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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 Become a Data Scientist After the 12th Standard?

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 13 Oct, 2023
8 min read

Introduction

Data science is a booming industry in the global IT and business sector, with a lot of youngsters wanting to pursue a career in it. Although the term ‘Data Science’ was coined in the 1970s, it became a buzzword only in 2008 and has since captivated the minds of young professionals. Over the years, data science has carved a special niche for itself, with the Harvard Business Review naming the role of data scientist the “sexiest job of the 21st century”. Currently, the demand for data scientists in India is expected to exceed 1 million by 2026. Wondering how to become a data scientist after the 12th grade? Let’s discover the procedure and other related information.

Table of Contents

Why Become a Data Scientist After 12th?

Data Scientists are a dynamic and lucrative career option with the ability to impact companies and society’s future. After graduating from high school, pursuing a career in data science gives the possibility to have a significant effect in addition to several other compelling rewards.

How to become a Data Scientist after the 12th standard?
Source: iimtindia

Impactful Contributions

The influence of data scientists within organizations and on a global scale has great potential. They can work on projects that address significant global issues like climate modeling, healthcare, or environmental sustainability while automating tedious processes and economizing time and resources.

Rising Demand

According to the US BLS, data science occupations are expected to grow by roughly 27.9% between 2016 and 2026. Expert data scientists are constantly in demand owing to a reliance on data-driven decision-making by market leaders such as Amazon, Google, and Apple.

Lucrative Salaries

Glassdoor reveals how Data scientists are paid fairly enough. The average income for a data scientist in India as of September 2023 is ₹14,00,000 annually, which is more than that of data analysts and software engineers in related fields.

Data scientist salary
Source: Glassdoor

A chance to work remotely is an added perk, which increases appeal. In India, the average cash bonus or compensation for a Data Scientist ranges from ₹1,00,000 to ₹3,00,000, with an average of ₹2,00,000.

Evolving Field

Data science powers technology innovation today. More valuable than even oil, data is currently regarded as the world’s most valuable resource. Opportunities to learn and use new abilities, such as AI, machine learning, and big data analytics, are provided by the ongoing evolution of data science.

Professional Advancement

Education in data science provides access to a variety of job options. In addition to becoming a master data scientist, you can progress into leadership positions or pursue a variety of professional options within the sector.

Is Data Science a Suitable Career for You?

If you want to work in data science after you finish your 12th, data science may be an appropriate career choice for you. It provides a dynamic and fruitful career path that integrates corporate planning, data analysis, and programming. You need a commitment to learn the skills and stay current in data science, an ideal career for those with a passion for data and problem-solving.

Eligibility to Become a Data Scientist

The question ‘How to become a Data Scientist after the 12th?’ demands a proper knowledge of the courses one needs to take in order to achieve the position of a Data Scientist. Seeking a career in data science after 12th involves passing entrance exams, completing certain UG programs, and meeting eligibility criteria.

Most Data Scientist roles require you to have a strong educational background in data science or a related field such as IT, Computer Science, Mathematics, or Statistics. This could be an acknowledged online certificate, a bachelor’s degree, a master’s degree, or even a Ph.D.

Courses to Opt for After 12th Grade to Become a Data Scientist

To become a Data Scientist after the 12th standard, you can either enroll for a Bachelor of Science (B.Sc.) or a Bachelor of Technology (B.Tech) degree, details of which you will find below. On the side, you can also enroll for online certificate courses that can boost your knowledge in specific topics, and add value to your resume.

B.Sc./B.Tech in Data Science

  • Admission Eligibility: Minimum 50% in 10+2 Science Stream (Physics, Chemistry, Maths)
  • Selection Process: Based on a basic test after the 1st month of the course
  • Course Time: B.Sc. – 3 years, full-time | B.Tech – 4 years, full-time

Prominent Colleges Offering Data Science Courses

  • IIT Madras: Offers BSc in Programming and Data Science, as well as various online data science courses
  • BITS Pilani: Offers M.Tech in Data Science
  • VIT Vellore: Offers data science courses
  • IISC Bangalore: Renowned for research in data science, including Ph.D. programs
  • Other Research Institutes: Include ISER, Tata Institute Of Fundamental Science, and Harish Chand Institute – Allahabad

Average Data Science Course Fees

Most online courses in Data Science are priced from Rs. 10,000 to Rs. 65,000. Meanwhile, a bachelor’s degree in the field can cost anywhere between Rs 2 Lakhs to Rs 8 Lakhs, depending on the institution and program.

Online Courses After 12th to Become a Data Scientist

1. Certified AI ML Blackbelt+ Program

A thorough learning experience that equips people to grasp the fields of data science, machine learning, and deep learning is the Certified AI & ML BlackBelt+ Programme. This program, which costs INR 64,999 ($999), includes a variety of classes that experienced educators have carefully planned with in-depth knowledge of data science. Participants have access to more than 14 courses, which helps them advance their careers by becoming proficient in a variety of machine learning tools and methodologies.

