights that help bridge the gap between theory and real-world application.

One of the core strengths of the MLMP is its focus on building a holistic skill set that makes you industry-ready. This includes fostering a structured thinking mindset for tackling complex data science challenges. The program also covers essential tools such as SQL for data extraction and exploratory data analysis for deriving valuable insights.

Additionally, the MLMP dives deep into the world of machine learning algorithms, from fundamentals to advanced applications. It equips you to deploy machine learning models in real-world production environments. Including time series forecasting models, recommender systems, and customer communication strategies makes this program well-rounded and aligned with industry demands.

Click here to explore this data science certification in USA. 

Top Data Science Projects for Analysts and Data Scientists

Our data science courses and projects offer a dynamic pathway into data science, featuring projects spanning machine learning, computer vision, deep learning, natural language processing, reinforcement learning, and data engineering. These projects are not just learning exercises but opportunities to apply your skills in real-world scenarios.

Practical experience is the key to success in data science, and our open-source projects provide precisely that. The question of how many data science projects you’ve completed often arises in interviews. Certifications alone don’t suffice; you need hands-on experience to complement your knowledge.

Our top data science projects encompass a broad spectrum, catering to beginners and advanced data science enthusiasts alike. These projects showcase your passion and curiosity, greatly enhancing your employability. Whether you aspire to become a data scientist, data analyst, or business analyst, or simply wish to refine your data science skills, our projects are your stepping stones to success.

All you need to embark on this journey is a functional laptop or desktop with 4 GB of RAM, a stable internet connection, and a basic understanding of Python or R. Elevate your data science career with our top data science projects and turn your passion into practical expertise.

Click here to explore this data science certification in USA. 

Getting Started with Git and GitHub for Data Science Professionals

Git and GitHub course for data scientists is your gateway to mastering version control, a critical skill in the data science domain. If you’re new to version control, this course provides an ideal starting point.

Git and GitHub are indispensable tools for data professionals. Understanding their purpose and functionality is essential for a successful data science career. These tools enable seamless collaboration, allowing team members to work independently on different project features while maintaining a stable codebase.

This course is designed for beginners, requiring no prior experience. It offers a comprehensive overview of Git and GitHub, emphasizing their practical application in data science projects. By the end of the course, you’ll have a valuable skill set to add to your data science portfolio: version control.

Don’t underestimate the importance of version control in data science. It streamlines your projects, making remote and independent work more efficient. Embrace Git and GitHub, and elevate your data science acumen to the next level.

Click here to explore this data science certification in USA. 

Conclusion

Pursuing a data science certification in the USA is a strategic career move. With data scientist roles consistently in high demand and the field’s exponential growth, certification is invaluable. These programs provide essential skills and hands-on experience, ensuring job readiness. Furthermore, online flexibility allows balancing studies with other commitments. A data science certification is an investment in professional growth, employability, and readiness for a rapidly evolving job market, making it a pivotal step toward a successful data science career in the USA.

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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