Can a Student Pursue Data Science after B.Com

Can a Student Pursue Data Science after B.Com?

Your abilities are what land you job opportunities and mould your career, not your degree. If you have a bachelor’s degree in business and accounting but lack business understanding, you will not be able to succeed in the workforce. There is something for everyone to learn. The only prerequisite is a desire to study.

Now let’s talk about Data science. It is a multidisciplinary subject that focuses on turning raw data into forecasts, suggestions, and actionable insights that can inform scientific study, business strategy, policy-making, and other areas.Every day, from the moment we woke up until we set our alarm, we generated almost 2.5 quintillion bytes of data.The latest innovations of today rely entirely on data.For this reason, the most difficult job of the past ten years is in data science.
A BCOM student with experience in data management, statistical and market analysis, etc.He or she is adept at playing with numbers.These are the essential competencies required in data science.This allows the graduates of commerce to take advantage of their varied skill sets in this field. They just need to sharpen their abilities and have a solid foundation in data science to keep pushing their graph forward.

Topics to Learn

Data science is a multidisciplinary field that requires knowledge in various areas. Here are some key topics you should consider learning to become proficient in data science:

Programming Languages:

Python: Widely used for data manipulation, analysis, and building machine learning models.

R: Especially useful for statistical analysis and visualization.

Mathematics and Statistics:

Linear Algebra: A lot of data science methods rely on matrices, vectors, eigenvalues, and eigenvectors.
Probability: Recognise ideas such as conditional probability, anticipated values, and distributions.
Statistics: Regression, confidence intervals, hypothesis testing, and other topics.

Data Manipulation and Analysis:

Numpy is an excellent tool for computing in mathematics.

Pandas is a Python data analysis and manipulation toolkit.

SQL: Essential for managing and querying databases, especially when working with big datasets.

Matplotlib and Seaborn are two flexible Python libraries that may be used to create static, interactive, and animated visualisations.

Feature Engineering and Domain Knowledge:

If you have a history in commerce, you should have a solid understanding of concepts like risk management, trading factors, economics, etc., as feature engineering involves adding, removing, and modifying existing features (columns) in order to enhance model performance.We are unable to accomplish this without domain knowledge.

Machine Learning:

Supervised Learning: Regression and classification algorithms like linear regression, decision trees, random forests, and support vector machines.

Unsupervised Learning: Clustering algorithms like K-means, hierarchical clustering, and dimensionality reduction techniques like PCA.

Deep Learning: Neural networks and frameworks like TensorFlow and PyTorch.

Ensemble Methods: Techniques that combine multiple models for improved performance, such as bagging and boosting.

Data science transforms management and commerce. It’s crucial to get this talent because of this. Students can access a data science platform with technical evaluations, expert insights, and partnerships through Theta Academy. Accept data science to open up a world of possibilities in the future.