Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

What to Do After BSc?

A
Analytics Vidhya Last Updated : 18 Oct, 2024
6 min read

Introduction

Science is often an endless source of possibilities, offering vast opportunities for further study and employment in various fields. What to do after BSc is a common question that bugs final-year students. There are some traditional career options that are said to be safe, and then there are some trending careers that are not explored by many before. Here is a list of best career options after BSc, opening doors to rewarding and lucrative employment opportunities.

Table of contents

Top 10 Things to Do After BSc 

BSc graduates are in high demand across various industries, offering numerous work prospects. If you’re wondering about the options after BSc and the available courses, this guide will assist you in making informed decisions. Whether you choose to pursue further academic studies or dive into the job market, here are some potential pathways to consider after completing your BSc degree:

1. Data Science

Data science is a rapidly growing field that combines statistical analysis, programming, and domain expertise to extract insights and make data-driven decisions. After completing a BSc degree, pursuing a career in data science is a promising option. Data scientists leverage machine learning and advanced programming skills to develop innovative algorithms and automate repetitive tasks.

Online certifications are the best way to kickstart your career in Data Science. Analytics Vidhya’s Blackbelt Plus program covers basics to advanced Data Science topics. Additionally, you get to solve real-life projects, gain skills from practical assignments and connect with mentors who know everything about the domain.

If you choose this career option after BSc, you can become a data scientist, data analyst, ML expert, product manager, insights manager and more. The salary for data science professionals ranges from ₹3,60,000 to ₹25,80,000 annually, reflecting the high demand and value of their expertise in the industry.

Become a Data Scientist in 2023 | Step-by-Step Roadmap to Master Data Science

2. MSc

An MSc in science offers specialization in diverse subjects such as physics, chemistry, biology, statistics, mathematics, and more. These programs typically span two years, equipping students with advanced knowledge and practical skills necessary for a competitive job market. Graduates can explore various career paths as researchers, scientists, field agents, lecturers, professors, or teachers. Pursuing a Ph.D. in their area of interest is another option. Salaries after completing an MSc range from ₹3,60,000 to ₹21,60,000 per year, reflecting the potential for lucrative remuneration.

3. Internship

Internships provide an excellent launchpad for your career after completing a BSc degree. They offer valuable opportunities to expand your professional network, gain practical experience, and secure a good job later. Many businesses, including innovative startups, offer internships to undergraduate students, enabling them to gain industry-specific skills. Exploring internships with government organizations can expose you to diverse projects and activities. To enhance your chances of securing an internship, tailor your applications to highlight your relevant skills and strengths. In India, salaries for BSc graduates starting as interns typically start from ₹2,00,000 per year.

4. Data Analyst

Choosing a career as a data analyst after completing a BSc offers several advantages. It allows you to leverage your strong analytical skills, use data-driven insights, and contribute to decision-making processes. Data analysts are in high demand across industries, offering diverse job opportunities and attractive salary prospects for those with a knack for working with data.

The certified AI and ML Blackbelt Plus program by Analytics Vidya provides practical assignments and case studies using real datasets, enabling students to practice data analysis and problem-solving.

After completing a data analytics course, various career paths become available, including business analyst, data analyst, data engineer, risk analyst, healthcare data analyst, and more.

The salary for professionals in the data analytics field typically ranges from ₹1,60,000 to ₹12,00,000 annually.

Become a Data Analyst in 2023 | Roadmap to become a Data Visualisation Rockstar

5. MBA 

An MBA degree is valuable for individuals who want to excel in their chosen field and gain a competitive edge. Pursuing an MBA can significantly accelerate your career progression and lead to higher salaries if you aspire to work in management or leadership roles related to your undergraduate field. Job opportunities for MBA graduates after completing a BSc degree include finance manager, supply chain manager, R&D manager, executive manager, and product manager. Salaries after obtaining an MBA can range from ₹3,50,000 to ₹17,00,000 per year. It can be even higher, depending upon the B school you go to.

6. Machine Learning

Choosing a machine learning career after completing a BSc provides numerous opportunities for growth and innovation. Machine learning is a rapidly expanding field with applications in various industries, offering exciting prospects for those passionate about combining data analysis, coding, and problem-solving. It allows you to work on cutting-edge technologies, build intelligent systems, and contribute to advancements in artificial intelligence. With a high demand for machine learning professionals, it offers a rewarding career path with competitive salaries and continuous learning opportunities.

