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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 Change Career from Mechanical Engineer to Data Scientist?

Nitika Sharma
Nitika Sharma Last Updated : 23 Oct, 2024
6 min read

Data science has transformed the world and changed how we approach problem-solving. Given the high demand for data professionals, lucrative salaries, and growing career paths, professionals from different fields want to pivot their careers to data science. We can say the same for mechanical engineers. Transitioning from mechanical engineer to data scientist can be exciting, requiring individuals to bridge the gap between their engineering expertise and the intricacies of data analysis, machine learning, and programming.

It entails not only acquiring new technical skills but also developing a fresh mindset that embraces the power of data to drive informed decision-making. This article will shed light on how to transition your career from mechanical engineer to data scienctist!

Table of contents

At first look, data science and mechanical engineering appear to be separate professions, although they are interconnected in various ways. In the age of digital transformation, incorporating data science concepts and approaches into mechanical engineering has become increasingly important. Here are some examples of how data science and mechanical engineering are related:

Predictive Maintenance

Predictive maintenance is an important area of concentration in mechanical engineering, and data science is essential to it. Data scientists can create prediction models to spot probable faults or maintenance needs in advance by analyzing vast amounts of sensor data gathered from equipment. This proactive strategy assists in minimizing downtime, optimizing maintenance schedules, and enhancing mechanical systems’ overall effectiveness and dependability.

Design Optimization

The design of mechanical systems and components can be improved using data science techniques like statistical modeling, simulation, and machine learning. Engineers can acquire insights into design parameters, spot bottlenecks, and optimize mechanical systems to improve performance, sturdiness, and energy efficiency by analyzing data from simulations, real-world tests, and historical performance.

Performance Monitoring and Analysis

Mechanical engineers can now track and evaluate the performance of complicated systems in real-time thanks to data science. Using sensor data, engineers can learn a lot about how systems behave, spot abnormalities, and improve performance. Engineers may make informed judgements on system operation, upkeep, and enhancements using this data-driven method.

Computational Fluid Dynamics (CFD)

CFD, a mechanical engineering branch that analyzes fluid flow and heat transfer, makes heavy use of data science approaches. Data scientists use simulations, algorithms, and numerical methods to model and examine the behavior of fluids. Thanks to data science techniques, engineers can collect valuable data from CFD simulations, validate models, and improve designs.

Big Data Analysis

Big data has emerged due to the expanding availability of sensor data, IoT devices, and automation systems in mechanical engineering. Data science is essential when handling, analyzing, and drawing conclusions from huge and complicated datasets. Using big data analytics approaches, mechanical engineers can find patterns, trends, and correlations that aid decision-making, system optimization, and process improvements.

Automation and Robotics

In mechanical engineering, automation and robots are essential to data science and machine learning. These innovations make it possible to create intelligent systems that can learn from data, adjust to shifting circumstances, and make independent judgements. Robotic systems are trained using data science methods to carry out jobs, navigate settings, and communicate with people effectively.

Checkout: From Petroleum Engineering to Data Science: Jaiyesh Chahar’s Journey

How to Plan Career Change from Mechanical Engineer to Data Scientist?

Here are some points to keep in mind while transitioning your career from mechanical engineering to data science:

Assess your Skills and Identify Knowledge Gaps

Start by assessing your current skill set and determining areas where you need to learn new information. Data science needs knowledge of statistical analysis, machine learning algorithms, data visualization techniques, and programming skills in languages like Python or R.

Gain Knowledge Through Online Resourses

The basics of how to get into data science can be studied flexibly and comfortably through online courses. Look for trustworthy websites that provide in-depth data science courses created especially for workers changing careers. Topics like programming, statistics, machine learning, and data analysis are frequently included in these classes.

Build a Strong Foundation in Programming

An essential skill in data science is programming. Since they are both frequently used in the sector, start by studying Python or R. Learn the common data formats, libraries, and syntax used in data science projects. You may hone your programming abilities by participating in online tutorials, coding competitions, and practice projects.

Gain Practical Experience through Projects

Work on practical data science projects as you advance your education. These assignments give you practical experience while showcasing your abilities to potential employers. Consider working with others to generate data-driven solutions to mechanical engineering data challenges.

