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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

Data Scientist vs Data Analyst: Which is a Better Career Option to Pursue in 2023?

Nitika Sharma
Nitika Sharma Last Updated : 09 May, 2023
6 min read

Are you a data enthusiast looking to break into the world of analytics? The field of data science and analytics is booming, with exciting career opportunities for those with the right skills and expertise. But with so many job titles and buzzwords floating around, figuring out which path to pursue can be challenging. So, let’s settle the age-old debate: Data Scientist vs Data Analyst – which is the better career option to pursue in 2023? This article will explore the differences between the data science and data analytics, the skills required for each role, and the job prospects in 2023.

Table of contents

What is a Data Scientist?

A Data Scientist is a professional who uses statistical and computational methods to extract insights and knowledge from data. They analyze, visualize, and model data to help businesses make informed decisions. They have a strong math, statistics, and programming background and are skilled in machine learning and data visualization.

Source: Analytics Vidhya Youtube Channel

What is a Data Analyst?

A Data Analyst is a professional who uses data to answer questions and solve problems for businesses. They collect, clean, and organize data and then analyze it to identify patterns and trends. They use data visualization tools to present findings and provide insights to help businesses make data-driven decisions.

Data Scientist vs Data Analyst

CategoryData ScientistData Analyst
Main RoleUse statistical and computational methods to extract insights from data and build predictive models.Analyze data and provide insights to help businesses make data-driven decisions.
SkillsStrong background in math, statistics, and programming. Skilled in machine learning, data visualization, and big data tools.Proficient in data collection, data cleaning, data analysis, and data visualization. Skilled in Excel, SQL, and Tableau.
Job ResponsibilitiesCollect and analyze large and complex data sets. Develop algorithms and predictive models. Create and interpret data visualizations.Collect, clean, and organize data. Analyze data using statistical methods. Create data visualizations and dashboards.
Industry FocusTypically found in industries such as tech, finance, healthcare, and marketing.Found in a wide range of industries, including finance, marketing, healthcare, and retail.
EducationTypically requires a Master’s or PhD in a quantitative field such as statistics, mathematics, or computer science.Usually requires a Bachelor’s or Master’s degree in a quantitative field such as mathematics, statistics, or computer science.
Salary Range (US)$78,000 – $171,000 per year$50,000 – $97,000 per year

Let’s explore the difference between data scientist and data analyst based on different components.

Education Qualifications

Data Scientist vs Data Analyst
Source: Data Science Process Alliance

Data Scientist

  • Typically requires a Master’s or PhD in a quantitative field such as statistics, mathematics, computer science, or engineering.
  • Strong background in math and statistics, with knowledge of machine learning, data mining, and big data technologies.
  • Proficiency in programming languages such as Python, R, and SQL and data visualization tools such as Tableau and PowerBI.

Data Analyst

  • Usually requires a Bachelor’s or Master’s degree in a quantitative field such as mathematics, statistics, computer science, or economics.
  • Strong analytical skills, with knowledge of statistical methods and data visualization techniques.
  • Proficiency in data manipulation tools such as Excel, SQL, and SAS and data visualization tools such as Tableau and PowerBI.

What are the Responsibilities of a Data Analyst and Data Scientist?

Data Scientist

  • Use logical forecasting for insights and generating strategies for ad performance, customer behavior and preferences, targeting the ads and other similar parameters
  • Generate A/B testing frameworks for evaluating the performance of different features 
  • Use the unstructured data to collect information
  • Create algorithms and data models specific to the industry and business 
  • Improve service, brand reach and efficiency of the business
  • Develop tools to monitor and optimize the business performance and strategies 
  • Evaluate the data source and identify its position in statistical and customer models.

Data Analyst

  • Analyze the data and interpret it in a user-friendly format 
  • Find actionable information for the data for decision-making
  • Analyze the figures associated with sales and their change and performance rate 
  • Find problematic parts or scenarios from such figures
  • Help in business improvement 
  • Build an algorithm for understanding the customer-behavior and their expectations
  • Check KPIs to find room for improvement and overall performance 
  • Import data from the warehouse for tracking the business production and improving the customer service.

