It is your skill, not your academic background, that secures employment offers and shapes your career. You cannot have a successful job in commerce if you have a bcom degree but have no knowledge of business or accounting. Everyone can learn something. The desire to learn is the sole requirement.
Now come to Data Science,it is a multidisciplinary field that involves transform raw data into actionable insights, predictions, and recommendations that can guide business strategies, scientific research, policy-making, and more.From the journey of waking up in morning to setting alarm at night we produced about 2.5 quintillion bytes of data daily.Today's new Innovations are totally dependable on Data.That's why Data Science has become the most demanding job of this decade.
A student of BCOM, have background in statistical and market analysis,data handling etc.He/She can easily play with numbers.These are the vital skills that are needed in data science.This enables the commerce graduates to benefit from their diverse skill sets in this industry. To continue advancing their graph, they only need to improve their skills and lay a strong foundation in data science.
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:
Python: Widely used for data manipulation, analysis, and building machine learning models.
R: Especially useful for statistical analysis and visualization.
Linear Algebra: Matrices, vectors, eigenvalues, and eigenvectors are fundamental for many data science techniques.
Probability: Understand concepts like distributions, expected values, and conditional probability.
Statistics: Hypothesis testing, confidence intervals, regression, and more.
Numpy: A great tool for mathematics computation.
Pandas: A Python library for data manipulation and analysis.
SQL: For querying and managing databases, crucial for handling large datasets.
Matplotlib & Seaborn: A versatile libraries for creating static, interactive, and animated visualizations in Python.
If you are from commerce background,you should be strong in the concept like Risk Management,Trading factors ,Economics etc. because Feature Engineering is the process of creating,dropping and modifying current features(columns) so that to improve model performance.This cannot be done if we don't have domain knowedge.
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 revolutionizes commerce and management. That's why it's an important skill to learn. Theta Academy offers students a data science platform with expert insights, technical reviews, and partnerships. Embrace data science and unlock a future full of opportunities.
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