Networking

Participate in online forums, meetups, and social media platforms to communicate with data science experts. Networking can lead to employment openings, mentorship, and insightful advice from seasoned professionals. To increase your knowledge and visibility, participate in debates, share your work, and ask for feedback.

You can join our Data Science Community to connect with more than 2 million users that belong to the field of data science. Here’s your chance to connect, converse and thrive!

How can an Online Course Help you Switch?

Wondering how to become data analyst with no experience or how to become a data scientist without a degree? Online courses can be your one-stop solution. 

Online courses offer numerous advantages for individuals transitioning into a new field, like data science. They provide a structured curriculum, industry-relevant content, and the flexibility to learn at your own pace. Furthermore, online courses often have dedicated career support services to assist you in job placements, resume building, and interview preparation. An online course demonstrates your commitment to learning and upgrading your skills, which can significantly boost your chances on how to get a data science job.

Explore our AI/ML Blackbelt Plus program – the ultimate example of a transformative course for career transitions. We’ve witnessed numerous learners successfully shift from diverse fields to data science through our simplified videos, personalized mentorship, guided projects, assignments, and dedicated job assistance. Experience the best of all worlds with us on your journey to becoming a data science expert.

Discover more about our program here!

Different Data Science Careers

Data science offers a diverse range of career paths. Here is a glimpse of various roles and responsibilities within the field:

Data Science RolesJob Description
Data ScientistAnalyzing large data sets and deriving actionable insights for decision-making.
Data AnalystCollecting and interpreting data for decision-making and providing insights.
Machine Learning EngineerDeveloping machine learning algorithms and models to solve complex problems.
Data EngineerDesigning and maintaining the infrastructure to store, process, and analyze large datasets.
Business AnalystAnalyzing business data and providing insights to improve operations and decision-making.
Data ArchitectDesigning and implementing the overall structure and framework for data systems and databases.
StatisticianApplying statistical methods to analyze data and derive meaningful conclusions.
Data Visualization ExpertCreating visual representations of data to communicate insights and trends effectively.

Also Read: Step by Step Guide to Become a Data Scientist

Conclusion

Transitioning from a mechanical engineer to a data scientist is an exciting and transformative journey. By leveraging transferable skills, acquiring new technical proficiencies, and embracing the power of data-driven decision-making, individuals can successfully navigate this career shift. While challenges may arise, the possibilities for growth and impact in data science are vast. By embarking on this path, mechanical engineers can unlock new opportunities, contribute to cutting-edge advancements, and shape the future of data-driven innovation. So, embrace the change, persist in learning, and embark on this rewarding career transition to become a skilled and successful data scientist.

You can kickstart your journey by taking up our courses.

Frequently Asked Questions

Q1. How do you get a data analyst job as a fresher after mechanical engineering?

A. To secure a data science job, focus on acquiring relevant skills through online courses, practical projects, and networking. Tailor your resume to highlight transferable skills and showcase your data science projects during interviews.

Q2. Can I switch from engineering to data science?

A. It is possible to change from engineering to data science. Many technical abilities may be used in data science, and with the proper education and work experience, you can successfully shift from engineering to data science. Explore our AI/ML Blackbelt Plus program to get the relevant skills.

Q3. Why should I switch from mechanical engineer to data scientist?

A. Data science offers exciting opportunities for growth, higher salaries, and the chance to work on cutting-edge technologies. It allows you to leverage your existing analytical skills while exploring new domains.

Q4. Can I switch my career to data science without a degree?

A. Yes, a degree in data science is only sometimes a prerequisite for a career in the field. Employers often prioritize skills and practical experience over formal education. Demonstrating your proficiency through projects and certifications can be equally valuable. Explore our Data Science Learning Path to know more.

Q5. Who earns more, data scientist or mechanical engineer?

A. On average, data scientists tend to have higher earning potential, but salaries can vary based on experience and location.

Q6. Can mechanical engineer become data scientist?

A. Yes, a mechanical engineer can become a data scientist. They possess strong analytical skills and a solid math foundation. By learning programming, statistics, and machine learning through courses or bootcamps, they can transition into data science roles.

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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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