Online ML certification programs offer individuals the opportunity to become knowledgeable and certified ML specialists, benefiting from the current high demand in the job market. Through ML programs, participants learn to develop intelligent systems capable of learning from vast datasets. The certified AI and ML Blackbelt Plus program by Analytics Vidya covers topics such as supervised learning, unsupervised learning, and industry best practices, providing comprehensive training.

Upon completing an ML course, various career paths become available, including machine learning engineer, robotics engineer, algorithm developer, research scientist, AI engineer, data engineer, and more.

Salaries for professionals with ML expertise typically range between ₹5,00,000 and ₹14,20,000 per year, reflecting the value and demand for their skills in today’s market.

Deep Dive into Practical Machine Learning with Career Insights

7. MCA

An MCA (Master of Computer Applications) program encompasses various subjects, including mathematics, computer science fundamentals, advanced computer topics, and software development/design. This three-year degree is excellent for individuals aiming to establish a successful IT career. By pursuing an MCA, you can enhance your technical skills and prepare for higher-level positions in information technology.

An MCA degree also offers opportunities for research projects, allowing you to delve into computer science and contribute to innovative advancements. Graduates with an MCA degree can get jobs in IT companies and government organizations. Job prospects after MCA include roles such as cyber security analyst, database/network administrator, systems analyst, IT consultant, project manager, software developer, and more. Salaries for MCA graduates typically range from ₹2,55,000 to ₹14,00,000 per year.

8. Artificial Intelligence

Pursuing a career in AI after completing a BSc degree can be a strategic decision due to the growing demand and lucrative opportunities in this field. AI is experiencing widespread adoption across industries, driving the need for skilled professionals who can harness its potential. By entering the field of AI, you can become part of a rapidly evolving industry at the forefront of technological advancements.

To make a successful career in AI after BSc, consider enhancing your skills and knowledge in this domain. Engage in specialized AI courses, certifications, or higher education programs focusing on machine learning, data science, and AI technologies. These educational opportunities will give you a strong foundation and practical exposure to AI tools and techniques.

To stand out in the competitive job market, seek hands-on experience in AI by participating in internships, AI contests, or contributing to open-source AI projects. Building a portfolio of AI projects will demonstrate your practical expertise and showcase your problem-solving abilities.
The career prospects in AI after BSc are diverse, ranging from AI research scientists and machine learning engineers to NLP engineers and computer vision engineers. These roles offer attractive salaries and ample growth opportunities.

Average salary in this field ranges from ₹4,50,000 – ₹17,11,800. It can be higher as well, depending upon your skills and work.

Also Read: Building Strong Careers in AI in 2023

9. PGDM

PGDM is another management degree, just like MBA. The course is usually one to two years long and comes with several specializations like HR, Finance, Marketing, Sales, etc. You can pick an area of interest and specialize in the same. Employment opportunities exist in the government and private sectors, with salaries ranging from ₹5,00,000 to ₹10,00,000. Pursuing a PGDM program expands career prospects, deepens business understanding, and enhances earning potential in a competitive job market.

10. Get a Full Time Job

After completing a BSc, you have diverse professional opportunities based on your area of study and interests. You can explore career options aligned with your academic subject, expertise, and personal preferences. Some potential roles for BSc graduates include research assistant, laboratory technician, environment consultant, data analyst, quality assurance analyst, and pharmaceutical sales representative. These roles offer a salary range of ₹2,00,000 to ₹5,00,000 for freshers, providing a solid start to your professional journey after BSc. Consider leveraging your knowledge and skills to pursue a rewarding career in your chosen field.

Conclusion 

A BSc degree opens you to many professional options, but you must choose a field that best suits your interests and career objectives. Following a B.Sc degree, there are numerous work prospects! To choose a specialist course, you must figure out your areas of expertise. It is also important to keep yourself updated with whats happening in the field you choose. So, keep following relevant blogs, courses and trends to keep yourself at toes.

Analytics Vidhya offers a range of free and paid courses for Data enthusiasts. You can explore these courses here.

Frequently Asked Questions 

Q1. Which course is best after BSc?

A. Analytics Vidhya’s BlackBelt Program is an excellent choice for BSc graduates and final year students. The course includes everything to become a Data Scientist, Data Analyst, and AI/ML Expert. With guided projects and mentorship, it provides the best solutions to real-world problems that can later help you at your job.

Q2. What to do after getting a BSc degree?

A. You can go for higher education or pursue online courses in trending fields to get employment easily.

A
Analytics Vidhya

Analytics Vidhya Content team

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details