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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

Top 10 Data Science Job Profiles for the Future

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 01 Nov, 2023
7 min read

Introduction

Have you ever wondered what the future holds for data science careers? Yes, you are guessing it right– endless opportunities. Data science has become the topmost emerging field in the world of technology. There is an increased demand for skilled data enthusiasts in the field of data science. Its potential rewards and benefits to the career are not something you would want to miss. Whether you are entering the field or looking for newer profiles, this article will help you know about the top-notch data science job profiles for a great career investment and bright future.

Table of Contents

Top 10 Data Science Job Profiles

Being a fresher, choosing the right field becomes crucial and hectic at the same time. But you are at the right place to find the right data science job profiles that can best fit your future endeavors.

1. Data Scientist

Massive and complex data are collected, observed, and interpreted by the data scientists. Data scientists are a blend of mathematicians, computer experts, scientists, and statisticians. People who have an interest in data analysis can choose the field to make their future shine.

Key Responsibilities of Data Scientists

  • Discovering data sources
  • Automating data collection procedures
  • Analyzing information based on trends and patterns
  • Works on data pre-processing on unstructured and structured data
  • Generates predictive models
  • Develops machine learning algorithm

Average Salary: A data scientist earns $135,310 per annum.

2. Data Analyst

Huge systems and databases are monitored and maintained by data analysts. They are also specialists in rectifying errors. Data analysts manipulate data in a way that makes complex data understandable for non-technical people. These experts use statistical tools to evaluate, understand, and simplify bulky data. A data analyst must possess analytical and leadership skills and have the ability to keep an eye on the latest trends and patterns for predictive and diagnostic analytics.

Data analysis | data science job profiles

Key Responsibilities of Data Analysts

  • Use a statistical approach to visualize and produce reports
  • Collects and maintains massive data in simplified form
  • Generates and deploys data collection system
  • Responsible for A/B testing analysis
  • Performs web analytics tracking, such as the latest trends and patterns
  • Assess, evaluate, and understand patterns and trends in complex and bulky datasets
  • Prepares data for business communication

Average Salary: A data analyst earns $78,511 per year.

3. Data Engineer

Data engineers play a significant role in data science team operations. They are important as they create, perform testing, and regulate big data ecosystems optimized for data scientists and businesses. Data engineers aid in running algorithms smoothly. Furthermore, they collect the bulky data and match its format with the stored data. The optimized dataset is the key contribution of the data engineers that streamlines the tasks of data scientists and analysts.

Key Responsibilities of Data Engineers

  • Creates and Optimize data sets for data scientists and business
  • Built prototypes and algorithms to convert data into valuable insights
  • Suggests improvements to enhance the quality and reliability of the dataset and models
  • Develops pipelines and data system that make evaluation, reporting, and utilization of data more accessible and effective

Average Salary: A data engineer earns $136,707 per annum.

4. Data Architect

This data science job profile is quite related to data engineers. Data architects ensure that the data provided to the data scientists and analysts is accessible, appropriate, and well-formatted. Data architects design and manage an organization’s data infrastructure. They derive a strategy to centralize, safeguard, and maintain data flow and quality standards.

Key Responsibilities of Data Architects

  • Deliver a framework for replicating organizational huge data accurately
  • Create and implement data strategies in accordance with business goals and objectives
  • Monitoring and supervising data migration and implementation
  • Assuring data safety and efficacy of the database system
  • Overseeing and taking part in end-to-end architecture from designing to implementation

Average Salary: A data architect earns $135,779 per annum.

5. Machine Learning Scientist

When there is a role called scientist, it conveys that the job role majorly involves in-depth research to create effective algorithms and draw valuable insights. Machine learning scientists play a vital role in performing research to develop new approaches, from the manipulation of data to designing and implementing the latest algorithm.

Machine learning scientists are a crucial part of the R&D team, and their approaches and tasks generate valid publications. It indicates that ML scientists’ jobs are in academia instead of industrial work.

ML scientist | data science job profiles

Key Responsibilities of Machine Learning Scientists

  • Design and implement adaptive algorithms and models that acquire AI systems
  • Create autonomous AI software
  • Conduct tests to assess whether the software is working effectively and offering accurate predictions
  • Use data to enhance the performance of the algorithm and brief predictions

Average Salary: An ML scientist earns $158,229 per year.

6. Machine Learning Engineer

Machine learning engineers are highly paid professionals in today’s world of data. They are technically skilled programmers. They perform intense research, design, and generate automated prediction models. With each operation, models produce more accurate results by learning from the outcomes.

