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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 BCom?

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 04 Oct, 2023
6 min read

Introduction

Why does a professional choose to be a data scientist after BCom? Is it for the sake of experimenting? Or Is there any bigger reason behind the switch? Well. That reminds us of the fact that data sciences have recently earned a great reputation in the professional arena in terms of the rapid vocational growth of society as a whole. Also, from 2022 to 2032, data scientist jobs are predicted to experience 35% growth, with 17,700 openings every year!

Let’s discover the reasons and facts behind the switch of BCom professionals to data science for the survival of their businesses.

Table of Contents

Why are BCom Students Shifting to Data Science?

The field of Data Science is expanding industries really fast! It serves numerous applications in a broad breadth of businesses. It proposes Data Analysis, which is one of the highest-paying jobs currently and is expected to grow exponentially in the coming times.

Data analysis

Let’s have a quick yet descriptive glance at the reasons for the career shift, as in becoming a data scientist after BCom.

Proficiency in Microsoft Excel

For students seeking both a B.Com and a data science degree, proficiency in Microsoft Excel is a useful tool. Excel proficiency is required for budgeting, data analysis, and financial modeling in the B.Com discipline, as these competencies are vital for jobs like financial analysts and accountants. Interestingly, data science requires these skills just as much.

Excel is frequently used for data preparation, cleaning, and initial data exploration, giving prospective data scientists a solid base. Learning advanced Excel techniques might serve as a springboard for using programming languages, which are more difficult data manipulation and analysis languages used in data science.

Symbiotic Relationship between Commerce and Data

Data science and business work together in integration. Companies in the commerce sector heavily rely on data to make decisions in today’s data-driven business world. This requires data abilities, including data collection, transformation, and storage. B.Com courses already cover topics such as business analytics, market trends, and financial data, making the transfer to data science easier. These skills can be improved upon and altered to execute more complicated data analysis, predictive modeling, and data-driven decision-making.

Dual Career Paths with a Single Bachelor’s Degree

One bachelor’s degree can considerably increase work options if two career paths are pursued. A graduate who becomes a data scientist after BCom has the ability to access a wide range of employment prospects in both industries. They can pursue careers in data science as data analysts, machine learning engineers, or data engineers while also working as financial analysts, business intelligence analysts, or data analysts in the commercial sector. More employment alternatives are made possible by this versatility, along with the freedom to change careers when interests change.

Enhancing the Resume

A data science internship can do wonders for a person’s resume. A graduate who wants to be a data scientist after BCom has a treasure of skills in data analysis, statistical modeling, and data visualization that might be a useful asset even if they decide to leave data science. Candidates with strong data-driven decision-making and analytical skills are highly esteemed by employers across a huge spectrum of industries. Regardless of the selected professional route, having a data science experience on the resume can make career searches simpler and more fruitful.

Financially Lucrative and Secure Career

Compared to B.Com grads, data scientists frequently make a good sum. Numerous businesses are experiencing an increase in demand for data science workers, which results in competitive pay scales. Data scientists are vital assets to organizations because of their capacity to derive important insights from data and create data-driven strategies. As a result, data science is a stable and financially rewarding career alternative. So, it becomes a wise decision for an individual to become a data scientist after BCom, seeking to maximize their earning potential and job stability.

How to Become a Data Scientist?

A data scientist is a specialist with statistical, mathematical, and programming knowledge who analyzes complicated data sets to derive insights, support decision-making, and create models to predict outcomes. Let’s have access to the main points to consider if we aspire to enter the field of data science as a professional.

Understand Data Science

It is critical to comprehend what data science encompasses before going into its technical parts. Making well-thought rulings necessitates data science. It involves obtaining insights and information from data. To understand the fundamental ideas, procedures, and practical applications of the topic, perform research and read relevant literature.

Data science and data analytics

Learn Relevant Math and Statistics

In data science, excellent mathematics and statistical background are essential. Essential subjects include calculus, probability, inferential statistics, and linear algebra. You may efficiently deal with data, do analysis, and create prediction models by being aware of these principles.

Maths and statistics for data science career

Develop Programming Skills

Data science relies on programming. You should learn how to use popular programming languages in the industry. You learn to manipulate data, design algorithms, and generate machine learning models by learning to write code.

Programming skills for data science job

Enroll in Data Science Courses

Try enrolling in traditional or online data science courses. Numerous academic institutions and online learning environments provide specialized data science programs and courses. These courses address a range of topics in data science, including machine learning methods and data preprocessing.

We highly recommend enrolling in Analytics Vidhya’s Data Science Bootcamp, a well-rounded program covering everything you need to know to become a data scientist!

Learn More: Step-by-Step Guide to Become a Data Scientist in 2023

Build a Strong Foundation

Boost the knowledge of data analysis and manipulation. Learn how to use data-wrangling tools or libraries like Pandas. You will be able to effectively clean and preprocess data because of this foundation. It is a crucial step in any data science effort.

Work on Projects

Data science gains value from practical experience. Work on open-source or personal projects first. You can use your experience in these projects, study real-world datasets, and demonstrate your abilities to potential recruiters. Having a recorded collection of completed projects can greatly increase your credibility.

Pursue Advanced Education (Optional)

A master’s or doctoral degree in data science, machine learning, or a similar topic is not required. However, it can lead to more distinguished employment possibilities. Earning advanced degrees can also strengthen your comprehension of intricate data science concepts.

Internships and Entry-Level Positions

An internship or an entry-level position is one of the quickest options for growth in the data science field. It helps to gain practical experience. There are seasoned data scientists who help you explore the field and sharpen your skills in a professional setting.

Apply for Data Science Jobs

Start looking for employment openings in data science that fit your interests and skill set. Highlight applicable education, projects, and skill sets in your cover letter, resume, and curriculum vitae. Be ready for interviews that could involve technical evaluations and inquiries about your capacity for problem-solving.

Also Read: How To Get A Data Science Job? [Decoded In 8 Simple Steps]

Continuous Learning and Networking

We know that the field of data science is constantly growing. So, it has become the need of the hour to participate in online communities, attend conferences, and network with industry experts. This will assist in staying updated on the most contemporary tools, technologies, and trends. To succeed in data science, you must constantly learn new things.

Conclusion

The demand for data skills is rising, there is a synergy between commerce and data, there are multiple career routes, there are benefits for your resume, and there are good financial prospects driving the shift from B.Com to data science. For B.Com students looking to broaden their employment options, data science is an attractive option.

To succeed as a data scientist, keep developing your abilities, working on projects, and staying up to date on industry developments. In order to do so, most data science enthusiasts enroll in comprehensive programs like Analytics Vidhya’s Data Science Bootcamp, which covers everything you need to know to become a data scientist!

Frequently Asked Questions

Q1. Can a BCom graduate become a data scientist?

Ans. A BCom graduate can become a data scientist by building a strong foundation in mathematics, statistics, and programming.

Q2. Is data science good for B Com students?

Ans. Data science is a good career option for B Com students as it offers a secure career with a high salary by upskilling from a commerce background.

Q3. Can a commerce student become a data scientist?

Ans. Yes, a commerce student can become a data scientist as they have a strong foundation in business analysis, management, and basic mathematics.

bcombusinesscareercareer shiftcommercecommerce to data sciencedata analysisdata sciencedata scientistExcelGuidemathematics for data scienceprogrammingprogramming skills
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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Is Data Science a Good Career?
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