Data Scientist vs Data Analyst: Technical Skills and Tools Required

Data Scientist

  • Strong programming skills in languages such as Python, R, and SQL
  • Familiarity with big data tools such as Hadoop, Spark, and Hive
  • Knowledge of machine learning algorithms and frameworks, such as scikit-learn, TensorFlow, and Keras
  • Proficiency in data visualization tools such as Tableau, PowerBI, and matplotlib
  • Experience with cloud platforms such as AWS, Azure, and Google Cloud Platform
  • Ability to work with unstructured and semi-structured data, such as text, image, and video data

Data Analyst

  • Proficiency in data manipulation tools such as Excel, SQL, and SAS
  • Knowledge of statistical methods and data visualization techniques
  • Familiarity with data visualization tools such as Tableau, PowerBI, and ggplot2
  • Experience with data cleaning, transformation, and quality assurance
  • Ability to work with structured data, such as relational databases and spreadsheets

 Salary and Career Growth Potential

Data Scientist

  • The median salary for a Data Scientist in the United States is around $118,000 per year according to Glassdoor.
  • Data Scientists have a high career growth potential, with opportunities to move into management roles or specialize in specific areas such as artificial intelligence or data engineering.

Data Analyst

  • The median salary for a Data Analyst in the United States is around $68,000 per year according to Glassdoor.
  • Data Analysts have a moderate career growth potential, with opportunities to move into management roles or specialize in specific areas such as business intelligence or data visualization.

Which Career is Better for You?

Self-assessment of Personal Skills and Interests

The difference between the role of data scientist and data analyst lies largely on the technical level while handling and performing an action on the data. People with good core mathematical, statistical and programming skills looking forward to competitive environments can choose the Data scientist profession. The candidates focused on data analysis, predictive analysis, passion for creative visualization, and good communication skills may find themself inclined toward a data analyst role. 

Comparison of Job Duties and Responsibilities with Personal Goals

Suppose the personal goal of candidates is to learn more advanced techniques, focus on theoretical and application parts, build tools, and gain higher education. In that case, the actions of data scientists will align. While business prefers candidates who like optimizing growth opportunities for the company, and candidates who find themself more inclined towards customer behavior and understanding their preferences would like data analyst job roles. 

Evaluation of Salary and Career Growth Potential

The job profiles of data analysts and data scientists offer increasing growth potential. The salary constraints and the will to reach a specific point of a career in a certain period of time can specifically act as deciding factors for choosing the career. 

Data Scientist vs Data Analyst – Final Verdict

Which career path is right for you: data scientist or data analyst? While both roles share some similarities, there are significant differences in skills, responsibilities, and career growth opportunities. As we have seen, becoming a data scientist requires a strong foundation in math, statistics, and programming and expertise in machine learning, big data, and cloud computing. On the other hand, becoming a data analyst requires proficiency in data analysis tools and techniques, data visualization, and data storytelling.

Ultimately, the choice between data scientist and data analyst comes from your interests, skills, and career goals. Both roles offer exciting opportunities to work with data and make meaningful contributions to businesses and society.

If you’re interested in pursuing a career in data science or data analytics, we encourage you to check out our Full Stack Data Scientist course. This comprehensive training program covers everything from data collection and preprocessing to machine learning and deep learning, as well as real-world projects and case studies. Start your journey today!

Frequently Asked Questions

Q1. Is it easier to become a data analyst or data scientist?

A.  It depends on individual skills, interests, and experience. Both fields require specific technical skills, critical thinking, and problem-solving abilities. However, becoming a data analyst may require a shorter learning curve than a data scientist.

Q2. What’s the difference between a data analyst and a data scientist?

A. A data analyst is responsible for collecting, cleaning, and analyzing data to identify trends, patterns, and insights that can be used to make business decisions. A data scientist, on the other hand, not only performs the tasks of a data analyst but also has a deep understanding of statistical and machine learning techniques to build predictive models and drive actionable insights.

Q3. Is Python enough for data science?

A. Python is a powerful data science programming language widely used for data analysis, machine learning, and deep learning. However, it is not the only programming language used in data science. Other languages like R, SQL, and Java are also used for specific tasks in data science.

Q4. Is a data scientist above a data analyst?

A. A data scientist is considered a more advanced role than a data analyst. A data scientist typically has a more in-depth knowledge of machine learning algorithms, predictive modeling, and programming languages. However, both roles are equally important in the field of data science.

Q5. Does data analysis require coding? 

A. Yes, data analysts need to have a basic understanding of programming languages like SQL and Python to clean, manipulate, and analyze data efficiently.

Q6. Who earns more data scientists or MBA? 

A. It is difficult to compare the salaries of data scientists and MBAs as they are two fields with different job roles and responsibilities. However, data scientists are in high demand and have higher salaries than data analysts or MBAs.

Q7. Is Python a must for data analysis?

A.  Python is not a must for data analysts, but it is a useful skill to have in data analysis. A data analyst can also use Excel, SQL, and Tableau for data analysis and visualization.

careerdata analysisData Analystdata sciencedata science careersdata scientist
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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