Key Responsibilities of Machine Learning Engineers

  • Designs and develops machine learning systems
  • Chooses the most suitable method of data illustration
  • Performs in-depth research and implementation of ML algorithms and tools
  • Builds data pipelines and effective datasets and models
  • Executes effective training programs and deploys ML models
  • Consistent assessment and tracking of ML system performance and reliability to refine it with each outcome

Average Salary: An ML Engineer earns $140,180 per year.

7. Business Intelligence Developer

Business intelligence developers or BI developers are amongst the top data science jobs in today’s world. They are responsible for designing strategies that help businesses discover potential data and make informed decisions in a short period of time. A BI developer works with business intelligence tools to provide business insights and analytics.

BI developer | data science job profiles

Key Responsibilities of Business Intelligence Developers

  • Develops, organizes, and maintains business interface
  • Provides valuable business insights
  • Hands-on with data querying tools to extract information from the users
  • Performs data visualization and delivers impromptu or regular reports
  • Examine data to provide an outline

Average Salary: $101,478 per year is the average salary for a Business Intelligence Developer.

8. Database Administrator

Organizations design and develop databases based on business requirements. To manage the database system, database administrators are hired. It is one of the most demanding data science job profiles in the business world. A database administrator tracks the database and ensures its proper functioning. In addition, they monitor data flow, generate backup and recoveries, and safeguard the datasets.

Key Responsibilities of Database Administrators

  • Comfortable with using database software to evaluate methods of storing, organizing, and managing datasets.
  • Responsible for keeping the database upgraded
  • Performs debugging of datasets
  • Assists in designing and generating databases
  • Provide strict security measures and backup process
  • Find and acknowledge errors in databases and resolve the issue to ignore bottlenecks

Average Salary: $94,541 per year is the average salary for a Database Administrator.

9. Business Analyst 

Unlike other data science job profiles, business analysts are hired to work with information technology (IT) and business operations teams. They play a vital role in processing, assessing, and documenting the business data for products, services, and procedures. They help find actionable business insights for the growth and development of the business.

Key Responsibilities of Business Analysts

  • Conduct research to evaluate all the aspects of the business model to improve the system
  • Lias critical information and valuable insights from stakeholders and distinct departments to collect data and provide a logical conclusion
  • Create innovative solutions for complex business problems and improve productivity with strategic enhancements
  • Use their statistical methodologies for analytics and conceptual thinking and deliver accurate outcomes
  • Expertise in allocating resources, forecasting, and budgeting in business

Average Salary: $91,372 per year is the average salary for a Business Analyst.

10. NLP Engineer 

Natural Language Processing engineers, or NLP engineers, are one of the most in-demand data science job profiles today. They are experts in information science, artificial intelligence, and computer science, along with linguistics skills. Their knowledge, skills, and experience are employed to develop programs that understand human language.

Key Responsibilities of NLP Engineers

  • Find and apply the best tools and correct algorithm for NLP tasks
  • Asses and converts data science prototypes
  • Choose valuable annotated datasets that administer learning methods
  • Design natural language processing applications
  • Perform efficient text illustrations to convert natural language into potential features

Average Salary: $119,412 per annum is the average salary for NLP Engineers.

Conclusion

The data science job profiles mentioned above are among the most demanding data science careers in the evolving world. Their job responsibilities require strong background knowledge, skills, and experience to excel in the field.

For any candidate willing to take a step ahead for a successful career in data science, Analytics Vidhya brings forth the AI & ML BlackBelt Plus Program. Besides knowledge and teachings from leading experts from the industry, this BlackBelt Plus program also offers on-demand doubt-clearing sessions. Embark on a comprehensive and personalized learning path today!

Frequently Asked Questions

Q1. Name some companies that hire machine learning engineers.

Ans. Databricks, IBM, Google, Amazon, AWS, and Accenture are some of the well-known companies that hire machine learning engineers.

Q2. What tools or skills are required for data scientist jobs?

Ans. Data scientists must be proficient in programming languages such as Python, R, and JavaScript. They must have hands-on experience with data visualization tools like Tableau, D3, JS, Matplotlib, and so on. In addition, a data scientist must possess a good knowledge of ML algorithms and applied statistical skills.

Q3. Can I switch careers from business analyst to business intelligence developer?

Ans. Yes. A business analyst is among the types of data science jobs. If you want to transition your career from data analyst to business intelligence developer, you need to learn about technologies and tools specific to the latter. Your expertise in tools and strong knowledge of the related background increase your chances of landing a job as a business intelligence developer.

Business Analystbusiness intelligence developercareerData Analystdata architectdata engineerdata sciencedata science careerdata science jobsdata scientistdatabase administratorjobjob profileslisticlemachine learning engineermachine learning scientistML engineerML scientistNLP Engineerresponsibilitiestools
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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How Can You Build a Career in Data Science and Machine Learning?
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