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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 In-Demand Data Tech Roles in Data Science

Yana Khare
Yana Khare Last Updated : 18 Jul, 2024
7 min read

Introduction

Data science’s abilities are so versatile that they open up various job alternatives.

Overview:

  • Learn about the top data science alternative career paths.
  • Gain an understanding of the key skills required for each role.
Top 10 Data science alternative career path.

Table of contents

Top Data Science Career Paths

1. Data Engineering

Data engineers play a crucial role in every data-focused company. They plan, build, set up, and maintain significant processing systems. They facilitate efficient task completion by ensuring that data is present, dependable, and conveniently reachable for data scientists and analysts. This position concentrates on the design and framework required to sustain significant data initiatives.

Key Skills:

  • and Kafka
  • Proficiency in programming languages like SQL, Python, and Java.
  • Knowledge related to big data technologies such as Hadoop and Spark.
  • Strong problem-solving skills and attention to detail.

Salary:

According to a summary of various sources, the average total annual pay for a Data Engineer is around $111,998, with senior data engineers earning significantly more​.

Educational background:

Bachelor’s degree in Computer Science, Information Systems, or related field.

Also Read: Step-by-Step Roadmap to Become a Data Engineer in 2024

2. Business Intelligence (BI) Analyst

BI analysts play a crucial role in connecting data with decision-making processes. They examine information to offer practical insights that influence strategic corporate choices. This position requires designing dashboards, reports, and visualizations to convey findings effectively to stakeholders.

Key Skills:

  • Strong understanding of BI tools like Tableau, Power BI, or Looker
  • Ability to translate complex data into understandable insights
  • Knowledge of SQL for data querying
  • Excellent communication and storytelling skills
  • Advanced Excel skills.
  • Familiarity with database management systems.

Salary:

As of 2024, the average salary for a BI Analyst in the United States is approximately $87,560 per year. Salaries typically range from $72,729 for those with less than a year of experience to $107,956 for those with over seven years of experience.

Educational background:

Most BI Analysts have a bachelor’s degree in fields such as Data Science, Mathematics, Statistics, Computer Science, Information Technology, Business Intelligence, or a related field.

3. Machine Learning Engineer

Machine learning engineers, , focuses on developing, applying, and managing machine learning algorithms. They develop efficient algorithms for production systems.

Key Skills:

  • Proficiency in programming languages such as Python, R, or Java
  • Deep understanding of machine learning algorithms and frameworks like TensorFlow or PyTorch
  • Experience with model deployment and monitoring
  • Knowledge of software engineering principles
  • Strong analytical thinking and creativity.
  • Familiarity with cloud services like AWS and Azure.
  • Experience with tools like Scikit-Learn, Keras, and Jupyter Notebooks.

Salary:

The average salary for a Machine Learning Engineer in the US is $155,888, with additional cash compensation averaging $41,074, bringing the total compensation to $196,962.

Educational background:

A bachelor’s degree in computer science, mathematics, or a related field is often the minimum requirement for a Machine Learning Engineer. However, a master’s or Ph.D. can provide a deeper understanding of the complex concepts involved.

Also Read: 10 Must Have Machine Learning Engineer Skills in 2024

4. Data Architect

Key Skills:

  • Expertise in data modeling and database design
  • Knowledge of data governance and data management
  • Familiarity with data integration and data migration techniques
  • Familiarity with big data technologies like Hadoop.
  • Strong strategic thinking and project management skills.
  • Experience with tools like Oracle, SQL Server, and AWS.

Salary:

The average salary for a Data Architect in the US is $145,374, with additional cash compensation averaging $42,533, bringing the total compensation to $187,907.

Educational background:

A bachelor’s degree in computer science, computer engineering, or a related field is typically required to become a Data Architect.

5. AI Product Manager

Key Skills:

  • Understanding of AI and machine learning concepts
  • Experience in product management and agile methodologies
  • Ability to communicate technical details to non-technical stakeholders
  • Strategic thinking and project management skills
  • Strong leadership and strategic thinking skills.
  • Proficiency in communication and project management.
  • Experience with product development and Agile methodologies.
  • Familiarity with tools like Jira and Confluence.

Salary:

The average salary for an AI Product Manager in the US is around $144,167, with potential earnings reaching over $200,000 for those with 10+ years of experience.

Educational background:

A background in computer science, business administration, or a related field is often preferred for AI Product Managers. However, a strong understanding of AI, product management principles, and business acumen are crucial, regardless of the specific degree.

Also Read: How to Become a Product Analyst in 2024?

6. Data Privacy and Ethics Specialist

Key Skills:

  • Knowledge of data privacy laws and regulations like GDPR or CCPA
  • Understanding of ethical issues related to data usage
  • Ability to develop and implement data governance policies
  • Strong communication and advocacy skills
  • Familiarity with encryption tools like VeraCrypt, AxCrypt and BitLocker etc.

