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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

What Can You Do with a Math Degree? [ Top 7 Career Options]

A
Analytics Vidhya Last Updated : 09 Jul, 2023
9 min read

A mathematics degree equips graduates with a versatile skill set that opens up several career opportunities. The analytical and problem-solving abilities developed through studying mathematics make math graduates highly sought after in various industries. In this article, we will explore key career paths for math graduates and shed light on a math degree’s valuable role in these fields.

Table of contents

Top 7 Careers for Math Graduates

Here are top 10 careers where you can excel with your maths knowledge:

1. Data Analyst

Data analysts play a crucial role in extracting valuable insights from vast amounts of data. They are responsible for collecting, organizing, and analyzing data to identify patterns, trends, and correlations. With their strong analytical and mathematical skills, data analysts are proficient in using statistical techniques and data visualization tools to present findings in a meaningful way. 

Data analysis
Source: Pexels

Role and Responsibilities

  • Data analysts collect, organize, and analyze large datasets to extract meaningful insights.
  • They develop and implement data models and algorithms for effective data analysis.
  • Data analysts clean and validate data to ensure accuracy and reliability.
  • They create data visualizations and reports to communicate findings.
  • Data analysts collaborate with stakeholders to understand business requirements and provide data-driven recommendations.

How a Math Degree can be Valuable in Data Analysis?

  • Degrees in mathematics give students a strong foundation in quantitative concepts and methods, such as statistical analysis, probability theory, and linear algebra.
  • These abilities are essential for decoding big datasets, finding patterns, and coming to data-driven conclusions.
  • Strong problem-solving skills, critical thinking, and logical reasoning are qualities that math graduates have, which are crucial for data analysis.

Resources to Build a Career in Data Analysis

2. Actuary

Actuaries specialize in assessing and managing risks in various industries, particularly insurance and finance. They use mathematical models and statistical analysis to evaluate the probability of future events and their potential financial impact. A math degree provides a solid foundation for actuaries, as it covers key mathematical concepts and develops skills in probability theory and statistical analysis.

Life of a Actuary
Source: The Balance

Role and Responsibilities

  • Actuaries assess and manage financial risks using their expertise in mathematics and statistics.
  • They analyze complex data sets to identify potential risks and quantify their likelihood and impact.
  • Actuaries develop mathematical models to simulate scenarios and make informed decisions on risk management strategies.
  • They calculate financial parameters such as insurance premiums, reserves, and pension benefits.
  • Actuaries apply actuarial principles to determine appropriate pricing for insurance policies and design sustainable retirement benefit plans.

How a Math Degree can be Valuable in Actury?

  • Since actuaries use probability theory, calculus, and statistics to evaluate risks and forecast future outcomes, math abilities are essential for the field of actuarial science.
  • To calculate the probability of specific events, such as accidents or natural disasters, they analyze past data.
  • Actuaries also employ mathematical models to assess the effects of various situations and forecast financial trends.

Resources to Build a Career in Actuary

3. Financial Analyst

Financial analysts play a vital role in assessing and interpreting financial data to guide investment decisions and financial strategies. With their strong mathematical foundation and analytical skills, individuals with a math degree possess a valuable toolkit for pursuing a career as a financial analyst.

Role and Responsibilities

  • Financial analysts analyze complex financial data to evaluate investment opportunities and assess potential risks.
  • They conduct research and use statistical analysis to forecast financial trends and performance.
  • Financial analysts provide insights and recommendations on investment portfolios, company valuations, and financial planning strategies.
  • They prepare reports and presentations to communicate financial analysis findings to stakeholders.
  • Financial analysts monitor economic and market trends to make informed predictions and adjust investment strategies accordingly.

How a Math Degree can be Valuable in Financial Analysis?

  • Mathematical concepts such as calculus, statistics, and probability theory provide a foundation for understanding financial models and analyzing market data.
  • Mathematics helps financial analysts assess risk, calculate returns, and analyze the performance of investment portfolios.
  • Strong mathematical and analytical skills enable financial analysts to make data-driven decisions and evaluate the financial health of companies.

