Image credit: Analytics Drift
Produced By: Jalaj Jain
Begin with a strong educational foundation in mathematics and statistics (descriptive and inferential statistics).
Learn to code in languages like Python or R, which are essential for data manipulation and analysis. Familiarize yourself with popular data science libraries like numpy and pandas.
Become proficient in data collection techniques, including web scraping, data extraction, and API integration.
Clean, preprocess, and wrangle data to ensure its quality. This step is often the most time-consuming but essential for accurate analysis.
Conduct EDA to gain insights from data visualization. Explore relationships, patterns, and outliers that will guide your analysis.
Dive into the world of machine learning. Learn various algorithms and types of machine learning, such as supervised, unsupervised, and semi-supervised.
Train and evaluate machine learning models, understanding model performance metrics is key to refining your models.
Become adept at feature engineering to enhance the predictive power of your models. Feature selection and creation are critical for model accuracy.
Learn MLOps to deploy models in real-world applications. Then, interpret the results and communicate your findings effectively to stakeholders.
Data science is a dynamic field. Stay updated with the latest trends, attend conferences, and network with fellow data scientists to keep learning and growing.
Get the latest updates on AI developments.
Designed by: Prathamesh