Certified AI ML Blackbelt+ Program

The program offers 25+ real-world projects and 18 individual 1:1 mentorship meetings for students interested in AI. It provides hands-on experience and helps them address business difficulties. This program is an ideal starting point for those aspiring to work in AI, making it an engaging and fruitful educational experience.

2. Machine Learning Certification Course for Beginners

The Machine Learning Certification Course for Beginners is a one-stop solution for beginners like 12th-standard graduates to learn machine learning. This free course offers in-depth knowledge of Python and the basics of machine learning. It explains the working and evaluation of machine learning models, in very basic terms for easy understanding.

Machine Learning Certification Course for Beginners

The course teaches how to build machine learning models and improve the overall performance of those models using feature engineering techniques. It also mentions the real-life applications of machine learning in various industries.

3. Top Data Science Projects for Analysts and Data Scientists

The course “Top Data Science Projects for Analysts and Data Scientists” is a thorough compilation of open-source data science projects that covers a variety of topics like ML, Computer Vision, NLP, and Data Engineering. It serves professionals of various experience levels, from amateurs to experts in data science. One can enroll for free.

Top Data Science Projects for Analysts and Data Scientists

This course, designed for individuals looking to enhance their data science skills and bolster their résumé, is for current analysts and aspiring data scientists, alike. A laptop/desktop with 4 GB RAM, an internet connection, and some acquaintance with Python/R is enough to begin with the course.

4. Getting Started with Git and GitHub for Data Science Professionals

Data scientists should take the “Getting Started with Git and GitHub for Data Science Professionals” course. The course teaches the foundations of version control using Git and GitHub, which are essential for efficient project tracking and collaboration. This introductory course walks you through the ins and outs of Git and GitHub without requiring any prior knowledge.

Getting Started with Git and GitHub for Data Science Professionals

Your data science abilities will improve, and you’ll add a strong skill to your CV if you can grasp version control. A person who is intrigued about learning Git and GitHub in data science programming must enroll in the course. The basic requirements to get started include a laptop/desktop with 4 GB RAM, an internet connection, and a GitHub account.

5. Pandas for Data Analysis in Python

The Pandas for Data Analysis in Python course teaches you exactly what it says – how to use Pandas in Python for data analysis. Learn how to work with Pandas in this free course and master the most popular Python library in data science. Excel at data analysis and manipulation through this course, and step closer to being a data scientist.

Pandas for Data Analysis in Python

This comprehensive course teaches you all about Pandas in Python right from installation and importing data, to performing data analysis and applying functions. The course also teaches you to aggregate, write, manipulate, and convert data to various formats. Moreover, it familiarizes you with pivot tables and shows you how to deal with missing data values.

Skills Required to Become a Data Scientist After the 12th Grade

Various educational and training programs available focus on the key skills that are required to become a data scientist. After completing their 12th-grade education, individuals can acquire these skills and become proficient data scientists.

Programming Skills

Data science is built on a foundation of programming knowledge. They curate codes to clean, preprocess, analyze, and create ML models from data. It is of the utmost importance to be proficient in programming languages (Python and R). Due to their extensive libraries and adaptability, these languages are often used in the data science industry.

Interpersonal Skills

Data scientists are required to work with cross-functional teams and non-technical stakeholders. The collaboration for sharing data-driven insights and other details requires strong interpersonal and communication skills.

Data Visualization

Data visualization is a great method for presenting complex information in an aesthetically pleasing and accessible way. It facilitates the effective communication of research and trends. For the purpose of producing instructive charts and graphs, knowledge of data visualization tools such as Tableau, Seaborn, Power BI, etc., is advantageous.

Business Strategy

It is essential for data scientists to know the business environment. They must coordinate data analysis with organizational goals and plans to produce insights that inform business decisions. Key performance indicators (KPIs) and domain knowledge, as well as familiarity with business principles, are crucial.

Algorithm Modeling

Data science’s core methodology is the construction of machine learning models. To address particular problems and make predictions, data scientists must choose, create, and fine-tune algorithms. It is required to be proficient in model evaluation, optimization techniques, and feature engineering.

Salary of a Fresher Data Scientist

Data Science has risen to the top of the list of professions that Indian employers are looking for in 2023. The starting compensation for data scientists is rather high in India. As of July 2023, the average base salary of a fresher data scientist is ₹589,484/ year, based on 881 salary profiles. The base salary varies from city to city and according to experience levels.

Conclusion

Now, you are pretty well informed about how to become a data scientist after the 12th grade. The field of data science is gaining exponential growth globally. So, it is a highly recommended profession for a person just out of school with an inclination towards specific subjects.

Frequently Asked Questions

Q1. Which college is best for data science after the 12th?

Ans. IIT Madras is the best college in India for studying data science after 12th. It offers a BSc in Programming and Data Science, as well as various online data science courses. VIT Vellore is another good option.

Q2. What is the salary of a data scientist after the 12th?

Ans. As of July 2023, the average base salary of a fresher data scientist is ₹589,484/ year, based on 881 salary profiles. The base salary varies from city to city and according to experience levels.

Q3. Can 12th pass become a data analyst?

Ans. Yes, you can become a data analyst after the 12th by pursuing a bachelor’s and master’s degree in data science or a related field or by doing online courses.

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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