Resources to Build a Career in Financial Analysis

4. Data Scientist

Data scientists are at the forefront of harnessing the potential of data to derive insights and drive decision-making across industries. With a math degree in hand, individuals possess a strong foundation in mathematical concepts and analytical skills that are highly relevant in data science.

data_scientist_role

Role and Responsibilities

  • Data scientists use their mathematics and statistical analysis expertise to extract valuable insights from complex datasets.
  • They develop and apply advanced machine learning algorithms to solve complex problems, uncover patterns, and make predictions.
  • Data scientists work closely with stakeholders to understand business needs and formulate data-driven solutions to drive growth and innovation.
  • They clean, preprocess, and transform data, ensuring its quality and suitability for analysis.
  • Data scientists communicate findings through visualizations, reports, and presentations, making complex information accessible and actionable for decision-makers.

How a Math Degree can be Valuable in Data Science?

  • Maths provides the necessary tools and techniques for statistical analysis, probability theory, and linear algebra, enabling data scientists to understand and manipulate data.
  • Mathematical modeling and optimization techniques form the backbone of machine learning algorithms and predictive modeling, allowing data scientists to extract insights and make accurate predictions.
  • It also fosters critical thinking, problem-solving, and logical reasoning skills essential in framing and tackling data-driven challenges.

Resources to Build a Career in Financial Analysis

5. Machine Learning Engineer

Machine Learning Engineers are key players in developing and deploying cutting-edge AI solutions. With a math degree, individuals possess a strong mathematical foundation and analytical skills that perfectly align with the requirements of this dynamic field.

what to do after math degree?
Source: Freelancermap

Role and Responsibilities

  • Machine Learning Engineers design, develop and implement machine learning models and algorithms to solve complex problems and enhance AI capabilities.
  • They work closely with data scientists and software engineers to transform prototypes into scalable and efficient production-ready systems.
  • Using their mathematical and statistical expertise, machine Learning Engineers optimize and fine-tune models for performance, accuracy, and efficiency.
  • They preprocess and clean large datasets, ensuring data quality and compatibility with machine learning algorithms.
  • Machine Learning Engineers collaborate with cross-functional teams to understand business needs and translate them into technical solutions.

How a Math Degree can be Valuable in Machine Learning Engineering?

  • Mathematics underpins the algorithms and statistical techniques used in machine learning, enabling engineers to design and develop robust models.
  • Concepts like linear algebra, calculus, and probability theory are essential for understanding and implementing machine learning algorithms.
  • Strong mathematical skills allow Machine Learning Engineers to analyze and interpret model performance, optimize hyperparameters, and make informed decisions throughout development.

Resources to Build a Career in Machine Learning

6. Operations Research Analyst

With a math degree, individuals acquire a solid grounding in mathematical modeling, statistical analysis, and problem-solving aptitude, making them well-equipped for the dynamic field of operations research. These skills provide a valuable toolkit for Operations Research Analysts to tackle complex problems, optimize operations, and drive informed decision-making within organizations.

what to do after a math degree?
Source: Penn State

Role and Responsibilities

  • Operations Research Analysts apply mathematical modeling, optimization techniques, and statistical analysis to solve complex problems and improve operational efficiency.
  • They collect and analyze data to identify areas for improvement and make data-driven recommendations to enhance decision-making processes.
  • Operations Research Analysts develop mathematical models and algorithms to optimize resource allocation, scheduling, inventory management, and logistics.
  • They collaborate with stakeholders to understand business objectives, formulate research questions, and implement solutions that align with organizational goals.
  • Operations Research Analysts communicate findings through reports, presentations, and visualizations to facilitate understanding and drive actionable insights.

How a Math Degree can be Valuable in Operations Research?

  • Mathematics equips individuals with the skills to formulate and solve complex optimization problems, applying linear programming, integer programming, and other mathematical techniques.
  • Statistical analysis allows Operations Research Analysts to derive meaningful insights from data, identify patterns, and validate models.
  • Mathematical modeling skills enable analysts to develop mathematical representations of real-world problems and apply optimization algorithms to find optimal solutions.