Salary:

The average salary for a Data Privacy and Ethics Specialist in the US ranges from $80,000 to $150,000 annually, with factors like experience, industry, and location influencing the final figure.

Educational background:

A Data Privacy and Ethics Specialist typically requires a degree in law, computer science, information technology, or a related field. Additionally, certifications like Certified Information Privacy Professional (CIPP) can enhance career prospects.

7. Quantitative Analyst (Quant)

Key Skills:

  • Strong background in mathematics, statistics, and finance
  • Proficiency in programming languages like Python, R, or MATLAB
  • Experience with financial modeling and risk management
  • Analytical thinking and attention to detail
  • Experience with tools like MATLAB, Excel, and SQL.

Salary:

The average salary for a Quantitative Analyst in the US is $110,659 with a typical range between $97,810 and $124,668.

Educational background:

A bachelor’s degree in mathematics, statistics, computer science, engineering, or economics is typically required to become a Quantitative Analyst. However, a master’s degree in quantitative finance or financial engineering is often preferred for advanced roles.

Also Read: How to Become a Quantitative Analyst?

8. Data Analyst

Data analysts

Key Skills:

  • Proficiency in data analysis tools like Excel, Tableau, Power BI.
  • Good experience in SQL, and Python
  • Statistical solid knowledge and analytical thinking
  • Ability to create detailed reports and data visualizations
  • Strong communication skills to present findings to stakeholders

Salary:

The average salary for a Data Analyst in the US is $84,217 with additional cash compensation of $42,142, bringing the total compensation to $126,359.

Educational background:

A bachelor’s degree in statistics, computer science, economics, or a related field is typically required to become a Data Analyst.

Also Read: Learning Path to Become a Data Analyst in 2024

9. Data Visualization Specialist

Key Skills:

  • Expertise in data visualization tools such as Tableau, Power BI, or D3.js
  • Robust design and storytelling skills
  • Ability to translate complex data into clear and compelling visuals
  • Understanding of user experience (UX) principles
  • Strong creativity and communication skills.
  • Attention to detail and storytelling ability.
  • Experience with tools like Tableau, Power BI, and Google Data Studio.

Salary:

The average salary for a Data Visualization Specialist in the US is around $107,829, with a typical range between $94,231 and $122,633.

Educational background:

A bachelor’s degree in computer science, statistics, graphic design, or a related field is a common educational path for becoming a Data Visualization Specialist. This provides a foundation in data analysis, visual communication, and technical skills.

10. Research Scientist

Researchers in data science usually work in business or academic settings, developing novel approaches, algorithms, and models. Their efforts result in advancements in data science fields such as artificial intelligence, machine learning, and others.

Key Skills:

  • Deep knowledge of machine learning and statistical modeling
  • Proficiency in programming languages like Python, R, or MATLAB
  • Strong analytical and problem-solving skills
  • Ability to publish and present research findings
  • Familiarity with tools like TensorFlow, PyTorch, and MATLAB.

Salary:

The average salary for a Research Scientist in the US is around $130,000 annually, with a range between $112,000 to $169,000.

Educational background:

A Ph.D. is typically required to become a Research Scientist. This advanced degree provides the in-depth knowledge and research experience necessary for this role. While a master’s degree can be sufficient for some positions, a Ph.D. is often preferred, especially in academic or high-level industry research.

Conclusion

Data science abilities are so versatile, they open up various alternate job alternatives. There is a job that will match your enthusiasm and talents, whether engineering, business analysis, product management, or ethics are your areas of interest. In the rapidly evolving area of data science, taking these many paths can lead to fulfilling and significant professions.

By delving into different career paths, professionals can discover fresh opportunities to utilize their data science expertise, stimulate creativity, and contribute significantly to their workplaces.

Frequently Asked Questions

Q1. What will replace data scientists?

A. Automation and advancements in AI may reduce the need for traditional data scientists. Roles like AI engineers and data engineers, focusing on building and maintaining AI systems, could become more prominent, blending data science skills with software engineering and machine learning expertise.

Q2. Is it good to switch careers in data science?

A. Switching to a career in data science can be highly beneficial due to its high demand, lucrative salaries, and the opportunity to solve complex problems across various industries. Strong analytical skills, curiosity, and proficiency in programming are essential for success in this field.

Q3. Is data science still a rising career?

A. Yes, data science remains a rising career. The increasing importance of data-driven decision-making and technological advancements drive demand for data science professionals, making it a promising field with numerous opportunities for growth and innovation.

Q4. Which field of data science is best?

A. The best field in data science depends on individual interests and career goals. Popular areas include machine learning engineering, data engineering, and business intelligence analysis. Each offers unique challenges and opportunities, with roles like AI engineers and data architects gaining prominence due to technological advancements.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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