Resources to Build a Career in Operations Research Analysis

7. MBA and Management Careers

Pursuing an MBA (Master of Business Administration) opens doors to diverse career opportunities and equips individuals with various business skills and strategic acumen. With an MBA, professionals can take on leadership roles and contribute significantly in multiple industries.

Role and Responsibilities

  • MBA graduates can take on diverse roles, such as business consultants, project managers, marketing managers, financial analysts, or entrepreneurs.
  • They lead teams and organizations, developing and implementing strategies to drive growth, improve operations, and achieve business objectives.
  • MBA professionals analyze market trends, research, and make data-driven decisions to develop competitive business strategies.
  • They manage budgets, assess financial performance, and develop financial plans to ensure the financial success of organizations.
  • MBA graduates also excel in effective communication, negotiation, and leadership, collaborating with cross-functional teams and stakeholders to achieve organizational goals.

How an MBA Degree can be Valuable in Various Industries?

  • A math degree enhances MBA students’ ability to analyze complex data sets and make data-driven decisions.
  • Mathematics provides a solid foundation for understanding financial concepts, such as modeling, risk assessment, and valuation techniques.
  • Mathematical modeling and optimization skills contribute to streamlining processes and improving efficiency in operations management.
  • Math skills enable MBA students to extract insights from large datasets, uncover trends, and drive strategic decision-making.
  • Problem-Solving and Critical Thinking: Mathematics fosters strong problem-solving and logical reasoning abilities, valuable for addressing complex business challenges.

Other Career Options Math Graduates

CareerJob Description
StatisticianCollect and analyze data to interpret and solve real-world problems, design experiments, and create statistical models.
Market Research AnalystConduct research, gather and analyze data, and provide insights into market trends, consumer behavior, and competitive landscapes.
Business AnalystAnalyze business processes, gather requirements, and propose solutions to improve operational efficiency and support decision-making.
Risk AnalystIdentify and assess potential risks, analyze data to evaluate risk levels, and develop strategies to mitigate and manage risks for organizations.
Quantitative AnalystApply mathematical and statistical models to analyze financial data, develop investment strategies, and make data-driven decisions in finance and investment sectors.
Mathematical ProgrammerDevelop and implement mathematical algorithms and programs, solve complex computational problems, and optimize code for efficient mathematical computations.
CryptographerDesign and develop cryptographic algorithms and protocols, ensure data security and privacy, and analyze and solve complex encryption problems.
EconometricianApply mathematical and statistical methods to analyze economic data, develop economic models, and provide insights into economic trends and relationships.
Mathematical ModelerCreate mathematical models to simulate and solve real-world problems, develop computational algorithms, and optimize systems and processes.
Supply Chain AnalystAnalyze supply chain operations, optimize inventory levels, streamline logistics, and identify opportunities for cost savings and process improvements.
Pricing AnalystAnalyze market trends and competitive landscapes, develop pricing strategies, and optimize pricing models to maximize profitability and revenue.

Conclusion

A math degree opens up various career opportunities across multiple industries. Embrace the possibilities, explore these diverse career options, and leverage your math degree to make a meaningful impact in the world of numbers and beyond.

If you are interested in making a career in the fields of Data, AI and Machine Learning, then our courses can help you fastrack the process. You can explore our courses here.

Frequently Asked Question

Q1. What can I do with a math degree?

A. A math degree offers diverse career opportunities in data analysis, actuarial science, finance, and research. Roles include data analysts, actuaries, financial analysts, statisticians, and operations research analysts.

Q2. What jobs can you get with a mathematics degree?

A. With a math degree, you can pursue careers as a data analyst, actuary, financial analyst, statistician, or data scientist. Math skills are transferable and applicable across industrie

Q3. How can a math degree be valuable in data analysis?

A. A math degree’s quantitative skills are valuable in data analysis, enabling analysis of complex datasets, identifying patterns, and making data-driven decisions using statistical methods.

Q4. Are there any courses available to enhance data analysis skills?

A. You can consider the following courses to make a career in data analysis:

– Introduction to Exploratory Data Analysis (EDA)
– Microsoft Excel: Formulas & Functions
– Tableau for Beginners
– End to End Statistics
– SQL Tutorials for Beginners
– Top Data Science Projects for Analysts and Data Scientists

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